Package evaluation of JWAS on Julia 1.11.4 (a71dd056e0*) started at 2025-04-08T15:29:43.980 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Set-up completed after 9.18s ################################################################################ # Installation # Installing JWAS... Resolving package versions... Updating `~/.julia/environments/v1.11/Project.toml` [c9a035f4] + JWAS v1.2.1 Updating `~/.julia/environments/v1.11/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [336ed68f] + CSV v0.10.15 [944b1d66] + CodecZlib v0.7.8 [bbf7d656] + CommonSubexpressions v0.3.1 [34da2185] + Compat v4.16.0 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.7.0 [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [163ba53b] + DiffResults v1.1.0 [b552c78f] + DiffRules v1.15.1 [31c24e10] + Distributions v0.25.118 [ffbed154] + DocStringExtensions v0.9.4 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.13.0 ⌅ [f6369f11] + ForwardDiff v0.10.38 [34004b35] + HypergeometricFunctions v0.3.28 [842dd82b] + InlineStrings v1.4.3 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.4 [82899510] + IteratorInterfaceExtensions v1.0.0 [692b3bcd] + JLLWrappers v1.7.0 [c9a035f4] + JWAS v1.2.1 [b964fa9f] + LaTeXStrings v1.4.0 [2ab3a3ac] + LogExpFunctions v0.3.29 [1914dd2f] + MacroTools v0.5.15 [e1d29d7a] + Missings v1.2.0 [77ba4419] + NaNMath v1.1.3 [bac558e1] + OrderedCollections v1.8.0 [90014a1f] + PDMats v0.11.33 [69de0a69] + Parsers v2.8.1 [2dfb63ee] + PooledArrays v1.4.3 ⌅ [aea7be01] + PrecompileTools v1.2.1 [21216c6a] + Preferences v1.4.3 [08abe8d2] + PrettyTables v2.4.0 [92933f4c] + ProgressMeter v1.10.4 [43287f4e] + PtrArrays v1.3.0 [1fd47b50] + QuadGK v2.11.2 [189a3867] + Reexport v1.2.2 [79098fc4] + Rmath v0.8.0 [91c51154] + SentinelArrays v1.4.8 [a2af1166] + SortingAlgorithms v1.2.1 [276daf66] + SpecialFunctions v2.5.0 [1e83bf80] + StaticArraysCore v1.4.3 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.7.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.4.0 [892a3eda] + StringManipulation v0.4.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.0 [3bb67fe8] + TranscodingStreams v0.11.3 [ea10d353] + WeakRefStrings v1.4.2 [76eceee3] + WorkerUtilities v1.6.1 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [f50d1b31] + Rmath_jll v0.5.1+0 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.11.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [de0858da] + Printf v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v0.7.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.11.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0 [4536629a] + OpenBLAS_jll v0.3.27+1 [05823500] + OpenLibm_jll v0.8.5+0 [bea87d4a] + SuiteSparse_jll v7.7.0+0 [83775a58] + Zlib_jll v1.2.13+1 [8e850b90] + libblastrampoline_jll v5.11.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m` Installation completed after 4.58s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompilation completed after 53.57s ################################################################################ # Testing # Testing JWAS Status `/tmp/jl_WJtPd9/Project.toml` [336ed68f] CSV v0.10.15 [a93c6f00] DataFrames v1.7.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.118 ⌅ [f6369f11] ForwardDiff v0.10.38 [c9a035f4] JWAS v1.2.1 [92933f4c] ProgressMeter v1.10.4 ⌅ [2913bbd2] StatsBase v0.33.21 [b77e0a4c] InteractiveUtils v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.11.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_WJtPd9/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [336ed68f] CSV v0.10.15 [944b1d66] CodecZlib v0.7.8 [bbf7d656] CommonSubexpressions v0.3.1 [34da2185] Compat v4.16.0 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.7.0 [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [163ba53b] DiffResults v1.1.0 [b552c78f] DiffRules v1.15.1 [31c24e10] Distributions v0.25.118 [ffbed154] DocStringExtensions v0.9.4 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.13.0 ⌅ [f6369f11] ForwardDiff v0.10.38 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.3 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.4 [82899510] IteratorInterfaceExtensions v1.0.0 [692b3bcd] JLLWrappers v1.7.0 [c9a035f4] JWAS v1.2.1 [b964fa9f] LaTeXStrings v1.4.0 [2ab3a3ac] LogExpFunctions v0.3.29 [1914dd2f] MacroTools v0.5.15 [e1d29d7a] Missings v1.2.0 [77ba4419] NaNMath v1.1.3 [bac558e1] OrderedCollections v1.8.0 [90014a1f] PDMats v0.11.33 [69de0a69] Parsers v2.8.1 [2dfb63ee] PooledArrays v1.4.3 ⌅ [aea7be01] PrecompileTools v1.2.1 [21216c6a] Preferences v1.4.3 [08abe8d2] PrettyTables v2.4.0 [92933f4c] ProgressMeter v1.10.4 [43287f4e] PtrArrays v1.3.0 [1fd47b50] QuadGK v2.11.2 [189a3867] Reexport v1.2.2 [79098fc4] Rmath v0.8.0 [91c51154] SentinelArrays v1.4.8 [a2af1166] SortingAlgorithms v1.2.1 [276daf66] SpecialFunctions v2.5.0 [1e83bf80] StaticArraysCore v1.4.3 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.7.0 ⌅ [2913bbd2] StatsBase v0.33.21 [4c63d2b9] StatsFuns v1.4.0 [892a3eda] StringManipulation v0.4.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.0 [3bb67fe8] TranscodingStreams v0.11.3 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [efe28fd5] OpenSpecFun_jll v0.5.6+0 [f50d1b31] Rmath_jll v0.5.1+0 [56f22d72] Artifacts v1.11.0 [2a0f44e3] Base64 v1.11.0 [ade2ca70] Dates v1.11.0 [8ba89e20] Distributed v1.11.0 [9fa8497b] Future v1.11.0 [b77e0a4c] InteractiveUtils v1.11.0 [8f399da3] Libdl v1.11.0 [37e2e46d] LinearAlgebra v1.11.0 [56ddb016] Logging v1.11.0 [d6f4376e] Markdown v1.11.0 [a63ad114] Mmap v1.11.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [ea8e919c] SHA v0.7.0 [9e88b42a] Serialization v1.11.0 [6462fe0b] Sockets v1.11.0 [2f01184e] SparseArrays v1.11.0 [4607b0f0] SuiteSparse [fa267f1f] TOML v1.0.3 [8dfed614] Test v1.11.0 [cf7118a7] UUIDs v1.11.0 [4ec0a83e] Unicode v1.11.0 [e66e0078] CompilerSupportLibraries_jll v1.1.1+0 [4536629a] OpenBLAS_jll v0.3.27+1 [05823500] OpenLibm_jll v0.8.5+0 [bea87d4a] SuiteSparse_jll v7.7.0+0 [83775a58] Zlib_jll v1.2.13+1 [8e850b90] libblastrampoline_jll v5.11.0+0 Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. Testing Running tests... Precompiling DistributionsTestExt... 4074.4 ms ✓ Distributions → DistributionsTestExt 1 dependency successfully precompiled in 6 seconds. 48 already precompiled. ┌ Warning: `missingstrings` keyword argument is deprecated; pass a `Vector{String}` to `missingstring` instead └ @ CSV ~/.julia/packages/CSV/XLcqT/src/context.jl:344 ┌ Warning: `missingstrings` keyword argument is deprecated; pass a `Vector{String}` to `missingstring` instead └ @ CSV ~/.julia/packages/CSV/XLcqT/src/context.jl:344 The delimiter in pedigree.txt is ','. ┌ Warning: `missingstrings` keyword argument is deprecated; pass a `Vector{String}` to `missingstring` instead └ @ CSV ~/.julia/packages/CSV/XLcqT/src/context.jl:344 ┌ Warning: `Progress(n::Integer, desc::AbstractString, offset::Integer = 0; kwargs...)` is deprecated, use `Progress(n; desc = desc, offset = offset, kwargs...)` instead. │ caller = ip:0x0 └ @ Core :-1 coding pedigree... 17%|█████▍ | ETA: 0:00:01 coding pedigree... 100%|████████████████████████████████| Time: 0:00:01 calculating inbreeding... 17%|████▏ | ETA: 0:00:03 calculating inbreeding... 100%|█████████████████████████| Time: 0:00:00 Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 Test single-trait BayesA analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0] random effect variances (y1:x2): [1.0;;] genetic variances (polygenic): 1.0 0.5 0.5 1.0 residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesB genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi false estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 random effect variances: 5.000 polygenic effect variances: 6.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2. The file results/MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances. The file results/MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:11:32 running MCMC ... 12%|████▎ | ETA: 0:02:55 Posterior means at iteration: 50 Residual variance: 1.109058 Posterior means at iteration: 100 Residual variance: 1.09171 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:24 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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. running GWAS... 20%|███████▎ | ETA: 0:00:01 running GWAS... 100%|████████████████████████████████████| Time: 0:00:00 (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.353403 0.368881 71.988 1.0 1.0 2 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.0697742 0.0544345 27.9653 1.0 1.0 3 │ 1 3 2 0 1000000 70350 101135 2 0.25941 0.305322 63.5827 1.0 1.0,) Test multi-trait BayesA analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 dam is not found in model equation 2. dam is not found in model equation 3. x2 is not found in model equation 1. The folder results is created to save results. BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID", "y3:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno y2 = intercept + x1 + x2 + x3 + ID + geno y3 = intercept + x1 + x1*x3 + x2 + ID + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y2:ID,y3:ID,y1:dam): 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 random effect variances (y2:x2,y3:x2): 1.0 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 residual variances: 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesB 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 ["N", "Y", "N"] 0.0 ["Y", "N", "Y"] 0.0 ["N", "N", "Y"] 0.0 ["Y", "N", "N"] 0.0 ["N", "N", "N"] 0.0 ["Y", "Y", "Y"] 1.0 ["N", "Y", "Y"] 0.0 ["Y", "Y", "N"] 0.0 estimatePi false estimateScale false Degree of freedom for hyper-parameters: residual variances: 7.000 random effect variances: 6.000 polygenic effect variances: 8.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file results/MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2. The file results/MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2. The file results/MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances. The file results/MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file results/MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:28:06 running MCMC ... 12%|████▎ | ETA: 0:05:11 Posterior means at iteration: 50 Residual variance: [1.531609 1.072613 0.739463; 1.072613 1.62629 0.77873; 0.739463 0.77873 1.05516] running MCMC ... 54%|██████████████████▉ | ETA: 0:00:39 Posterior means at iteration: 100 Residual variance: [1.093952 0.601335 0.543641; 0.601335 1.137905 0.578; 0.543641 0.578 0.966667] running MCMC ... 100%|███████████████████████████████████| Time: 0:00:45 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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. calculating genomic correlation... 20%|███▍ | ETA: 0:00:01 calculating genomic correlation... 100%|█████████████████| Time: 0:00:00 (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.156565 0.107722 20.5954 1.0 1.0 2 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.331399 0.503002 32.3935 1.0 1.0 3 │ 1 3 2 0 1000000 70350 101135 2 0.296789 0.240672 38.0357 1.0 1.0, 3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 2 1 1 0 1000000 16977 434311 2 0.168922 0.148255 24.8206 1.0 1.0 2 │ 2 2 1 1000000 2000000 1025513 1025513 1 0.368751 0.611053 35.9106 1.0 1.0 3 │ 2 3 2 0 1000000 70350 101135 2 0.386726 0.563776 52.8746 1.0 1.0, 3×12 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimate_cov std_cov estimate_cor std_cor │ String Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 ─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ cor(t1,t2) 1 1 0 1000000 16977 434311 2 -0.0204613 0.0637793 -0.172505 0.614412 2 │ cor(t1,t2) 2 1 1000000 2000000 1025513 1025513 1 0.100308 0.615294 -0.2 1.0328 3 │ cor(t1,t2) 3 2 0 1000000 70350 101135 2 0.170598 0.282409 0.503782 0.615214) Test single-trait BayesB analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0] random effect variances (y1:x2): [1.0;;] genetic variances (polygenic): 1.0 0.5 0.5 1.0 residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesB genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 random effect variances: 5.000 polygenic effect variances: 6.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2. The file results/MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances. The file results/MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:06 Posterior means at iteration: 50 Residual variance: 4.086951 Posterior means at iteration: 100 Residual variance: 2.712812 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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 0.6 3 │ m3 0.3 4 │ m4 0.6 5 │ m5 0.3 Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.531993 1.64969 38.1406 0.6 0.6 2 │ 1 3 2 0 1000000 70350 101135 2 0.168178 0.216715 40.6597 0.6 0.6 3 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.0312007 0.0627608 25.4379 0.3 0.5,) Test multi-trait BayesB analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 dam is not found in model equation 2. dam is not found in model equation 3. x2 is not found in model equation 1. The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID", "y3:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno y2 = intercept + x1 + x2 + x3 + ID + geno y3 = intercept + x1 + x1*x3 + x2 + ID + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y2:ID,y3:ID,y1:dam): 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 random effect variances (y2:x2,y3:x2): 1.0 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 residual variances: 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesB 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 ["N", "Y", "N"] 0.0 ["Y", "N", "Y"] 0.0 ["N", "N", "Y"] 0.0 ["Y", "N", "N"] 0.0 ["N", "N", "N"] 0.0 ["Y", "Y", "Y"] 1.0 ["N", "Y", "Y"] 0.0 ["Y", "Y", "N"] 0.0 estimatePi true estimateScale false Degree of freedom for hyper-parameters: residual variances: 7.000 random effect variances: 6.000 polygenic effect variances: 8.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file results/MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2. The file results/MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2. The file results/MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances. The file results/MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file results/MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:03:55 Posterior means at iteration: 50 Residual variance: [1.174577 0.044194 0.333704; 0.044194 1.335753 0.360702; 0.333704 0.360702 0.612548] running MCMC ... 59%|████████████████████▋ | ETA: 0:00:03 Posterior means at iteration: 100 Residual variance: [1.775734 -0.526918 -0.426087; -0.526918 2.218547 0.844683; -0.426087 0.844683 1.218908] running MCMC ... 100%|███████████████████████████████████| Time: 0:00:04 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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 0.5 4 │ m4 0.5 5 │ m5 0.3 Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.485979 0.444752 80.8641 1.0 1.0 2 │ 1 3 2 0 1000000 70350 101135 2 0.136526 0.292079 22.9807 0.6 0.8 3 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.127276 0.210942 18.1571 0.5 0.7, 3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 2 3 2 0 1000000 70350 101135 2 0.252982 0.434966 26.0976 0.8 0.8 2 │ 2 2 1 1000000 2000000 1025513 1025513 1 0.276526 0.552833 24.4425 0.7 0.75 3 │ 2 1 1 0 1000000 16977 434311 2 0.120492 0.252207 28.6377 0.5 0.666667, 3×12 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimate_cov std_cov estimate_cor std_cor │ String Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 ─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ cor(t1,t2) 1 1 0 1000000 16977 434311 2 0.0933545 0.310779 0.0457405 0.569845 2 │ cor(t1,t2) 2 1 1000000 2000000 1025513 1025513 1 0.0198024 0.0479985 0.2 0.632456 3 │ cor(t1,t2) 3 2 0 1000000 70350 101135 2 0.0959153 0.223719 0.39139 0.461206) Test single-trait BayesC analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0] random effect variances (y1:x2): [1.0;;] genetic variances (polygenic): 1.0 0.5 0.5 1.0 residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 random effect variances: 5.000 polygenic effect variances: 6.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2. The file results/MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances. The file results/MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:10 Posterior means at iteration: 50 Residual variance: 0.405394 Posterior means at iteration: 100 Residual variance: 1.093501 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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 0.2 2 │ m2 0.2 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. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 3 2 0 1000000 70350 101135 2 0.149836 0.176922 33.3174 0.8 0.8 2 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.433821 1.00958 50.1307 0.7 0.75 3 │ 1 1 1 0 1000000 16977 434311 2 0.103351 0.220103 4.63025 0.3 0.6,) Test multi-trait BayesC analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 dam is not found in model equation 2. dam is not found in model equation 3. x2 is not found in model equation 1. The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID", "y3:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno y2 = intercept + x1 + x2 + x3 + ID + geno y3 = intercept + x1 + x1*x3 + x2 + ID + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y2:ID,y3:ID,y1:dam): 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 random effect variances (y2:x2,y3:x2): 1.0 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 residual variances: 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC 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 ["N", "Y", "N"] 0.0 ["Y", "N", "Y"] 0.0 ["N", "N", "Y"] 0.0 ["Y", "N", "N"] 0.0 ["N", "N", "N"] 0.0 ["Y", "Y", "Y"] 1.0 ["N", "Y", "Y"] 0.0 ["Y", "Y", "N"] 0.0 estimatePi true estimateScale false Degree of freedom for hyper-parameters: residual variances: 7.000 random effect variances: 6.000 polygenic effect variances: 8.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file results/MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2. The file results/MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2. The file results/MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances. The file results/MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file results/MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:34 Posterior means at iteration: 50 Residual variance: [0.770702 0.233256 0.416104; 0.233256 1.050358 0.689436; 0.416104 0.689436 1.184378] running MCMC ... 78%|███████████████████████████▎ | ETA: 0:00:00 Posterior means at iteration: 100 Residual variance: [0.627043 0.235608 0.204438; 0.235608 0.826529 0.440732; 0.204438 0.440732 0.976718] running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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 0.8 3 │ m3 0.8 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. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.524886 0.718302 38.3374 0.9 0.9 2 │ 1 3 2 0 1000000 70350 101135 2 0.396442 0.523022 29.346 0.9 0.9 3 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.230072 0.422658 26.1067 0.7 0.833333, 3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 2 3 2 0 1000000 70350 101135 2 0.109564 0.168313 55.6893 0.8 0.8 2 │ 2 1 1 0 1000000 16977 434311 2 0.111669 0.237111 14.8312 0.5 0.65 3 │ 2 2 1 1000000 2000000 1025513 1025513 1 0.0580091 0.150263 4.26334 0.2 0.5, 3×12 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimate_cov std_cov estimate_cor std_cor │ String Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 ─────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ cor(t1,t2) 1 1 0 1000000 16977 434311 2 -0.00654897 0.0594534 -0.169446 0.629982 2 │ cor(t1,t2) 2 1 1000000 2000000 1025513 1025513 1 -0.0379572 0.120031 -0.1 0.316228 3 │ cor(t1,t2) 3 2 0 1000000 70350 101135 2 0.058536 0.245463 0.309681 0.684662) Test single-trait RR-BLUP analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0] random effect variances (y1:x2): [1.0;;] genetic variances (polygenic): 1.0 0.5 0.5 1.0 residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method RR-BLUP genetic variances (genomic): 1.000 marker effect variances: 0.492 estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 random effect variances: 5.000 polygenic effect variances: 6.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2. The file results/MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances. The file results/MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:28 Posterior means at iteration: 50 Residual variance: 0.930879 Posterior means at iteration: 100 Residual variance: 0.734607 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.220819 0.212303 69.4887 1.0 1.0 2 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.203601 0.454182 37.8932 1.0 1.0 3 │ 1 3 2 0 1000000 70350 101135 2 0.341207 0.410364 93.8369 1.0 1.0,) Test multi-trait RR-BLUP analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 dam is not found in model equation 2. dam is not found in model equation 3. x2 is not found in model equation 1. The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID", "y3:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno y2 = intercept + x1 + x2 + x3 + ID + geno y3 = intercept + x1 + x1*x3 + x2 + ID + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y2:ID,y3:ID,y1:dam): 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 random effect variances (y2:x2,y3:x2): 1.0 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 residual variances: 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method RR-BLUP 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 estimateScale false Degree of freedom for hyper-parameters: residual variances: 7.000 random effect variances: 6.000 polygenic effect variances: 8.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file results/MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2. The file results/MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2. The file results/MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances. The file results/MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file results/MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:03:32 Posterior means at iteration: 50 Residual variance: [0.966417 0.570692 0.525714; 0.570692 0.776767 0.391683; 0.525714 0.391683 0.817974] Posterior means at iteration: 100 Residual variance: [1.026154 0.589773 0.589093; 0.589773 0.926218 0.468718; 0.589093 0.468718 0.784849] running MCMC ... 100%|███████████████████████████████████| Time: 0:00:04 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.880025 0.825477 57.504 1.0 1.0 2 │ 1 3 2 0 1000000 70350 101135 2 0.830762 1.15877 47.8401 1.0 1.0 3 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.207563 0.383173 15.2774 0.9 0.966667, 3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 2 1 1 0 1000000 16977 434311 2 0.632649 0.730781 40.0139 1.0 1.0 2 │ 2 2 1 1000000 2000000 1025513 1025513 1 0.149305 0.100576 17.6719 1.0 1.0 3 │ 2 3 2 0 1000000 70350 101135 2 0.541355 0.644343 34.2097 1.0 1.0, 3×12 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimate_cov std_cov estimate_cor std_cor │ String Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ cor(t1,t2) 1 1 0 1000000 16977 434311 2 0.205828 0.882622 -0.00785375 0.868404 2 │ cor(t1,t2) 2 1 1000000 2000000 1025513 1025513 1 0.0327587 0.213163 0.0 1.05409 3 │ cor(t1,t2) 3 2 0 1000000 70350 101135 2 -0.0477576 0.885681 -0.0325561 0.780168) Test single-trait BayesL analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0] random effect variances (y1:x2): [1.0;;] genetic variances (polygenic): 1.0 0.5 0.5 1.0 residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesL genetic variances (genomic): 1.000 marker effect variances: 0.492 estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 random effect variances: 5.000 polygenic effect variances: 6.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2. The file results/MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances. The file results/MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:02:40 Posterior means at iteration: 50 Residual variance: 0.762223 Posterior means at iteration: 100 Residual variance: 1.873038 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.184694 0.243812 47.596 1.0 1.0 2 │ 1 3 2 0 1000000 70350 101135 2 0.197671 0.285639 31.3918 1.0 1.0 3 │ 1 1 1 0 1000000 16977 434311 2 0.178996 0.169575 36.3835 0.9 0.966667,) Test multi-trait BayesL analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 dam is not found in model equation 2. dam is not found in model equation 3. x2 is not found in model equation 1. The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID", "y3:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. 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 + geno y2 = intercept + x1 + x2 + x3 + ID + geno y3 = intercept + x1 + x1*x3 + x2 + ID + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y2:ID,y3:ID,y1:dam): 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 random effect variances (y2:x2,y3:x2): 1.0 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 residual variances: 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesL 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 estimateScale false Degree of freedom for hyper-parameters: residual variances: 7.000 random effect variances: 6.000 polygenic effect variances: 8.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file results/MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2. The file results/MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2. The file results/MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances. The file results/MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file results/MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:02:18 Posterior means at iteration: 50 Residual variance: [0.77451 0.208713 0.472178; 0.208713 0.807151 0.290504; 0.472178 0.290504 0.884672] Posterior means at iteration: 100 Residual variance: [0.755013 0.211843 0.465702; 0.211843 0.821132 0.279537; 0.465702 0.279537 0.841926] running MCMC ... 100%|███████████████████████████████████| Time: 0:00:02 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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. (3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 1 1 1 0 1000000 16977 434311 2 0.221678 0.183897 100.814 1.0 1.0 2 │ 1 2 1 1000000 2000000 1025513 1025513 1 0.244062 0.624542 27.4291 1.0 1.0 3 │ 1 3 2 0 1000000 70350 101135 2 0.219635 0.449982 42.3198 1.0 1.0, 3×13 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimateGenVar stdGenVar prGenVar WPPA PPA_t │ Int64 Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 ─────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ 2 1 1 0 1000000 16977 434311 2 0.394495 0.429676 51.9947 1.0 1.0 2 │ 2 2 1 1000000 2000000 1025513 1025513 1 0.367579 0.723552 28.5874 1.0 1.0 3 │ 2 3 2 0 1000000 70350 101135 2 0.463753 0.701009 54.7994 0.9 0.966667, 3×12 DataFrame Row │ trait window chr wStart wEnd start_SNP end_SNP numSNP estimate_cov std_cov estimate_cor std_cor │ String Int64 String Int64 Int64 Int64 Int64 Int64 Float64 Float64 Float64 Float64 ─────┼────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 │ cor(t1,t2) 1 1 0 1000000 16977 434311 2 0.141646 0.269631 0.205593 0.799113 2 │ cor(t1,t2) 2 1 1000000 2000000 1025513 1025513 1 0.19924 0.678271 0.2 1.0328 3 │ cor(t1,t2) 3 2 0 1000000 70350 101135 2 0.232613 0.467627 0.108955 0.815165) Test single-trait GBLUP analysis using complete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. A genomic relationship matrix is computed from genotypes. Genotype informatin: #markers: 0; #individuals: 7 The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept + x1*x3 + x2 + x3 + ID + dam + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y1:dam): [1.0 0.5; 0.5 1.0] random effect variances (y1:x2): [1.0;;] genetic variances (polygenic): 1.0 0.5 0.5 1.0 residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method GBLUP genetic variances (genomic): 1.000 estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 random effect variances: 5.000 polygenic effect variances: 6.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y1.x2.txt is created to save MCMC samples for y1:x2. The file results/MCMC_samples_y1.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y1:dam_variances. The file results/MCMC_samples_y1.x2_variances.txt is created to save MCMC samples for y1:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:03:15 Posterior means at iteration: 50 Residual variance: 0.977778 Posterior means at iteration: 100 Residual variance: 1.69798 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 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 The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. A genomic relationship matrix is computed from genotypes. Genotype informatin: #markers: 0; #individuals: 7 dam is not found in model equation 2. dam is not found in model equation 3. x2 is not found in model equation 1. The folder results is created to save results. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. In this complete genomic data (non-single-step) analyis, 1 phenotyped individuals are not genotyped. These are removed from the analysis. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y2:ID", "y3:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. Pi (Π) is not provided. Pi (Π) is generated assuming all markers have effects on all traits. A Linear Mixed Model was build using model equations: y1 = intercept + x1 + x3 + ID + dam + geno y2 = intercept + x1 + x2 + x3 + ID + geno y3 = intercept + x1 + x1*x3 + x2 + ID + geno 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: chain_length 100 burnin 0 starting_value true printout_frequency 50 output_samples_frequency 10 constraint false missing_phenotypes true update_priors_frequency 0 seed 314 Hyper-parameters Information: random effect variances (y1:ID,y2:ID,y3:ID,y1:dam): 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 random effect variances (y2:x2,y3:x2): 1.0 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 residual variances: 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 0.5f0 0.5f0 0.5f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method GBLUP genetic variances (genomic): 1.0 0.5 0.5 0.5 1.0 0.5 0.5 0.5 1.0 estimateScale false Degree of freedom for hyper-parameters: residual variances: 7.000 random effect variances: 6.000 polygenic effect variances: 8.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file results/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file results/MCMC_samples_marker_effects_geno_y3.txt is created to save MCMC samples for marker_effects_geno_y3. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y2.x2.txt is created to save MCMC samples for y2:x2. The file results/MCMC_samples_y3.x2.txt is created to save MCMC samples for y3:x2. The file results/MCMC_samples_y1.ID_y2.ID_y3.ID_y1.dam_variances.txt is created to save MCMC samples for y1:ID_y2:ID_y3:ID_y1:dam_variances. The file results/MCMC_samples_y2.x2_y3.x2_variances.txt is created to save MCMC samples for y2:x2_y3:x2_variances. The file results/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file results/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file results/MCMC_samples_EBV_y3.txt is created to save MCMC samples for EBV_y3. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:04:24 Posterior means at iteration: 50 Residual variance: [1.042879 0.750675 0.764753; 0.750675 1.237848 0.918957; 0.764753 0.918957 1.26299] Posterior means at iteration: 100 Residual variance: [1.055249 0.392191 0.407232; 0.392191 1.187341 0.705382; 0.407232 0.705382 1.02497] running MCMC ... 100%|███████████████████████████████████| Time: 0:00:05 The version of Julia and Platform in use: Julia Version 1.11.4 Commit a71dd056e0* (2025-04-07 13:42 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-16.0.6 (ORCJIT, znver2) Threads: 1 default, 0 interactive, 1 GC (on 1 virtual cores) Environment: JULIA_CPU_THREADS = 1 JULIA_NUM_PRECOMPILE_TASKS = 1 JULIA_PKG_PRECOMPILE_AUTO = 0 JULIA_PKGEVAL = true JULIA_DEPOT_PATH = /home/pkgeval/.julia:/usr/local/share/julia: JULIA_NUM_THREADS = 1 JULIA_LOAD_PATH = @:/tmp/jl_WJtPd9 The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Test single-trait BayesA analysis using incomplete genomic data The delimiterd in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Missing values (9.0) are replaced by column means. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs. Checking pedigree... Checking genotypes... Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. Predicted values for individuals of interest will be obtained as the summation of Any["y1:ID", "y1:dam"] (Note that genomic data is always included for now).Phenotypes for 8 observations are used in the analysis.These individual IDs are saved in the file IDs_for_individuals_with_phenotypes.txt. calculating A inverse 0.801927 seconds (89.40 k allocations: 4.447 MiB, 99.92% compilation time) imputing missing genotypes 8.221550 seconds (2.30 M allocations: 120.237 MiB, 5.89% gc time, 91.40% compilation time) completed imputing genotypes ERROR: LoadError: ArgumentError: string too large (7) to convert to String3 Stacktrace: [1] stringtoolong(T::Type, n::Int64) @ InlineStrings ~/.julia/packages/InlineStrings/gXrCa/src/InlineStrings.jl:268 [2] String3 @ ~/.julia/packages/InlineStrings/gXrCa/src/InlineStrings.jl:191 [inlined] [3] convert @ ./strings/basic.jl:232 [inlined] [4] fill! @ ./multidimensional.jl:1142 [inlined] [5] copyto! @ ./broadcast.jl:933 [inlined] [6] materialize! @ ./broadcast.jl:883 [inlined] [7] materialize!(dest::SubArray{String3, 1, Vector{String3}, Tuple{Vector{Int64}}, false}, bc::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{0}, Nothing, typeof(identity), Tuple{Base.RefValue{String}}}) @ Base.Broadcast ./broadcast.jl:880 [8] SSBRrun(mme::JWAS.MME, df_whole::DataFrame, train_index::Vector{Int64}, big_memory::Bool) @ JWAS ~/.julia/packages/JWAS/YSMFw/src/1.JWAS/src/single_step/SSBR.jl:26 [9] runMCMC(mme::JWAS.MME, df::DataFrame; heterogeneous_residuals::Bool, chain_length::Int64, starting_value::Bool, burnin::Int64, output_samples_frequency::Int64, update_priors_frequency::Int64, estimate_variance::Bool, single_step_analysis::Bool, pedigree::JWAS.PedModule.Pedigree, fitting_J_vector::Bool, causal_structure::Bool, mega_trait::Bool, missing_phenotypes::Bool, constraint::Bool, RRM::Bool, outputEBV::Bool, output_heritability::Bool, prediction_equation::Bool, seed::Int64, printout_model_info::Bool, printout_frequency::Int64, big_memory::Bool, double_precision::Bool, output_folder::String, output_samples_for_all_parameters::Bool, methods::String, Pi::Float64, estimatePi::Bool, estimateScale::Bool, categorical_trait::Bool, censored_trait::Bool) @ JWAS ~/.julia/packages/JWAS/YSMFw/src/1.JWAS/src/JWAS.jl:296 [10] top-level scope @ ~/.julia/packages/JWAS/YSMFw/test/test_BayesianAlphabet.jl:59 [11] include(fname::String) @ Main ./sysimg.jl:38 [12] top-level scope @ ~/.julia/packages/JWAS/YSMFw/test/runtests.jl:3 [13] include(fname::String) @ Main ./sysimg.jl:38 [14] top-level scope @ none:6 in expression starting at /home/pkgeval/.julia/packages/JWAS/YSMFw/test/test_BayesianAlphabet.jl:19 in expression starting at /home/pkgeval/.julia/packages/JWAS/YSMFw/test/runtests.jl:3 Testing failed after 464.39s ERROR: LoadError: Package JWAS errored during testing Stacktrace: [1] pkgerror(msg::String) @ Pkg.Types /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Types.jl:68 [2] test(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}; coverage::Bool, julia_args::Cmd, test_args::Cmd, test_fn::Nothing, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool) @ Pkg.Operations /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2124 [3] test @ /opt/julia/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2007 [inlined] [4] test(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}; coverage::Bool, test_fn::Nothing, julia_args::Cmd, test_args::Cmd, force_latest_compatible_version::Bool, allow_earlier_backwards_compatible_versions::Bool, allow_reresolve::Bool, kwargs::@Kwargs{io::IOContext{IO}}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:481 [5] test(pkgs::Vector{Pkg.Types.PackageSpec}; io::IOContext{IO}, kwargs::@Kwargs{julia_args::Cmd}) @ Pkg.API /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:159 [6] test @ /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:147 [inlined] [7] #test#74 @ /opt/julia/share/julia/stdlib/v1.11/Pkg/src/API.jl:146 [inlined] [8] top-level scope @ /PkgEval.jl/scripts/evaluate.jl:219 in expression starting at /PkgEval.jl/scripts/evaluate.jl:210 PkgEval failed after 550.54s: package tests unexpectedly errored