If you think that there is an error in how your package is being tested or represented, please file an issue at NewPkgEval.jl, making sure to read the FAQ first.
Results with Julia v1.2.0
Testing was successful.
Last evaluation was ago and took 7 minutes, 14 seconds.
Resolving package versions...
Installed Missings ──────────────────── v0.4.3
Installed DataAPI ───────────────────── v1.1.0
Installed PDMats ────────────────────── v0.9.10
Installed TableTraits ───────────────── v1.0.0
Installed BinaryProvider ────────────── v0.5.8
Installed CSV ───────────────────────── v0.5.18
Installed DataFrames ────────────────── v0.19.4
Installed JWAS ──────────────────────── v0.6.2
Installed ArrayLayouts ──────────────── v0.1.5
Installed URIParser ─────────────────── v0.4.0
Installed PooledArrays ──────────────── v0.5.2
Installed StatsBase ─────────────────── v0.32.0
Installed InvertedIndices ───────────── v1.0.0
Installed StatsFuns ─────────────────── v0.9.0
Installed DataValueInterfaces ───────── v1.0.0
Installed Reexport ──────────────────── v0.2.0
Installed Rmath ─────────────────────── v0.5.1
Installed Compat ────────────────────── v2.2.0
Installed OrderedCollections ────────── v1.1.0
Installed Tables ────────────────────── v0.2.11
Installed FillArrays ────────────────── v0.8.2
Installed WeakRefStrings ────────────── v0.6.1
Installed Parsers ───────────────────── v0.3.10
Installed DataStructures ────────────── v0.17.6
Installed FilePathsBase ─────────────── v0.7.0
Installed Distributions ─────────────── v0.21.9
Installed JSON ──────────────────────── v0.21.0
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed SortingAlgorithms ─────────── v0.3.1
Installed QuadGK ────────────────────── v2.1.1
Installed MacroTools ────────────────── v0.5.2
Installed SpecialFunctions ──────────── v0.8.0
Installed CategoricalArrays ─────────── v0.7.3
Installed StaticArrays ──────────────── v0.12.1
Installed LazyArrays ────────────────── v0.14.10
Installed Arpack ────────────────────── v0.3.1
Installed BinDeps ───────────────────── v0.8.10
Installed ProgressMeter ─────────────── v1.2.0
Updating `~/.julia/environments/v1.2/Project.toml`
[c9a035f4] + JWAS v0.6.2
Updating `~/.julia/environments/v1.2/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[4c555306] + ArrayLayouts v0.1.5
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[336ed68f] + CSV v0.5.18
[324d7699] + CategoricalArrays v0.7.3
[34da2185] + Compat v2.2.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[31c24e10] + Distributions v0.21.9
[48062228] + FilePathsBase v0.7.0
[1a297f60] + FillArrays v0.8.2
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[c9a035f4] + JWAS v0.6.2
[5078a376] + LazyArrays v0.14.10
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.1.1
[189a3867] + Reexport v0.2.0
[79098fc4] + Rmath v0.5.1
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[ea10d353] + WeakRefStrings v0.6.1
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Testing JWAS
Status `/tmp/jl_XVrhf9/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[4c555306] ArrayLayouts v0.1.5
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[336ed68f] CSV v0.5.18
[324d7699] CategoricalArrays v0.7.3
[34da2185] Compat v2.2.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[31c24e10] Distributions v0.21.9
[48062228] FilePathsBase v0.7.0
[1a297f60] FillArrays v0.8.2
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[c9a035f4] JWAS v0.6.2
[5078a376] LazyArrays v0.14.10
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[92933f4c] ProgressMeter v1.2.0
[1fd47b50] QuadGK v2.1.1
[189a3867] Reexport v0.2.0
[79098fc4] Rmath v0.5.1
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[30578b45] URIParser v0.4.0
[ea10d353] WeakRefStrings v0.6.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
The delimiter in pedigree.txt is ','.
coding pedigree... 8%|██▋ | ETA: 0:00:01[K
coding pedigree... 100%|████████████████████████████████| Time: 0:00:00[K
calculating inbreeding... 8%|██▏ | ETA: 0:00:02[K
calculating inbreeding... 100%|█████████████████████████| Time: 0:00:00[K
Finished!
Test single-trait BayesC analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesC
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:01:59[K
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:33[K
Posterior means at iteration: 50
Residual variance: 2.089545
Polygenic effects covariance matrix
[0.708 0.171; 0.171 1.661]
Marker effects variance: 0.547218
π: 0.41
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:04[K
Posterior means at iteration: 100
Residual variance: 1.843023
Polygenic effects covariance matrix
[2.245 1.627; 1.627 3.015]
Marker effects variance: 0.432835
π: 0.47
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:04[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.4 │
│ 2 │ m2 │ 0.2 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.5 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
Test multi-trait BayesC analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesC
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:04:50[K
running MCMC for BayesC... 2%|▌ | ETA: 0:02:33[K
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:36[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.511597 0.724487 0.726751; 0.724487 0.994278 0.602416; 0.726751 0.602416 0.89288]
Polygenic effects covariance matrix
[0.586253 0.178594 0.156443 0.080118; 0.178594 0.639766 0.191299 0.253934; 0.156443 0.191299 0.550044 0.219931; 0.080118 0.253934 0.219931 0.472004]
Marker effects covariance matrix:
[0.578527 0.072306 0.23891; 0.072306 0.730512 0.287563; 0.23891 0.287563 0.500752]
π:
Dict([1.0, 1.0, 0.0] => 0.14677963794863028,[0.0, 0.0, 0.0] => 0.10008152549288205,[1.0, 0.0, 0.0] => 0.15170949470012896,[0.0, 1.0, 1.0] => 0.0970841381260155,[1.0, 0.0, 1.0] => 0.11101142583937376,[0.0, 0.0, 1.0] => 0.09179311640171843,[1.0, 1.0, 1.0] => 0.15821592819696348,[0.0, 1.0, 0.0] => 0.1433247332942876)
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:04[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.284647 0.595646 0.608917; 0.595646 0.860753 0.501498; 0.608917 0.501498 0.820684]
Polygenic effects covariance matrix
[0.53723 0.161462 0.167774 0.158116; 0.161462 0.700232 0.22131 0.329786; 0.167774 0.22131 0.665673 0.267817; 0.158116 0.329786 0.267817 0.545734]
Marker effects covariance matrix:
[0.572237 0.181676 0.251517; 0.181676 0.586536 0.260452; 0.251517 0.260452 0.449416]
π:
Dict([1.0, 1.0, 0.0] => 0.13793932714885343,[0.0, 0.0, 0.0] => 0.10774786495186296,[1.0, 0.0, 0.0] => 0.15503078022959724,[0.0, 1.0, 1.0] => 0.08806264630721632,[1.0, 0.0, 1.0] => 0.11996335370649042,[0.0, 0.0, 1.0] => 0.11942536525905809,[1.0, 1.0, 1.0] => 0.14664838082791587,[0.0, 1.0, 0.0] => 0.12518228156900565)
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:04[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.8 │
│ 2 │ m2 │ 0.3 │
│ 3 │ m3 │ 0.8 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait BayesB analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesB
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesB... 1%|▎ | ETA: 0:00:37[K
running MCMC for BayesB... 10%|██▌ | ETA: 0:00:05[K
Posterior means at iteration: 50
Residual variance: 2.407378
Polygenic effects covariance matrix
[1.644 0.251; 0.251 0.88]
π: 0.606
Posterior means at iteration: 100
Residual variance: 1.569562
Polygenic effects covariance matrix
[6.712 4.994; 4.994 4.874]
π: 0.601
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.5 │
│ 2 │ m2 │ 0.4 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesB analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesB
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesB... 1%|▎ | ETA: 0:01:40[K
running MCMC for BayesB... 10%|██▌ | ETA: 0:00:13[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.569077 0.198376 0.667713; 0.198376 1.83411 0.564869; 0.667713 0.564869 1.445847]
Polygenic effects covariance matrix
[1.416899 0.327117 0.678036 0.098028; 0.327117 0.661926 0.228613 0.309764; 0.678036 0.228613 0.713282 0.11413; 0.098028 0.309764 0.11413 0.680155]
π:
Dict([1.0, 1.0, 0.0] => 0.1950864469754054,[0.0, 0.0, 0.0] => 0.13766447072882068,[1.0, 0.0, 0.0] => 0.14132207455467682,[0.0, 1.0, 1.0] => 0.10477544556877501,[1.0, 0.0, 1.0] => 0.08845309693372158,[0.0, 0.0, 1.0] => 0.09230955927572831,[1.0, 1.0, 1.0] => 0.12669419131233717,[0.0, 1.0, 0.0] => 0.1136947146505351)
running MCMC for BayesB... 67%|████████████████▊ | ETA: 0:00:01[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.029044 -0.106009 0.665409; -0.106009 1.652695 0.493566; 0.665409 0.493566 1.224332]
Polygenic effects covariance matrix
[1.037958 0.207909 0.367737 0.192657; 0.207909 0.633469 0.221825 0.191849; 0.367737 0.221825 0.593255 0.131286; 0.192657 0.191849 0.131286 0.623739]
π:
Dict([1.0, 1.0, 0.0] => 0.1517018807149787,[0.0, 0.0, 0.0] => 0.12033774976827588,[1.0, 0.0, 0.0] => 0.11894000409968558,[0.0, 1.0, 1.0] => 0.09571707086393884,[1.0, 0.0, 1.0] => 0.14353373243650683,[0.0, 0.0, 1.0] => 0.12117426577345272,[1.0, 1.0, 1.0] => 0.13029865844777402,[0.0, 1.0, 0.0] => 0.11829663789538758)
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:01[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.8 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.8 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait RR-BLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods RR-BLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for RR-BLUP... 1%|▎ | ETA: 0:00:11[K
Posterior means at iteration: 50
Residual variance: 0.623985
Polygenic effects covariance matrix
[1.138 0.539; 0.539 2.739]
Marker effects variance: 0.633985
Posterior means at iteration: 100
Residual variance: 1.410191
Polygenic effects covariance matrix
[0.918 0.521; 0.521 2.036]
Marker effects variance: 0.478467
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait RR-BLUP analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods RR-BLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for RR-BLUP... 1%|▎ | ETA: 0:00:23[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.889725 0.359647 0.458737; 0.359647 1.07213 0.32933; 0.458737 0.32933 0.712036]
Polygenic effects covariance matrix
[0.697628 0.343464 0.30571 0.346486; 0.343464 0.723595 0.270126 0.273999; 0.30571 0.270126 0.533881 0.325397; 0.346486 0.273999 0.325397 0.673711]
Marker effects covariance matrix:
[1.518624 -0.367241 0.229344; -0.367241 0.778159 0.260332; 0.229344 0.260332 0.419257]
running MCMC for RR-BLUP... 77%|██████████████████▌ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.81899 0.310111 0.37757; 0.310111 1.045768 0.449769; 0.37757 0.449769 0.848511]
Polygenic effects covariance matrix
[0.730438 0.33467 0.304032 0.300146; 0.33467 0.666541 0.239056 0.230684; 0.304032 0.239056 0.591364 0.285046; 0.300146 0.230684 0.285046 0.581339]
Marker effects covariance matrix:
[1.333881 -0.191596 0.23858; -0.191596 0.82945 0.325228; 0.23858 0.325228 0.426886]
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait GBLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods GBLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
running MCMC for GBLUP... 1%|▎ | ETA: 0:01:10[K
Posterior means at iteration: 50
Residual variance: 1.126209
Polygenic effects covariance matrix
[2.702 1.227; 1.227 1.298]
Genetic variance (G matrix): 1.693829
Genetic variance (GenSel): 2.548151
Heritability: 0.570808
Posterior means at iteration: 100
Residual variance: 0.990462
Polygenic effects covariance matrix
[4.773 1.48; 1.48 1.15]
Genetic variance (G matrix): 2.178183
Genetic variance (GenSel): 3.224517
Heritability: 0.616008
running MCMC for GBLUP...100%|██████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait GBLUP analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test single-trait BayesL analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesL
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesL... 1%|▎ | ETA: 0:01:13[K
Posterior means at iteration: 50
Residual variance: 3.347955
Polygenic effects covariance matrix
[1.382 1.237; 1.237 4.784]
Marker effects variance: 0.070225
Posterior means at iteration: 100
Residual variance: 2.621509
Polygenic effects covariance matrix
[1.724
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
1.02; 1.02 3.099]
Marker effects variance: 0.060721
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesL analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesL
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesL... 1%|▎ | ETA: 0:01:05[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.145179 0.438883 0.436076; 0.438883 1.259668 0.252765; 0.436076 0.252765 0.813304]
Polygenic effects covariance matrix
[1.153228 0.506613 0.501081 0.232776; 0.506613 0.796365 0.32915 0.203048; 0.501081 0.32915 0.630091 0.219997; 0.232776 0.203048 0.219997 0.644982]
Marker effects covariance matrix:
[0.028979 0.000764 0.003378; 0.000764 0.025506 0.010196; 0.003378 0.010196 0.029048]
running MCMC for BayesL... 54%|█████████████▌ | ETA: 0:00:01[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.588023 0.508094 0.596295; 0.508094 1.122767 0.304816; 0.596295 0.304816 0.996274]
Polygenic effects covariance matrix
[1.230834 0.635255 0.486027 0.409295; 0.635255 0.840258 0.37894 0.328553; 0.486027 0.37894 0.585446 0.282178; 0.409295 0.328553 0.282178 0.6637]
Marker effects covariance matrix:
[0.040614 0.000326 0.008534; 0.000326 0.02399 0.008193; 0.008534 0.008193 0.023642]
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait non_genomic analysis using complete genomic data
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 10%|▋ | ETA: 0:00:03[K
Posterior means at iteration: 50
Residual variance: 3.947419
Polygenic effects covariance matrix
[0.855 0.269; 0.269 0.792]
Posterior means at iteration: 100
Residual variance: 5.277993
Polygenic effects covariance matrix
[1.213 0.431; 0.431 0.815]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait non_genomic analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 10%|▋ | ETA: 0:00:03[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.79202 0.586832 0.587491; 0.586832 0.900624 0.416834; 0.587491 0.416834 0.699899]
Polygenic effects covariance matrix
[3.492525 0.653282 0.833342 0.760299; 0.653282 1.102923 0.606216 0.526255; 0.833342 0.606216 1.076944 0.681164; 0.760299 0.526255 0.681164 0.956798]
running MCMC for conventional (no markers)... 81%|████▉ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.266702 -0.344018 0.441981; -0.344018 1.491424 0.364544; 0.441981 0.364544 0.655918]
Polygenic effects covariance matrix
[2.116747 0.45946 0.437417 0.466392; 0.45946 0.912117 0.448495 0.439509; 0.437417 0.448495 0.879283 0.481154; 0.466392 0.439509 0.481154 0.853443]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test single-trait BayesC analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.272267 seconds (114.38 k allocations: 5.889 MiB)
imputing missing genotypes
6.172383 seconds (5.46 M allocations: 269.158 MiB, 8.68% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesC
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:02[K
Posterior means at iteration: 50
Residual variance: 2.086329
Polygenic effects covariance matrix
[0.875 0.088; 0.088 0.847]
Marker effects variance: 1.258955
π: 0.223
Posterior means at iteration: 100
Residual variance: 1.752582
Polygenic effects covariance matrix
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
[3.041 -0.628; -0.628 1.471]
Marker effects variance: 0.709353
π: 0.306
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.8 │
│ 2 │ m2 │ 0.7 │
│ 3 │ m3 │ 0.8 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesC analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000132 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.214891 seconds (158 allocations: 20.156 KiB, 99.81% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesC
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesC... 40%|██████████ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.799584 -1.612219 0.040403; -1.612219 2.44039 0.747375; 0.040403 0.747375 0.93459]
Polygenic effects covariance matrix
[0.851606 0.51276 0.290657 0.437186; 0.51276 1.01262 0.420342 0.558198; 0.290657 0.420342 0.634012 0.318387; 0.437186 0.558198 0.318387 0.72504]
Marker effects covariance matrix:
[0.50737 0.132361 0.268219; 0.132361 0.301601 0.143297; 0.268219 0.143297 0.363484]
π:
Dict([1.0, 1.0, 0.0] => 0.1291410505813946,[0.0, 0.0, 0.0] => 0.12636388241786578,[1.0, 0.0, 0.0] => 0.14569341043793022,[0.0, 1.0, 1.0] => 0.13027731678908047,[1.0, 0.0, 1.0] => 0.10431004838433226,[0.0, 0.0, 1.0] => 0.06319911681666106,[1.0, 1.0, 1.0] => 0.18939040503375226,[0.0, 1.0, 0.0] => 0.11162476953898341)
running MCMC for BayesC... 81%|████████████████████▎ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.035799 -1.95232 -0.144398; -1.95232 2.648186 0.726947; -0.144398 0.726947 0.881555]
Polygenic effects covariance matrix
[1.028665 0.324411 -0.088014 -0.048872; 0.324411 0.869372 0.326703 0.465347; -0.088014 0.326703 1.238713 0.91527; -0.048872 0.465347 0.91527 1.351795]
Marker effects covariance matrix:
[0.495122 0.224233 0.268926; 0.224233 0.400505 0.193024; 0.268926 0.193024 0.417603]
π:
Dict([1.0, 1.0, 0.0] => 0.12380692263429452,[0.0, 0.0, 0.0] => 0.12827657983669014,[1.0, 0.0, 0.0] => 0.14595906571940112,[0.0, 1.0, 1.0] => 0.11411736145033224,[1.0, 0.0, 1.0] => 0.12075359734114074,[0.0, 0.0, 1.0] => 0.10400081374832569,[1.0, 1.0, 1.0] => 0.14335807862114006,[0.0, 1.0, 0.0] => 0.11972758064867556)
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait BayesB analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000133 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.225338 seconds (158 allocations: 20.156 KiB, 99.40% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesB
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 3.753966
Polygenic effects covariance matrix
[1.201 0.699; 0.699 0.829]
π: 0.517
Posterior means at iteration: 100
Residual variance: 2.532159
Polygenic effects covariance matrix
[2.067 1.824; 1.824 2.223]
π: 0.575
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.5 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.4 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesB analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000134 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.227483 seconds (158 allocations: 20.156 KiB, 99.84% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesB
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesB... 45%|███████████▎ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[4.194016 -2.291761 -0.409959; -2.291761 2.731494 0.776305; -0.409959 0.776305 0.862124]
Polygenic effects covariance matrix
[1.084931 0.517389 0.210719 -0.245377; 0.517389 0.664403 0.05997 -0.207029; 0.210719 0.05997 0.873098 0.416936; -0.245377 -0.207029 0.416936 2.099615]
π:
Dict([1.0, 1.0, 0.0] => 0.09205834619787336,[0.0, 0.0, 0.0] => 0.14110590904499262,[1.0, 0.0, 0.0] => 0.15486758108590193,[0.0, 1.0, 1.0] => 0.12804777109701385,[1.0, 0.0, 1.0] => 0.12526693990060997,[0.0, 0.0, 1.0] => 0.13424192577544528,[1.0, 1.0, 1.0] => 0.10040233729553703,[0.0, 1.0, 0.0] => 0.12400918960262595)
running MCMC for BayesB... 84%|█████████████████████ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[4.137922 -2.553205 -0.637956; -2.553205 3.055102 0.95393; -0.637956 0.95393 0.960629]
Polygenic effects covariance matrix
[1.027297 0.423975 0.045431 -0.081729; 0.423975 0.593672 0.121342 -0.040675; 0.045431 0.121342 0.854635 0.341133; -0.081729 -0.040675 0.341133 1.281242]
π:
Dict([1.0, 1.0, 0.0] => 0.11192778269985354,[0.0, 0.0, 0.0] => 0.1370651550938405,[1.0, 0.0, 0.0] => 0.13295250061855163,[0.0, 1.0, 1.0] => 0.12793821097535224,[1.0, 0.0, 1.0] => 0.11666084089892018,[0.0, 0.0, 1.0] => 0.1280189350119462,[1.0, 1.0, 1.0] =>
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:00[K
0.10738981624430236,[0.0, 1.0, 0.0] => 0.13804675845723333)
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.4 │
│ 2 │ m2 │ 0.4 │
│ 3 │ m3 │ 0.4 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.5 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait RR-BLUP analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000124 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.224827 seconds (158 allocations: 20.156 KiB, 99.78% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods RR-BLUP
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 1.733297
Polygenic effects covariance matrix
[0.975 0.595; 0.595 1.003]
Marker effects variance: 0.647935
Posterior means at iteration: 100
Residual variance: 2.527614
Polygenic effects covariance matrix
[0.986 0.507; 0.507 1.135]
Marker effects variance: 0.521475
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait RR-BLUP analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000149 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.235627 seconds (158 allocations: 20.156 KiB, 99.83% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods RR-BLUP
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
Posterior means at iteration: 50
Residual covariance matrix:
[2.81531 -1.859312 -0.397239; -1.859312 2.885358 0.998469; -0.397239 0.998469 0.907784]
Polygenic effects covariance matrix
[0.547757 0.305484 0.173744 0.132885; 0.305484 0.901642 0.29398 0.43785; 0.173744 0.29398 0.618919 0.288518; 0.132885 0.43785 0.288518 0.669372]
Marker effects covariance matrix:
[0.471681 0.021558 0.103998; 0.021558 0.398719 0.202442; 0.103998 0.202442 0.418244]
running MCMC for RR-BLUP... 50%|████████████ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.023438 -2.058334 -0.521189; -2.058334 2.822977 1.05857; -0.521189 1.05857 0.943851]
Polygenic effects covariance matrix
[0.584471 0.296856 0.354233 0.239475; 0.296856 0.923465 0.506097 0.520495; 0.354233 0.506097 1.021437 0.519982; 0.239475 0.520495 0.519982 0.759864]
Marker effects covariance matrix:
[0.465197 0.084152 0.144768; 0.084152 0.343099 0.185278; 0.144768 0.185278 0.434146]
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait GBLUP analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test multi-trait GBLUP analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test single-trait BayesL analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000148 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.245472 seconds (158 allocations: 20.156 KiB, 99.82% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesL
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 2.288205
Polygenic effects covariance matrix
[2.136 1.409; 1.409 1.406]
Marker effects variance: 0.072843
Posterior means at iteration: 100
Residual variance: 2.690807
Polygenic effects covariance matrix
[1.827 0.974; 0.974 1.106]
Marker effects variance: 0.055582
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesL analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000119 seconds (203 allocations: 16.063 KiB)
imputing missing genotypes
0.219381 seconds (158 allocations: 20.156 KiB, 99.86% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesL
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesL... 46%|███████████▌ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.124465 -1.707861 -0.574508; -1.707861 3.255973 1.503032; -0.574508 1.503032 1.211731]
Polygenic effects covariance matrix
[1.101853 0.426624 0.676978 0.4771; 0.426624 0.951794 0.353908 0.401367; 0.676978 0.353908 0.82948 0.330965; 0.4771 0.401367 0.330965 0.676423]
Marker effects covariance matrix:
[0.026077 0.00611 0.010133; 0.00611 0.017235 0.009811; 0.010133 0.009811 0.018749]
running MCMC for BayesL... 95%|███████████████████████▊ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.012653 -1.890609 -0.239003; -1.890609 2.789292 0.953614; -0.239003 0.953614 0.885815]
Polygenic effects covariance matrix
[1.012748 0.449241 0.605313 0.508489; 0.449241 0.979202 0.492798 0.505865; 0.605313 0.492798 0.92232 0.431305; 0.508489 0.505865 0.431305 0.840726]
Marker effects covariance matrix:
[0.024928 0.008332 0.009127; 0.008332 0.021946 0.011625; 0.009127 0.011625 0.019549]
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait non_genomic analysis using incomplete genomic data
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
Posterior means at iteration: 50
Residual variance: 3.363618
Polygenic effects covariance matrix
[0.836 0.39; 0.39 0.525]
Posterior means at iteration: 100
Residual variance: 2.003963
Polygenic effects covariance matrix
[1.148 1.089; 1.089 1.812]
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait non_genomic analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 2%|▏ | ETA: 0:00:09[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.843125 0.137215 0.665346; 0.137215 1.415364 0.339442; 0.665346 0.339442 0.912046]
Polygenic effects covariance matrix
[1.46515 0.456793 0.288226 0.997611; 0.456793 0.539753 0.291448 0.41769; 0.288226 0.291448 0.603634 0.244947; 0.997611 0.41769 0.244947 1.262027]
running MCMC for conventional (no markers)... 90%|█████▍| ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.69457 0.071757 0.512905; 0.071757 1.302583 0.319044; 0.512905 0.319044 0.826825]
Polygenic effects covariance matrix
[1.700018 0.688362 0.120717 1.093634; 0.688362 0.76108 0.25917 0.495794; 0.120717 0.25917 0.771777 0.078778; 1.093634 0.495794 0.078778 1.373281]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.2.0
Commit c6da87ff4b (2019-08-20 00:03 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_XVrhf9
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
load genotype ...
from a text file with a header (marker IDs)
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
from a text file without a header (no marker IDs)
The delimiter in genotypes_noheader.txt is ','.
The header (marker IDs) is not provided in genotypes_noheader.txt.
5 markers on 7 individuals were added.
from an Array (matrix) with marker IDs and individual IDs provided
5 markers on 7 individuals were added.
from an Array (matrix) without marker IDs and with individual IDs provided
The header (marker IDs) is set to 1,2,...,#markers
5 markers on 7 individuals were added.
from an Array (matrix) without marker IDs and individual IDs
The individual IDs is set to 1,2,...,#observations
The header (marker IDs) is set to 1,2,...,#markers
5 markers on 7 individuals were added.
from a DataFrame with marker IDs and individual IDs provided
5 markers on 7 individuals were added.
Testing JWAS tests passed
Results with Julia v1.3.0
Testing was successful.
Last evaluation was ago and took 8 minutes, 12 seconds.
Resolving package versions...
Installed SortingAlgorithms ─────────── v0.3.1
Installed URIParser ─────────────────── v0.4.0
Installed Arpack ────────────────────── v0.3.1
Installed DataStructures ────────────── v0.17.6
Installed StaticArrays ──────────────── v0.12.1
Installed LazyArrays ────────────────── v0.14.10
Installed CSV ───────────────────────── v0.5.18
Installed JWAS ──────────────────────── v0.6.2
Installed Compat ────────────────────── v2.2.0
Installed InvertedIndices ───────────── v1.0.0
Installed QuadGK ────────────────────── v2.1.1
Installed CategoricalArrays ─────────── v0.7.3
Installed Parsers ───────────────────── v0.3.10
Installed StatsFuns ─────────────────── v0.9.0
Installed ProgressMeter ─────────────── v1.2.0
Installed BinaryProvider ────────────── v0.5.8
Installed Missings ──────────────────── v0.4.3
Installed SpecialFunctions ──────────── v0.8.0
Installed MacroTools ────────────────── v0.5.2
Installed ArrayLayouts ──────────────── v0.1.5
Installed Rmath ─────────────────────── v0.5.1
Installed TableTraits ───────────────── v1.0.0
Installed Distributions ─────────────── v0.21.9
Installed OrderedCollections ────────── v1.1.0
Installed JSON ──────────────────────── v0.21.0
Installed DataAPI ───────────────────── v1.1.0
Installed BinDeps ───────────────────── v0.8.10
Installed DataValueInterfaces ───────── v1.0.0
Installed FilePathsBase ─────────────── v0.7.0
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed WeakRefStrings ────────────── v0.6.1
Installed Tables ────────────────────── v0.2.11
Installed PooledArrays ──────────────── v0.5.2
Installed Reexport ──────────────────── v0.2.0
Installed DataFrames ────────────────── v0.19.4
Installed PDMats ────────────────────── v0.9.10
Installed StatsBase ─────────────────── v0.32.0
Installed FillArrays ────────────────── v0.8.2
Updating `~/.julia/environments/v1.3/Project.toml`
[c9a035f4] + JWAS v0.6.2
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[4c555306] + ArrayLayouts v0.1.5
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[336ed68f] + CSV v0.5.18
[324d7699] + CategoricalArrays v0.7.3
[34da2185] + Compat v2.2.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[31c24e10] + Distributions v0.21.9
[48062228] + FilePathsBase v0.7.0
[1a297f60] + FillArrays v0.8.2
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[c9a035f4] + JWAS v0.6.2
[5078a376] + LazyArrays v0.14.10
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.1.1
[189a3867] + Reexport v0.2.0
[79098fc4] + Rmath v0.5.1
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[ea10d353] + WeakRefStrings v0.6.1
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Testing JWAS
Status `/tmp/jl_FDOX5A/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[4c555306] ArrayLayouts v0.1.5
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[336ed68f] CSV v0.5.18
[324d7699] CategoricalArrays v0.7.3
[34da2185] Compat v2.2.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[31c24e10] Distributions v0.21.9
[48062228] FilePathsBase v0.7.0
[1a297f60] FillArrays v0.8.2
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[c9a035f4] JWAS v0.6.2
[5078a376] LazyArrays v0.14.10
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[92933f4c] ProgressMeter v1.2.0
[1fd47b50] QuadGK v2.1.1
[189a3867] Reexport v0.2.0
[79098fc4] Rmath v0.5.1
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[30578b45] URIParser v0.4.0
[ea10d353] WeakRefStrings v0.6.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
The delimiter in pedigree.txt is ','.
coding pedigree... 8%|██▋ | ETA: 0:00:01[K
coding pedigree... 100%|████████████████████████████████| Time: 0:00:00[K
calculating inbreeding... 8%|██▏ | ETA: 0:00:03[K
calculating inbreeding... 100%|█████████████████████████| Time: 0:00:00[K
Finished!
Test single-trait BayesC analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesC
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:02:00[K
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:36[K
Posterior means at iteration: 50
Residual variance: 1.35641
Polygenic effects covariance matrix
[0.515 0.159; 0.159 1.789]
Marker effects variance: 0.534877
π: 0.394
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:04[K
Posterior means at iteration: 100
Residual variance: 1.793289
Polygenic effects covariance matrix
[0.609 0.329; 0.329 1.506]
Marker effects variance: 0.518295
π: 0.501
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:04[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.7 │
│ 3 │ m3 │ 0.7 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesC analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesC
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:05:25[K
running MCMC for BayesC... 2%|▌ | ETA: 0:02:46[K
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:43[K
running MCMC for BayesC... 39%|█████████▊ | ETA: 0:00:08[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.642509 0.39229 0.411613; 0.39229 1.08724 0.534837; 0.411613 0.534837 1.030577]
Polygenic effects covariance matrix
[0.542246 0.197425 0.227916 0.333465; 0.197425 0.553751 0.279807 0.269446; 0.227916 0.279807 0.600229 0.301101; 0.333465 0.269446 0.301101 0.620353]
Marker effects covariance matrix:
[0.845311 0.097098 0.25958; 0.097098 0.359057 0.167647; 0.25958 0.167647 0.52225]
π:
Dict([1.0, 1.0, 0.0] => 0.1310370272248599,[0.0, 0.0, 0.0] => 0.1267533604420694,[1.0, 0.0, 0.0] => 0.16719864085873476,[0.0, 1.0, 1.0] => 0.11790634149396899,[1.0, 0.0, 1.0] => 0.1294156227911969,[0.0, 0.0, 1.0] => 0.10220824842708966,[1.0, 1.0, 1.0] => 0.13126188683581688,[0.0, 1.0, 0.0] => 0.09421887192626348)
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:05[K
running MCMC for BayesC... 94%|███████████████████████▌ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.013274 0.317404 0.561686; 0.317404 0.927205 0.472355; 0.561686 0.472355 1.02518]
Polygenic effects covariance matrix
[0.639291 0.272145 0.368929 0.31844; 0.272145 0.544101 0.308463 0.304713; 0.368929 0.308463 0.792117 0.212216; 0.31844 0.304713 0.212216 0.770952]
Marker effects covariance matrix:
[0.72871 0.128794 0.295368; 0.128794 0.318391 0.162128; 0.295368 0.162128 0.501416]
π:
Dict([1.0, 1.0, 0.0] => 0.1297134950837826,[0.0, 0.0, 0.0] => 0.1265721514634981,[1.0, 0.0, 0.0] => 0.15035879347195927,[0.0, 1.0, 1.0] => 0.12712720035760441,[1.0, 0.0, 1.0] => 0.12277501198154006,[0.0, 0.0, 1.0] => 0.10017866594879962,[1.0, 1.0, 1.0] => 0.1335096167485793,[0.0, 1.0, 0.0] => 0.10976506494423652)
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:05[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.8 │
│ 2 │ m2 │ 0.6 │
│ 3 │ m3 │ 0.9 │
│ 4 │ m4 │ 0.5 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait BayesB analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesB
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesB... 1%|▎ | ETA: 0:00:41[K
running MCMC for BayesB... 10%|██▌ | ETA: 0:00:06[K
Posterior means at iteration: 50
Residual variance: 0.808181
Polygenic effects covariance matrix
[0.438 0.216; 0.216 0.834]
π: 0.45
Posterior means at iteration: 100
Residual variance: 0.963115
Polygenic effects covariance matrix
[1.907 0.036; 0.036 1.309]
π: 0.441
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.5 │
│ 2 │ m2 │ 0.7 │
│ 3 │ m3 │ 0.5 │
│ 4 │ m4 │ 0.5 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesB analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesB
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesB... 1%|▎ | ETA: 0:01:31[K
running MCMC for BayesB... 10%|██▌ | ETA: 0:00:12[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.871091 0.380922 0.48106; 0.380922 1.083453 0.503047; 0.48106 0.503047 1.065927]
Polygenic effects covariance matrix
[0.737476 0.245909 0.29003 0.342329; 0.245909 0.47829 0.220569 0.214402; 0.29003 0.220569 0.596623 0.328481; 0.342329 0.214402 0.328481 0.662534]
π:
Dict([1.0, 1.0, 0.0] => 0.12273352074454617,[0.0, 0.0, 0.0] => 0.09626960785346067,[1.0, 0.0, 0.0] => 0.14565238580326106,[0.0, 1.0, 1.0] => 0.11291496985887936,[1.0, 0.0, 1.0] => 0.09579051272089625,[0.0, 0.0, 1.0] => 0.12581898943445077,[1.0, 1.0, 1.0] => 0.21498665125631217,[0.0, 1.0, 0.0] => 0.08583336232819355)
running MCMC for BayesB... 52%|█████████████ | ETA: 0:00:01[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.827834 0.28365 0.427437; 0.28365 0.936286 0.427145; 0.427437 0.427145 0.87174]
Polygenic effects covariance matrix
[0.86821 0.336099 0.398042 0.240859; 0.336099 0.655249 0.288188 0.182972; 0.398042 0.288188 0.687406 0.193051; 0.240859 0.182972 0.193051 0.611497]
π:
Dict([1.0, 1.0, 0.0] => 0.14581231114362678,[0.0, 0.0, 0.0] => 0.09622442394512049,[1.0, 0.0, 0.0] => 0.1397540311165206,[0.0, 1.0, 1.0] => 0.1179694965714482,[1.0, 0.0, 1.0] => 0.09534816146833479,[0.0, 0.0, 1.0] => 0.13601207956424916,[1.0, 1.0, 1.0] => 0.18503672156307482,[0.0, 1.0, 0.0] => 0.0838427746276251)
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:01[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.7 │
│ 2 │ m2 │ 0.8 │
│ 3 │ m3 │ 0.9 │
│ 4 │ m4 │ 0.8 │
│ 5 │ m5 │ 0.5 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait RR-BLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods RR-BLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for RR-BLUP... 1%|▎ | ETA: 0:00:12[K
Posterior means at iteration: 50
Residual variance: 1.118614
Polygenic effects covariance matrix
[0.656 0.217; 0.217 1.493]
Marker effects variance: 0.260005
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
Posterior means at iteration: 100
Residual variance: 1.090456
Polygenic effects covariance matrix
[2.477 2.194; 2.194 3.471]
Marker effects variance: 0.31885
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait RR-BLUP analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods RR-BLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for RR-BLUP... 1%|▎ | ETA: 0:00:29[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.830305 0.355608 0.158133; 0.355608 0.94329 0.180585; 0.158133 0.180585 0.541454]
Polygenic effects covariance matrix
[0.599202 0.174261 0.302961 0.289346; 0.174261 0.650788 0.206448 0.193331; 0.302961 0.206448 0.559918 0.362713; 0.289346 0.193331 0.362713 0.682921]
Marker effects covariance matrix:
[0.776308 0.163265 0.173006; 0.163265 0.657232 0.265976; 0.173006 0.265976 0.43141]
running MCMC for RR-BLUP... 54%|█████████████ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.869861 0.482496 0.302527; 0.482496 1.392303 0.569492; 0.302527 0.569492 0.854899]
Polygenic effects covariance matrix
[0.714131 0.30018 0.275689 0.240738; 0.30018 0.754358 0.230971 0.089984; 0.275689 0.230971 0.611545 0.341491; 0.240738 0.089984 0.341491 0.77704]
Marker effects covariance matrix:
[0.844443 0.316754 0.255427; 0.316754 0.592585 0.272091; 0.255427 0.272091 0.416656]
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait GBLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods GBLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
running MCMC for GBLUP... 1%|▎ | ETA: 0:01:23[K
Posterior means at iteration: 50
Residual variance: 2.184032
Polygenic effects covariance matrix
[0.779 0.435; 0.435 0.768]
Genetic variance (G matrix): 0.849318
Genetic variance (GenSel): 0.818095
Heritability: 0.313881
Posterior means at iteration: 100
Residual variance: 1.493246
Polygenic effects covariance matrix
[1.078 0.59; 0.59 1.013]
Genetic variance (G matrix): 0.709913
Genetic variance (GenSel): 0.572774
Heritability: 0.301219
running MCMC for GBLUP...100%|██████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait GBLUP analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test single-trait BayesL analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesL
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesL... 1%|▎ | ETA: 0:01:11[K
Posterior means at iteration: 50
Residual variance: 2.175582
Polygenic effects covariance matrix
[6.701 7.569; 7.569 10.409]
Marker effects variance: 0.062749
Posterior means at iteration: 100
Residual variance: 1.678579
Polygenic effects covariance matrix
[3.879 4.167; 4.167 5.715]
Marker effects variance: 0.05237
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesL analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesL
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesL... 1%|▎ | ETA: 0:00:58[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.052927 0.476565 0.327481; 0.476565 0.651241 0.307384; 0.327481 0.307384 0.834686]
Polygenic effects covariance matrix
[0.702482 0.336855 0.291542 0.370443; 0.336855 0.699778 0.368893 0.290787; 0.291542 0.368893 0.554559 0.228415; 0.370443 0.290787 0.228415 0.675642]
Marker effects covariance matrix:
[0.044114 -0.021207 0.012904; -0.021207 0.08563 0.01133; 0.012904 0.01133 0.019291]
running MCMC for BayesL... 53%|█████████████▎ | ETA: 0:00:01[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.952151 0.384302 0.281818; 0.384302 0.855902 0.405425; 0.281818 0.405425 0.848446]
Polygenic effects covariance matrix
[0.656566 0.353392 0.278572 0.31735; 0.353392 0.671574 0.318013 0.254787; 0.278572 0.318013 0.595992 0.157539; 0.31735 0.254787 0.157539 0.669961]
Marker effects covariance matrix:
[0.042136 -0.009825 0.01286; -0.009825 0.060168 0.012725; 0.01286 0.012725 0.022648]
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait non_genomic analysis using complete genomic data
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 10%|▋ | ETA: 0:00:02[K
Posterior means at iteration: 50
Residual variance: 0.887568
Polygenic effects covariance matrix
[1.639 0.999; 0.999 1.328]
Posterior means at iteration: 100
Residual variance: 0.830961
Polygenic effects covariance matrix
[3.947 2.346; 2.346 1.965]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait non_genomic analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 10%|▋ | ETA: 0:00:03[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.752195 1.736339 1.195551; 1.736339 2.584261 1.248033; 1.195551 1.248033 1.26386]
Polygenic effects covariance matrix
[0.661699 0.086026 0.311913 0.353564; 0.086026 0.674043 0.185881 0.178879; 0.311913 0.185881 0.914988 0.39423; 0.353564 0.178879 0.39423 0.788623]
running MCMC for conventional (no markers)... 68%|████▏ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.290924 2.04495 2.619507; 2.04495 2.787857 2.337749; 2.619507 2.337749 3.583985]
Polygenic effects covariance matrix
[0.729865 0.18475 0.286646 0.360848; 0.18475 0.597612 0.244456 0.253391; 0.286646 0.244456 0.750074 0.379544; 0.360848 0.253391 0.379544 0.758803]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test single-trait BayesC analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.267343 seconds (91.25 k allocations: 4.468 MiB)
imputing missing genotypes
8.062392 seconds (6.84 M allocations: 337.897 MiB, 7.22% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesC
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:02[K
Posterior means at iteration: 50
Residual variance: 3.573595
Polygenic effects covariance matrix
[4.205 3.04; 3.04 3.438]
Marker effects variance: 0.397346
π: 0.757
Posterior means at iteration: 100
Residual variance: 3.450001
Polygenic effects covariance matrix
[2.724 1.839; 1.839 2.083]
Marker effects variance: 0.293151
π: 0.653
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.4 │
│ 2 │ m2 │ 0.4 │
│ 3 │ m3 │ 0.3 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.2 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesC analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000141 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.311860 seconds (167 allocations: 20.297 KiB, 99.86% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesC
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesC... 32%|████████ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[3.032885 -1.685777 -0.2197; -1.685777 2.220469 0.561772; -0.2197 0.561772 0.599536]
Polygenic effects covariance matrix
[0.729611 0.178572 0.194561 0.134096; 0.178572 0.462419 0.180381 0.216227; 0.194561 0.180381 0.404644 0.218202; 0.134096 0.216227 0.218202 0.693174]
Marker effects covariance matrix:
[0.406394 0.233393 0.154885; 0.233393 0.668272 0.114694; 0.154885 0.114694 0.401765]
π:
Dict([1.0, 1.0, 0.0] => 0.08216469108183569,[0.0, 0.0, 0.0] => 0.11104069925292183,[1.0, 0.0, 0.0] => 0.23481982152394093,[0.0, 1.0, 1.0] => 0.12189593197670125,[1.0, 0.0, 1.0] => 0.10019132778871703,[0.0, 0.0, 1.0] => 0.12864908738634698,[1.0, 1.0, 1.0] => 0.09275067503985174,[0.0, 1.0, 0.0] => 0.12848776594968453)
running MCMC for BayesC... 65%|████████████████▎ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.24555 -2.125477 -0.383653; -2.125477 2.76907 0.764234; -0.383653 0.764234 0.704973]
Polygenic effects covariance matrix
[0.722055 0.181884 0.25954 0.222652; 0.181884 0.462302 0.206209 0.183975; 0.25954 0.206209 0.480343 0.224382; 0.222652 0.183975 0.224382 0.621118]
Marker effects covariance matrix:
[0.416295 0.234583 0.147681; 0.234583 0.542133 0.130065; 0.147681 0.130065 0.36041]
π:
Dict([1.0, 1.0, 0.0] => 0.09755287474367844,[0.0, 0.0, 0.0] => 0.1316640342710369,[1.0, 0.0, 0.0] => 0.18233340300032116,[0.0, 1.0, 1.0] => 0.11240342312264762,[1.0, 0.0, 1.0] => 0.10877182586480402,[0.0, 0.0, 1.0] => 0.11910346029625533,[1.0, 1.0, 1.0] => 0.1317078769266478,[0.0, 1.0, 0.0] => 0.11646310177460852)
The version of Julia and Platform in use:
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.5 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.4 │
│ 4 │ m4 │ 0.9 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait BayesB analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000131 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.257400 seconds (167 allocations: 20.297 KiB, 99.86% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesB
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 4.045673
Polygenic effects covariance matrix
[0.939 0.161; 0.161 1.867]
π: 0.539
Posterior means at iteration: 100
Residual variance: 3.145791
Polygenic effects covariance matrix
[1.037 0.145; 0.145 3.755]
π: 0.428
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.7 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.7 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesB analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000135 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.248987 seconds (167 allocations: 20.297 KiB, 99.85% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesB
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesB... 47%|███████████▊ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[3.579008 -2.291532 -0.571055; -2.291532 3.093864 1.176408; -0.571055 1.176408 1.259518]
Polygenic effects covariance matrix
[0.538723 0.258542 0.234348 0.260964; 0.258542 0.717684 0.336885 0.369885; 0.234348 0.336885 0.535942 0.243618; 0.260964 0.369885 0.243618 0.764025]
π:
Dict([1.0, 1.0, 0.0] => 0.08796511413401095,[0.0, 0.0, 0.0] => 0.13606057081399459,[1.0, 0.0, 0.0] => 0.1498884316585501,[0.0, 1.0, 1.0] => 0.10448294413712289,[1.0, 0.0, 1.0] => 0.13135352654790827,[0.0, 0.0, 1.0] => 0.08959112642042515,[1.0, 1.0, 1.0] => 0.1962076618128159,[0.0, 1.0, 0.0] => 0.10445062447517223)
running MCMC for BayesB... 83%|████████████████████▊ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.609415 -2.316096 -0.532777; -2.316096 3.231556 1.171807; -0.532777 1.171807 1.051081]
Polygenic effects covariance matrix
[0.650841 0.268374 0.337923 0.201378; 0.268374 0.801316 0.31922 0.351962; 0.337923 0.31922 0.649122 0.243548; 0.201378 0.351962 0.243548 0.647885]
π:
Dict([1.0, 1.0, 0.0] => 0.10145496535921936,[0.0, 0.0, 0.0] => 0.12410625165197775,[1.0, 0.0, 0.0] => 0.11715545268578176,[0.0, 1.0, 1.0] => 0.12824343507443708,[1.0, 0.0, 1.0] => 0.11551340361228006,[0.0, 0.0, 1.0] => 0.11490056597531767,
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:00[K
[1.0, 1.0, 1.0] => 0.17191670525872388,[0.0, 1.0, 0.0] => 0.1267092203822625)
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.5 │
│ 2 │ m2 │ 0.4 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.5 │
│ 5 │ m5 │ 0.7 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait RR-BLUP analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000156 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.242369 seconds (167 allocations: 20.297 KiB, 99.84% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods RR-BLUP
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 1.552785
Polygenic effects covariance matrix
[0.81 0.102; 0.102 0.913]
Marker effects variance: 1.41226
Posterior means at iteration: 100
Residual variance: 4.23702
Polygenic effects covariance matrix
[0.723 0.177; 0.177 0.693]
Marker effects variance: 1.167101
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait RR-BLUP analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000130 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.237896 seconds (167 allocations: 20.297 KiB, 99.85% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods RR-BLUP
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
Posterior means at iteration: 50
Residual covariance matrix:
[2.995629 -2.133093 -0.313289; -2.133093 2.920545 0.904628; -0.313289 0.904628 0.753779]
Polygenic effects covariance matrix
[0.761425 0.202159 0.233414 0.185491; 0.202159 0.669245 0.251251 0.173854; 0.233414 0.251251 0.490714 0.156229; 0.185491 0.173854 0.156229 0.576711]
Marker effects covariance matrix:
[0.36347 0.202607 0.133966; 0.202607 0.363122 0.182617; 0.133966 0.182617 0.369876]
running MCMC for RR-BLUP... 50%|████████████ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[3.404297 -2.555284 -0.528113; -2.555284 3.390805 0.991399; -0.528113 0.991399 0.754391]
Polygenic effects covariance matrix
[0.719705 0.286642 0.222257 0.23331; 0.286642 0.632936 0.234626 0.215729; 0.222257 0.234626 0.470669 0.122794; 0.23331 0.215729 0.122794 0.582174]
Marker effects covariance matrix:
[0.334976 0.168777 0.151787; 0.168777 0.368885 0.210185; 0.151787 0.210185 0.410889]
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait GBLUP analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test multi-trait GBLUP analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test single-trait BayesL analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000129 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.250920 seconds (167 allocations: 20.297 KiB, 99.82% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesL
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 5.040434
Polygenic effects covariance matrix
[1.122 0.461; 0.461 1.395]
Marker effects variance: 0.031689
Posterior means at iteration: 100
Residual variance: 3.963762
Polygenic effects covariance matrix
[1.299 0.702; 0.702 1.216]
Marker effects variance: 0.034606
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesL analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000114 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.253647 seconds (167 allocations: 20.297 KiB, 99.87% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesL
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesL... 44%|███████████ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.960647 -1.926611 -0.236914; -1.926611 2.48362 0.720587; -0.236914 0.720587 0.63512]
Polygenic effects covariance matrix
[0.687183 0.356883 0.280197 0.302067; 0.356883 0.710138 0.355059 0.267162; 0.280197 0.355059 0.736999 0.30608; 0.302067 0.267162 0.30608 0.605351]
Marker effects covariance matrix:
[0.032306 0.007458 0.010354; 0.007458 0.024493 0.011869; 0.010354 0.011869 0.026273]
running MCMC for BayesL... 80%|████████████████████ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.799562 -1.755613 -0.401302; -1.755613 2.307375 0.836159; -0.401302 0.836159 0.761595]
Polygenic effects covariance matrix
[0.726645 0.4191 0.231706 0.250896; 0.4191 0.860882 0.316706 0.372538; 0.231706 0.316706 0.876098 0.412799; 0.250896 0.372538 0.412799 0.882001]
Marker effects covariance matrix:
[0.033102 0.00727 0.008934; 0.00727 0.024679 0.013501; 0.008934 0.013501 0.025248]
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait non_genomic analysis using incomplete genomic data
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
Posterior means at iteration: 50
Residual variance: 0.849581
Polygenic effects covariance matrix
[0.845 0.504; 0.504 0.664]
Posterior means at iteration: 100
Residual variance: 2.006282
Polygenic effects covariance matrix
[1.235 0.949; 0.949 1.315]
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait non_genomic analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
Posterior means at iteration: 50
Residual covariance matrix:
[1.708709 -0.238547 0.62401; -0.238547 1.337667 0.032676; 0.62401 0.032676 0.798992]
Polygenic effects covariance matrix
[0.732339 0.316755 0.199842 0.313582; 0.316755 0.6213 0.088639 0.261992; 0.199842 0.088639 0.617483 0.119309; 0.313582 0.261992 0.119309 0.566163]
running MCMC for conventional (no markers)... 82%|████▉ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.056221 -0.209538 0.735284; -0.209538 1.358683 0.080857; 0.735284 0.080857 0.849925]
Polygenic effects covariance matrix
[0.787497 0.288726 0.187893 0.29691; 0.288726 0.662605 0.190393 0.287212; 0.187893 0.190393 0.785653 0.188294; 0.29691 0.287212 0.188294 0.708307]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.0
Commit 46ce4d7933 (2019-11-26 06:09 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_FDOX5A
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
load genotype ...
from a text file with a header (marker IDs)
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
from a text file without a header (no marker IDs)
The delimiter in genotypes_noheader.txt is ','.
The header (marker IDs) is not provided in genotypes_noheader.txt.
5 markers on 7 individuals were added.
from an Array (matrix) with marker IDs and individual IDs provided
5 markers on 7 individuals were added.
from an Array (matrix) without marker IDs and with individual IDs provided
The header (marker IDs) is set to 1,2,...,#markers
5 markers on 7 individuals were added.
from an Array (matrix) without marker IDs and individual IDs
The individual IDs is set to 1,2,...,#observations
The header (marker IDs) is set to 1,2,...,#markers
5 markers on 7 individuals were added.
from a DataFrame with marker IDs and individual IDs provided
5 markers on 7 individuals were added.
Testing JWAS tests passed
Results with Julia v1.3.1-pre-7704df0a5a
Testing was successful.
Last evaluation was ago and took 7 minutes, 43 seconds.
Resolving package versions...
Installed ArrayLayouts ──────────────── v0.1.5
Installed WeakRefStrings ────────────── v0.6.1
Installed Tables ────────────────────── v0.2.11
Installed QuadGK ────────────────────── v2.1.1
Installed DataStructures ────────────── v0.17.6
Installed SpecialFunctions ──────────── v0.8.0
Installed LazyArrays ────────────────── v0.14.10
Installed JWAS ──────────────────────── v0.6.2
Installed Compat ────────────────────── v2.2.0
Installed DataFrames ────────────────── v0.19.4
Installed MacroTools ────────────────── v0.5.2
Installed FillArrays ────────────────── v0.8.2
Installed BinDeps ───────────────────── v0.8.10
Installed StaticArrays ──────────────── v0.12.1
Installed StatsBase ─────────────────── v0.32.0
Installed Missings ──────────────────── v0.4.3
Installed Arpack ────────────────────── v0.3.1
Installed URIParser ─────────────────── v0.4.0
Installed TableTraits ───────────────── v1.0.0
Installed StatsFuns ─────────────────── v0.9.0
Installed InvertedIndices ───────────── v1.0.0
Installed FilePathsBase ─────────────── v0.7.0
Installed ProgressMeter ─────────────── v1.2.0
Installed PooledArrays ──────────────── v0.5.2
Installed BinaryProvider ────────────── v0.5.8
Installed Rmath ─────────────────────── v0.5.1
Installed DataValueInterfaces ───────── v1.0.0
Installed DataAPI ───────────────────── v1.1.0
Installed Reexport ──────────────────── v0.2.0
Installed Distributions ─────────────── v0.21.9
Installed CategoricalArrays ─────────── v0.7.3
Installed JSON ──────────────────────── v0.21.0
Installed OrderedCollections ────────── v1.1.0
Installed PDMats ────────────────────── v0.9.10
Installed IteratorInterfaceExtensions ─ v1.0.0
Installed Parsers ───────────────────── v0.3.10
Installed SortingAlgorithms ─────────── v0.3.1
Installed CSV ───────────────────────── v0.5.18
Updating `~/.julia/environments/v1.3/Project.toml`
[c9a035f4] + JWAS v0.6.2
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7d9fca2a] + Arpack v0.3.1
[4c555306] + ArrayLayouts v0.1.5
[9e28174c] + BinDeps v0.8.10
[b99e7846] + BinaryProvider v0.5.8
[336ed68f] + CSV v0.5.18
[324d7699] + CategoricalArrays v0.7.3
[34da2185] + Compat v2.2.0
[9a962f9c] + DataAPI v1.1.0
[a93c6f00] + DataFrames v0.19.4
[864edb3b] + DataStructures v0.17.6
[e2d170a0] + DataValueInterfaces v1.0.0
[31c24e10] + Distributions v0.21.9
[48062228] + FilePathsBase v0.7.0
[1a297f60] + FillArrays v0.8.2
[41ab1584] + InvertedIndices v1.0.0
[82899510] + IteratorInterfaceExtensions v1.0.0
[682c06a0] + JSON v0.21.0
[c9a035f4] + JWAS v0.6.2
[5078a376] + LazyArrays v0.14.10
[1914dd2f] + MacroTools v0.5.2
[e1d29d7a] + Missings v0.4.3
[bac558e1] + OrderedCollections v1.1.0
[90014a1f] + PDMats v0.9.10
[69de0a69] + Parsers v0.3.10
[2dfb63ee] + PooledArrays v0.5.2
[92933f4c] + ProgressMeter v1.2.0
[1fd47b50] + QuadGK v2.1.1
[189a3867] + Reexport v0.2.0
[79098fc4] + Rmath v0.5.1
[a2af1166] + SortingAlgorithms v0.3.1
[276daf66] + SpecialFunctions v0.8.0
[90137ffa] + StaticArrays v0.12.1
[2913bbd2] + StatsBase v0.32.0
[4c63d2b9] + StatsFuns v0.9.0
[3783bdb8] + TableTraits v1.0.0
[bd369af6] + Tables v0.2.11
[30578b45] + URIParser v0.4.0
[ea10d353] + WeakRefStrings v0.6.1
[2a0f44e3] + Base64
[ade2ca70] + Dates
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[9fa8497b] + Future
[b77e0a4c] + InteractiveUtils
[76f85450] + LibGit2
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[44cfe95a] + Pkg
[de0858da] + Printf
[3fa0cd96] + REPL
[9a3f8284] + Random
[ea8e919c] + SHA
[9e88b42a] + Serialization
[1a1011a3] + SharedArrays
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[4607b0f0] + SuiteSparse
[8dfed614] + Test
[cf7118a7] + UUIDs
[4ec0a83e] + Unicode
Building SpecialFunctions → `~/.julia/packages/SpecialFunctions/ne2iw/deps/build.log`
Building Arpack ──────────→ `~/.julia/packages/Arpack/cu5By/deps/build.log`
Building Rmath ───────────→ `~/.julia/packages/Rmath/4wt82/deps/build.log`
Testing JWAS
Status `/tmp/jl_T1LX2I/Manifest.toml`
[7d9fca2a] Arpack v0.3.1
[4c555306] ArrayLayouts v0.1.5
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[336ed68f] CSV v0.5.18
[324d7699] CategoricalArrays v0.7.3
[34da2185] Compat v2.2.0
[9a962f9c] DataAPI v1.1.0
[a93c6f00] DataFrames v0.19.4
[864edb3b] DataStructures v0.17.6
[e2d170a0] DataValueInterfaces v1.0.0
[31c24e10] Distributions v0.21.9
[48062228] FilePathsBase v0.7.0
[1a297f60] FillArrays v0.8.2
[41ab1584] InvertedIndices v1.0.0
[82899510] IteratorInterfaceExtensions v1.0.0
[682c06a0] JSON v0.21.0
[c9a035f4] JWAS v0.6.2
[5078a376] LazyArrays v0.14.10
[1914dd2f] MacroTools v0.5.2
[e1d29d7a] Missings v0.4.3
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[69de0a69] Parsers v0.3.10
[2dfb63ee] PooledArrays v0.5.2
[92933f4c] ProgressMeter v1.2.0
[1fd47b50] QuadGK v2.1.1
[189a3867] Reexport v0.2.0
[79098fc4] Rmath v0.5.1
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.8.0
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.9.0
[3783bdb8] TableTraits v1.0.0
[bd369af6] Tables v0.2.11
[30578b45] URIParser v0.4.0
[ea10d353] WeakRefStrings v0.6.1
[2a0f44e3] Base64 [`@stdlib/Base64`]
[ade2ca70] Dates [`@stdlib/Dates`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[9fa8497b] Future [`@stdlib/Future`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[76f85450] LibGit2 [`@stdlib/LibGit2`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[44cfe95a] Pkg [`@stdlib/Pkg`]
[de0858da] Printf [`@stdlib/Printf`]
[3fa0cd96] REPL [`@stdlib/REPL`]
[9a3f8284] Random [`@stdlib/Random`]
[ea8e919c] SHA [`@stdlib/SHA`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[1a1011a3] SharedArrays [`@stdlib/SharedArrays`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[4607b0f0] SuiteSparse [`@stdlib/SuiteSparse`]
[8dfed614] Test [`@stdlib/Test`]
[cf7118a7] UUIDs [`@stdlib/UUIDs`]
[4ec0a83e] Unicode [`@stdlib/Unicode`]
The delimiter in pedigree.txt is ','.
calculating inbreeding... 8%|██▏ | ETA: 0:00:03[K
calculating inbreeding... 100%|█████████████████████████| Time: 0:00:00[K
Finished!
Test single-trait BayesC analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesC
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:02:16[K
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:38[K
Posterior means at iteration: 50
Residual variance: 2.864765
Polygenic effects covariance matrix
[1.673 -0.004; -0.004 2.211]
Marker effects variance: 0.571217
π: 0.361
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:05[K
Posterior means at iteration: 100
Residual variance: 3.332548
Polygenic effects covariance matrix
[1.751 0.21; 0.21 1.53]
Marker effects variance: 0.629005
π: 0.524
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:04[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.4 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesC analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesC
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesC... 1%|▎ | ETA: 0:05:00[K
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:36[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.894685 0.324044 0.370796; 0.324044 0.983766 0.559078; 0.370796 0.559078 0.781513]
Polygenic effects covariance matrix
[2.5682 1.348248 1.055543 0.656861; 1.348248 1.589093 0.868322 0.605754; 1.055543 0.868322 1.055195 0.584799; 0.656861 0.605754 0.584799 1.112812]
Marker effects covariance matrix:
[0.440674 0.205729 0.197932; 0.205729 0.445941 0.188837; 0.197932 0.188837 0.346224]
π:
Dict([1.0, 1.0, 0.0] => 0.1404284865906761,[0.0, 0.0, 0.0] => 0.10713430941273201,[1.0, 0.0, 0.0] => 0.0947206936086506,[0.0, 1.0, 1.0] => 0.08467990358826984,[1.0, 0.0, 1.0] => 0.1646084222560471,[0.0, 0.0, 1.0] => 0.1175248703457411,[1.0, 1.0, 1.0] => 0.16561510399860968,[0.0, 1.0, 0.0] => 0.12528821019927375)
running MCMC for BayesC... 50%|████████████▌ | ETA: 0:00:04[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.959445 0.310074 0.334619; 0.310074 1.053086 0.472015; 0.334619 0.472015 0.739885]
Polygenic effects covariance matrix
[2.03259 1.461384 0.951552 0.586745; 1.461384 2.607468 1.293102 0.692862; 0.951552 1.293102 1.101137 0.526248; 0.586745 0.692862 0.526248 0.942346]
Marker effects covariance matrix:
[0.555713 0.225909 0.274681; 0.225909 0.418177 0.20699; 0.274681 0.20699 0.416374]
π:
Dict([1.0, 1.0, 0.0] => 0.13117681249971622,[0.0, 0.0, 0.0] => 0.11972496182312915,[1.0, 0.0, 0.0] => 0.12802305150492194,[0.0, 1.0, 1.0] => 0.1100798808824889,[1.0, 0.0, 1.0] => 0.14666107173711204,[0.0, 0.0, 1.0] => 0.11226651830085896,[1.0, 1.0, 1.0] => 0.1319170377919485,[0.0, 1.0, 0.0] => 0.1201506654598245)
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:04[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.3 │
│ 3 │ m3 │ 0.7 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait BayesB analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesB
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesB... 1%|▎ | ETA: 0:00:35[K
running MCMC for BayesB... 10%|██▌ | ETA: 0:00:05[K
Posterior means at iteration: 50
Residual variance: 0.581285
Polygenic effects covariance matrix
[4.211 1.354; 1.354 1.019]
π: 0.44
Posterior means at iteration: 100
Residual variance: 0.649692
Polygenic effects covariance matrix
[3.49 0.816; 0.816 0.772]
π: 0.424
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.6 │
│ 3 │ m3 │ 0.6 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.8 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesB analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesB
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesB... 1%|▎ | ETA: 0:01:27[K
running MCMC for BayesB... 10%|██▌ | ETA: 0:00:11[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.634124 0.455928 0.327188; 0.455928 1.654915 0.46162; 0.327188 0.46162 0.71086]
Polygenic effects covariance matrix
[0.575143 0.190918 0.187034 0.30811; 0.190918 0.556469 0.226819 0.270064; 0.187034 0.226819 0.785423 0.167006; 0.30811 0.270064 0.167006 0.618108]
π:
Dict([1.0, 1.0, 0.0] => 0.12756025379782185,[0.0, 0.0, 0.0] => 0.09155731736409417,[1.0, 0.0, 0.0] => 0.1955088173064182,[0.0, 1.0, 1.0] => 0.12289704731384928,[1.0, 0.0, 1.0] => 0.13142742487749914,[0.0, 0.0, 1.0] => 0.10172769182512817,[1.0, 1.0, 1.0] => 0.12500813145782405,[0.0, 1.0, 0.0] => 0.10431331605736517)
running MCMC for BayesB... 67%|████████████████▊ | ETA: 0:00:01[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.775352 0.396437 0.421074; 0.396437 1.406933 0.659812; 0.421074 0.659812 1.081444]
Polygenic effects covariance matrix
[0.602092 0.221332 0.247586 0.230444; 0.221332 0.585546 0.280486 0.253767; 0.247586 0.280486 0.667946 0.198598; 0.230444 0.253767 0.198598 0.604574]
π:
Dict([1.0, 1.0, 0.0] => 0.10949272183485982,[0.0, 0.0, 0.0] => 0.09860356906046522,[1.0, 0.0, 0.0] => 0.15702601755488899,[0.0, 1.0, 1.0] => 0.10391866746709479,[1.0, 0.0, 1.0] => 0.15498461091118032,[0.0, 0.0, 1.0] => 0.10731600718529452,[1.0, 1.0, 1.0] => 0.14587848823720048,[0.0, 1.0, 0.0] => 0.12277991774901585)
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:01[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.3 │
│ 2 │ m2 │ 0.7 │
│ 3 │ m3 │ 0.9 │
│ 4 │ m4 │ 0.8 │
│ 5 │ m5 │ 0.4 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait RR-BLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods RR-BLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for RR-BLUP... 1%|▎ | ETA: 0:00:12[K
Posterior means at iteration: 50
Residual variance: 0.981041
Polygenic effects covariance matrix
[1.475 0.949; 0.949 1.663]
Marker effects variance: 0.676357
Posterior means at iteration: 100
Residual variance: 0.879927
Polygenic effects covariance matrix
[1.561 0.907; 0.907 1.265]
Marker effects variance: 0.819238
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait RR-BLUP analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods RR-BLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for RR-BLUP... 1%|▎ | ETA: 0:00:24[K
Posterior means at iteration: 50
Residual covariance matrix:
[1.199718 0.044826 0.586919; 0.044826 0.852367 0.112055; 0.586919 0.112055 0.822309]
Polygenic effects covariance matrix
[0.916724 0.209247 0.278816 0.106312; 0.209247 0.515199 0.231316 0.393497; 0.278816 0.231316 0.565555 0.283371; 0.106312 0.393497 0.283371 0.779454]
Marker effects covariance matrix:
[0.535303 0.177957 0.34691; 0.177957 0.492483 0.207702; 0.34691 0.207702 0.552904]
running MCMC for RR-BLUP... 65%|███████████████▋ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.050426 0.114605 0.454751; 0.114605 0.724979 0.144022; 0.454751 0.144022 0.756511]
Polygenic effects covariance matrix
[0.880806 0.400273 0.29275 0.293548; 0.400273 0.819311 0.272035 0.511282; 0.29275 0.272035 0.525108 0.328866; 0.293548 0.511282 0.328866 0.977122]
Marker effects covariance matrix:
[0.567503 0.120323 0.240538; 0.120323 0.406406 0.172676; 0.240538 0.172676 0.45586]
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait GBLUP analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods GBLUP
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
running MCMC for GBLUP... 1%|▎ | ETA: 0:01:47[K
Posterior means at iteration: 50
Residual variance: 1.386443
Polygenic effects covariance matrix
[1.483 0.398; 0.398 0.89]
Genetic variance (G matrix): 1.491433
Genetic variance (GenSel): 1.561803
Heritability: 0.465957
running MCMC for GBLUP...100%|██████████████████████████| Time: 0:00:01[K
Posterior means at iteration: 100
Residual variance: 1.238143
Polygenic effects covariance matrix
[1.235 0.481; 0.481 0.948]
Genetic variance (G matrix): 1.396291
Genetic variance (GenSel): 1.577867
Heritability: 0.49466
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait GBLUP analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test single-trait BayesL analysis using complete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods BayesL
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesL... 1%|▎ | ETA: 0:01:17[K
Posterior means at iteration: 50
Residual variance: 2.685162
Polygenic effects covariance matrix
[0.693 0.071; 0.071 0.411]
Marker effects variance: 0.052351
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
Posterior means at iteration: 100
Residual variance: 2.13205
Polygenic effects covariance matrix
[0.714 0.265; 0.265 0.683]
Marker effects variance: 0.051244
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesL analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods BayesL
complete genomic data
(i.e., non-single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for BayesL... 1%|▎ | ETA: 0:00:59[K
Posterior means at iteration: 50
Residual covariance matrix:
[0.750262 0.304316 0.326311; 0.304316 1.003075 0.266877; 0.326311 0.266877 0.659249]
Polygenic effects covariance matrix
[0.569846 0.255221 0.356763 0.158025; 0.255221 0.461598 0.247094 0.125068; 0.356763 0.247094 0.689694 0.172241; 0.158025 0.125068 0.172241 0.555936]
Marker effects covariance matrix:
[0.065349 0.012504 0.017989; 0.012504 0.027286 0.014629; 0.017989 0.014629 0.022554]
running MCMC for BayesL... 70%|█████████████████▌ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[0.725954 0.335331 0.317419; 0.335331 0.90601 0.292664; 0.317419 0.292664 0.654841]
Polygenic effects covariance matrix
[0.512901 0.217627 0.316525 0.165024; 0.217627 0.526748 0.159154 0.130665; 0.316525 0.159154 0.746089 0.243376; 0.165024 0.130665 0.243376 0.566206]
Marker effects covariance matrix:
[0.054857 0.007177 0.014565; 0.007177 0.035729
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
0.015595; 0.014565 0.015595 0.022631]
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
Test single-trait non_genomic analysis using complete genomic data
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 10%|▋ | ETA: 0:00:02[K
Posterior means at iteration: 50
Residual variance: 0.617698
Polygenic effects covariance matrix
[0.985 1.057; 1.057 1.976]
Posterior means at iteration: 100
Residual variance: 0.701763
Polygenic effects covariance matrix
[3.37 3.351; 3.351 4.238]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait non_genomic analysis using complete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC for conventional (no markers)... 10%|▋ | ETA: 0:00:03[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.509522 0.624794 0.485834; 0.624794 0.754794 0.346382; 0.485834 0.346382 0.618264]
Polygenic effects covariance matrix
[0.696895 0.309629 0.344464 0.356754; 0.309629 0.646467 0.318101 0.279559; 0.344464 0.318101 0.551073 0.284439; 0.356754 0.279559 0.284439 0.530793]
running MCMC for conventional (no markers)... 80%|████▊ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.723163 0.512749 0.483167; 0.512749 0.737064 0.344669; 0.483167 0.344669 0.643116]
Polygenic effects covariance matrix
[0.609833 0.131241 0.27838 0.427596; 0.131241 0.822824 0.264939 -0.029309; 0.27838 0.264939 0.546836 0.276367; 0.427596 -0.029309 0.276367 0.844863]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test single-trait BayesC analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.263143 seconds (91.25 k allocations: 4.468 MiB)
imputing missing genotypes
7.356833 seconds (6.84 M allocations: 338.122 MiB, 6.25% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesC
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
running MCMC for BayesC... 10%|██▌ | ETA: 0:00:02[K
Posterior means at iteration: 50
Residual variance: 4.802705
Polygenic effects covariance matrix
[0.808 0.106; 0.106 2.175]
Marker effects variance: 0.266428
π: 0.373
Posterior means at iteration: 100
Residual variance: 4.021556
Polygenic effects covariance matrix
[1.027 0.278; 0.278 1.4]
Marker effects variance: 0.505513
π: 0.419
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.6 │
│ 3 │ m3 │ 0.4 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.7 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesC analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000125 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.245309 seconds (167 allocations: 20.297 KiB, 99.84% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesC
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesC... 32%|████████ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[3.035928 -1.568213 -0.067624; -1.568213 2.470927 0.905862; -0.067624 0.905862 0.861313]
Polygenic effects covariance matrix
[0.605255 0.321894 0.280755 0.229863; 0.321894 1.08059 0.437057 0.211303; 0.280755 0.437057 0.658016 0.183271; 0.229863 0.211303 0.183271 0.408837]
Marker effects covariance matrix:
[0.299063 0.158615 0.15708; 0.158615 0.370119 0.140066; 0.15708 0.140066 0.399011]
π:
Dict([1.0, 1.0, 0.0] => 0.09311531727650041,[0.0, 0.0, 0.0] => 0.0960688704792752,[1.0, 0.0, 0.0] => 0.1478338171191563,[0.0, 1.0, 1.0] => 0.12321058326262888,[1.0, 0.0, 1.0] => 0.12574537404485314,[0.0, 0.0, 1.0] => 0.12783094974187326,[1.0, 1.0, 1.0] => 0.14777329591039126,[0.0, 1.0, 0.0] => 0.13842179216532147)
running MCMC for BayesC... 72%|██████████████████ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.411515 -1.374651 -0.05716; -1.374651 2.247722 0.696568; -0.05716 0.696568 0.76955]
Polygenic effects covariance matrix
[1.808362 -1.057052 -0.496985 0.705973; -1.057052 2.581179 1.313993 -0.395308; -0.496985 1.313993 1.215643 -0.066104; 0.705973 -0.395308 -0.066104 0.946986]
Marker effects covariance matrix:
[0.452127 0.171303 0.19294; 0.171303 0.447467 0.161858; 0.19294 0.161858 0.411789]
π:
Dict([1.0, 1.0, 0.0] => 0.0997607625756953,[0.0, 0.0, 0.0] => 0.1210177997850664,[1.0, 0.0, 0.0] => 0.1298684698346887,
running MCMC for BayesC...100%|█████████████████████████| Time: 0:00:00[K
[0.0, 1.0, 1.0] => 0.10954131181770436,[1.0, 0.0, 1.0] => 0.14219738590523093,[0.0, 0.0, 1.0] => 0.11051100693542584,[1.0, 1.0, 1.0] => 0.1596797320340662,[0.0, 1.0, 0.0] => 0.1274235311121221)
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.4 │
│ 2 │ m2 │ 0.5 │
│ 3 │ m3 │ 0.5 │
│ 4 │ m4 │ 0.5 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait BayesB analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000098 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.221733 seconds (167 allocations: 20.297 KiB, 99.87% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesB
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 1.714193
Polygenic effects covariance matrix
[3.518 2.689; 2.689 2.997]
π: 0.516
Posterior means at iteration: 100
Residual variance: 1.898947
Polygenic effects covariance matrix
[2.976 2.209; 2.209 2.453]
π: 0.585
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.3 │
│ 2 │ m2 │ 0.3 │
│ 3 │ m3 │ 0.5 │
│ 4 │ m4 │ 0.4 │
│ 5 │ m5 │ 0.5 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 2 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test multi-trait BayesB analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000126 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.218979 seconds (167 allocations: 20.297 KiB, 99.82% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesB
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi true
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesB... 45%|███████████▎ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.017522 -1.738368 -0.596942; -1.738368 3.031728 1.342672; -0.596942 1.342672 1.087164]
Polygenic effects covariance matrix
[0.876993 0.275354 0.544926 0.346838; 0.275354 0.87493 0.387317 0.301614; 0.544926 0.387317 1.11247 0.377667; 0.346838 0.301614 0.377667 0.60389]
π:
Dict([1.0, 1.0, 0.0] => 0.17461154719112967,[0.0, 0.0, 0.0] => 0.10357872409017735,[1.0, 0.0, 0.0] => 0.09097795181624818,[0.0, 1.0, 1.0] => 0.08883228277500967,[1.0, 0.0, 1.0] => 0.1025290140461996,[0.0, 0.0, 1.0] => 0.14642063018130963,[1.0, 1.0, 1.0] => 0.10428160927596065,[0.0, 1.0, 0.0] => 0.18876824062396524)
running MCMC for BayesB... 90%|██████████████████████▌ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.580742 -1.929147 -0.482724; -1.929147 2.989149 1.13923; -0.482724 1.13923 0.962704]
Polygenic effects covariance matrix
[0.762764 0.38786 0.46732 0.279347; 0.38786 1.038423 0.512811 0.369212; 0.46732 0.512811 1.005028 0.36503; 0.279347 0.369212 0.36503 0.611428]
π:
Dict([1.0, 1.0, 0.0] => 0.1475883121193188,[0.0, 0.0, 0.0] => 0.10575187685210131,[1.0, 0.0, 0.0] => 0.10372486605854948,[0.0, 1.0, 1.0] => 0.10728359023322849,[1.0, 0.0, 1.0] => 0.12381155004468067,[0.0, 0.0, 1.0] => 0.12676804344917866,[1.0, 1.0, 1.0] => 0.11300015173791009,[0.0, 1.0, 0.0] => 0.1720716095050325)
running MCMC for BayesB...100%|█████████████████████████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 0.6 │
│ 2 │ m2 │ 0.6 │
│ 3 │ m3 │ 0.4 │
│ 4 │ m4 │ 0.6 │
│ 5 │ m5 │ 0.6 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait RR-BLUP analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000119 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.270396 seconds (167 allocations: 20.297 KiB, 99.87% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods RR-BLUP
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 1.412727
Polygenic effects covariance matrix
[0.944 0.595; 0.595 1.029]
Marker effects variance: 0.551684
Posterior means at iteration: 100
Residual variance: 1.522112
Polygenic effects covariance matrix
[1.338 0.838; 0.838 1.242]
Marker effects variance: 0.451641
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait RR-BLUP analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000132 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.248726 seconds (167 allocations: 20.297 KiB, 99.84% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods RR-BLUP
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
Posterior means at iteration: 50
Residual covariance matrix:
[2.445103 -2.152606 -0.405365; -2.152606 3.560939 1.143527; -0.405365 1.143527 0.849471]
Polygenic effects covariance matrix
[1.611108 0.531476 0.270214 0.053505; 0.531476 0.63525 0.240907 0.220661; 0.270214 0.240907 0.514668 0.185827; 0.053505 0.220661 0.
running MCMC for RR-BLUP... 50%|████████████ | ETA: 0:00:00[K185827 0.606773]
Marker effects covariance matrix:
[0.377939 0.194975 0.198908; 0.194975 0.360182 0.104397; 0.198908 0.104397 0.541208]
running MCMC for RR-BLUP...100%|████████████████████████| Time: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[2.679749 -2.074687 -0.096726; -2.074687 3.198285 0.811862; -0.096726 0.811862 0.837814]
Polygenic effects covariance matrix
[1.139575 0.354658 0.20412 0.247455; 0.354658 0.543663 0.231854 0.200956; 0.20412 0.231854 0.536559 0.177421; 0.247455 0.200956 0.177421 0.677377]
Marker effects covariance matrix:
[0.402227 0.169871 0.208215; 0.169871 0.345893 0.150813; 0.208215 0.150813 0.582068]
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
│ 3 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
Test single-trait GBLUP analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test multi-trait GBLUP analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Test single-trait BayesL analysis using incomplete genomic data
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000125 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.288206 seconds (167 allocations: 20.297 KiB, 99.84% gc time)
completed imputing genotypes
The prior for marker effects variance is calculated from the genetic variance and π.
The mean of the prior for the marker effects variance is: 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesL
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
random effect variances (ϵ): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
genetic variances (genomic): 1.000
marker effect variances: 0.492
π 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
Posterior means at iteration: 50
Residual variance: 2.286292
Polygenic effects covariance matrix
[3.989 1.417; 1.417 1.753]
Marker effects variance: 0.041985
Posterior means at iteration: 100
Residual variance: 4.66018
Polygenic effects covariance matrix
[2.501 0.973; 0.973 2.155]
Marker effects variance: 0.080375
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test multi-trait BayesL analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
calculating A inverse
0.000114 seconds (209 allocations: 16.156 KiB)
imputing missing genotypes
0.488951 seconds (167 allocations: 20.297 KiB, 99.93% gc time)
completed imputing genotypes
Pi (Π) is not provided.
Pi (Π) is generated assuming all markers have effects on all traits.
The prior for marker effects covariance matrix is calculated from genetic covariance matrix and Π.
The mean of the prior for the marker effects covariance matrix is:
0.492462 0.246231 0.246231
0.246231 0.492462 0.246231
0.246231 0.246231 0.492462
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
ϵ factor random 5
J covariate fixed 1
MCMC Information:
methods BayesL
incomplete genomic data
(i.e., single-step analysis)
chain_length 100
burnin 0
estimatePi false
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
random effect variances (ϵ):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
genetic variances (genomic):
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
marker effect variances:
0.492 0.246 0.246
0.246 0.492 0.246
0.246 0.246 0.492
Π: (Y(yes):included; N(no):excluded)
["y1", "y2", "y3"] probability
["Y", "Y", "N"] 0.0
["N", "N", "N"] 0.0
["Y", "N", "N"] 0.0
["N", "Y", "Y"] 0.0
["Y", "N", "Y"] 0.0
["N", "N", "Y"] 0.0
["Y", "Y", "Y"] 1.0
["N", "Y", "N"] 0.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
marker effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_marker_effects_y1.txt is created to save MCMC samples for marker_effects_y1.
The file MCMC_samples_marker_effects_y2.txt is created to save MCMC samples for marker_effects_y2.
The file MCMC_samples_marker_effects_y3.txt is created to save MCMC samples for marker_effects_y3.
The file MCMC_samples_marker_effects_variances.txt is created to save MCMC samples for marker_effects_variances.
The file MCMC_samples_pi.txt is created to save MCMC samples for pi.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_y1:J.txt is created to save MCMC samples for y1:J.
The file MCMC_samples_y2:J.txt is created to save MCMC samples for y2:J.
The file MCMC_samples_y3:J.txt is created to save MCMC samples for y3:J.
The file MCMC_samples_y1:ϵ.txt is created to save MCMC samples for y1:ϵ.
The file MCMC_samples_y2:ϵ.txt is created to save MCMC samples for y2:ϵ.
The file MCMC_samples_y3:ϵ.txt is created to save MCMC samples for y3:ϵ.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_ϵ_variances.txt is created to save MCMC samples for ϵ_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
running MCMC for BayesL... 50%|████████████▌ | ETA: 0:00:00[K
Posterior means at iteration: 50
Residual covariance matrix:
[2.853082 -1.845334 -0.117493; -1.845334 2.898062 0.759933; -0.117493 0.759933 0.696329]
Polygenic effects covariance matrix
[0.707939 0.079158 0.125929 0.103179; 0.079158 0.900454 0.350215 0.280377; 0.125929 0.350215 0.675456 0.346007; 0.103179 0.280377 0.346007 0.712964]
Marker effects covariance matrix:
[0.025254 0.013625 0.010776; 0.013625 0.025408 0.009584; 0.010776 0.009584 0.019143]
Posterior means at iteration: 100
Residual covariance matrix:
[2.378817 -1.331214 -0.328824; -1.331214 2.33669 1.041152; -0.328824 1.041152 1.049279]
Polygenic effects covariance matrix
[1.106683 -0.306542 0.369267 0.356312; -0.306542 1.695281 0.070417 0.336331; 0.369267 0.070417 0.709351 0.330329; 0.356312 0.336331 0.330329 0.860793]
Marker effects covariance matrix:
[0.02416 0.010617 0.008317; 0.010617 0.022578 0.010634; 0.008317 0.010634 0.023016]
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
running MCMC for BayesL...100%|█████████████████████████| Time: 0:00:00[K
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Compute the model frequency for each marker (the probability the marker is included in the model).
5×2 DataFrame
│ Row │ marker_ID │ modelfrequency │
│ │ Abstract… │ Float64 │
├─────┼───────────┼────────────────┤
│ 1 │ m1 │ 1.0 │
│ 2 │ m2 │ 1.0 │
│ 3 │ m3 │ 1.0 │
│ 4 │ m4 │ 1.0 │
│ 5 │ m5 │ 1.0 │
Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance.
3×10 DataFrame. Omitted printing of 3 columns
│ Row │ window │ chr │ wStart │ wEnd │ start_SNP │ end_SNP │ numSNP │
│ │ Int64 │ String │ Int64 │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼────────┼────────┼─────────┼─────────┼───────────┼─────────┼────────┤
│ 1 │ 1 │ 1 │ 0 │ 1000000 │ 16977 │ 434311 │ 2 │
│ 2 │ 2 │ 1 │ 1000000 │ 2000000 │ 1025513 │ 1025513 │ 1 │
│ 3 │ 3 │ 2 │ 0 │ 1000000 │ 70350 │ 101135 │ 2 │
Test single-trait non_genomic analysis using incomplete genomic data
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1*x3 + x2 + x3 + ID + dam
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1*x3 interaction fixed 2
x2 factor random 2
x3 factor fixed 2
ID factor random 12
dam factor random 12
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2): [1.0]
residual variances: 1.000
genetic variances (polygenic):
[1.0 0.5; 0.5 1.0]
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y1:x2.txt is created to save MCMC samples for y1:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
Posterior means at iteration: 50
Residual variance: 1.730405
Polygenic effects covariance matrix
[1.088 0.347; 0.347 1.274]
Posterior means at iteration: 100
Residual variance: 1.500123
Polygenic effects covariance matrix
[1.065 0.178; 0.178 1.193]
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
Test multi-trait non_genomic analysis using incomplete genomic data
x2 is not found in model equation 1.
dam is not found in model equation 2.
dam is not found in model equation 3.
Checking phenotypes...
Individual IDs (strings) are provided in the first column of the phenotypic data.
Phenotypes for all traits included in the model for individual a7 in the row 6 are missing. This record is deleted
A Linear Mixed Model was build using model equations:
y1 = intercept + x1 + x3 + ID + dam
y2 = intercept + x1 + x2 + x3 + ID
y3 = intercept + x1 + x1*x3 + x2 + ID
Model Information:
Term C/F F/R nLevels
intercept factor fixed 1
x1 covariate fixed 1
x3 factor fixed 2
ID factor random 12
dam factor random 12
x2 factor random 2
x1*x3 interaction fixed 2
MCMC Information:
methods conventional (no markers)
chain_length 100
burnin 0
estimateScale false
starting_value false
printout_frequency 50
output_samples_frequency 10
constraint false
missing_phenotypes true
update_priors_frequency 0
seed false
Hyper-parameters Information:
random effect variances (x2):
1.0 0.5
0.5 1.0
residual variances:
1.0 0.5 0.5
0.5 1.0 0.5
0.5 0.5 1.0
genetic variances (polygenic):
1.0 0.5 0.5 0.5
0.5 1.0 0.5 0.5
0.5 0.5 1.0 0.5
0.5 0.5 0.5 1.0
Degree of freedom for hyper-parameters:
residual variances: 4.000
iid random effect variances: 4.000
polygenic effect variances: 4.000
The file MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance.
The file MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance.
The file MCMC_samples_y2:x2.txt is created to save MCMC samples for y2:x2.
The file MCMC_samples_y3:x2.txt is created to save MCMC samples for y3:x2.
The file MCMC_samples_x2_variances.txt is created to save MCMC samples for x2_variances.
The file MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1.
The file MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2.
The file MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3.
The file MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
The file MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
Posterior means at iteration: 50
Residual covariance matrix:
[2.501105 0.989617 0.913422; 0.989617 1.090211 0.324079; 0.913422 0.324079 0.967835]
Polygenic effects covariance matrix
[0.646956 0.277462 0.1798 0.124438; 0.277462 0.76477 0.326503 0.217411; 0.1798 0.326503 1.274943 1.14033; 0.124438 0.217411 1.14033 1.940019]
running MCMC for conventional (no markers)... 75%|████▌ | ETA: 0:00:00[K
Posterior means at iteration: 100
Residual covariance matrix:
[1.906057 0.693789 0.650716; 0.693789 0.922746 0.354563; 0.650716 0.354563 0.845572]
Polygenic effects covariance matrix
[1.156128 0.090719 0.504783 0.512365; 0.090719 0.761365 0.124682 0.115049; 0.504783 0.124682 1.210126 0.94246; 0.512365 0.115049 0.94246 1.499892]
running MCMC for conventional (no markers)...100%|██████| Time: 0:00:00[K
The version of Julia and Platform in use:
Julia Version 1.3.1-pre.11
Commit 7704df0a5a (2019-12-02 13:23 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Xeon(R) Silver 4114 CPU @ 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-6.0.1 (ORCJIT, skylake)
Environment:
JULIA_LOAD_PATH = @:/tmp/jl_T1LX2I
The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
load genotype ...
from a text file with a header (marker IDs)
The delimiter in genotypes.txt is ','.
The header (marker IDs) is provided in genotypes.txt.
5 markers on 7 individuals were added.
from a text file without a header (no marker IDs)
The delimiter in genotypes_noheader.txt is ','.
The header (marker IDs) is not provided in genotypes_noheader.txt.
5 markers on 7 individuals were added.
from an Array (matrix) with marker IDs and individual IDs provided
5 markers on 7 individuals were added.
from an Array (matrix) without marker IDs and with individual IDs provided
The header (marker IDs) is set to 1,2,...,#markers
5 markers on 7 individuals were added.
from an Array (matrix) without marker IDs and individual IDs
The individual IDs is set to 1,2,...,#observations
The header (marker IDs) is set to 1,2,...,#markers
5 markers on 7 individuals were added.
from a DataFrame with marker IDs and individual IDs provided
5 markers on 7 individuals were added.
Testing JWAS tests passed