Package evaluation to test JWAS on Julia 1.14.0-DEV.1918 (78a0dc1151*) started at 2026-03-20T11:34:54.664 ################################################################################ # Set-up # Installing PkgEval dependencies (TestEnv)... Activating project at `~/.julia/environments/v1.14` Set-up completed after 11.35s ################################################################################ # Installation # Installing JWAS... Resolving package versions... Installed JWAS ─ v2.2.0 Updating `~/.julia/environments/v1.14/Project.toml` [c9a035f4] + JWAS v2.2.0 Updating `~/.julia/environments/v1.14/Manifest.toml` [66dad0bd] + AliasTables v1.1.3 [d1d4a3ce] + BitFlags v0.1.9 [336ed68f] + CSV v0.10.16 [da1fd8a2] + CodeTracking v3.0.0 [944b1d66] + CodecZlib v0.7.8 [35d6a980] + ColorSchemes v3.31.0 [3da002f7] + ColorTypes v0.12.1 [c3611d14] + ColorVectorSpace v0.11.0 [5ae59095] + Colors v0.13.1 [34da2185] + Compat v4.18.1 [807dbc54] + Compiler v0.1.1 [f0e56b4a] + ConcurrentUtilities v2.5.1 [d38c429a] + Contour v0.6.3 [a8cc5b0e] + Crayons v4.1.1 [9a962f9c] + DataAPI v1.16.0 [a93c6f00] + DataFrames v1.8.1 ⌅ [864edb3b] + DataStructures v0.18.22 [e2d170a0] + DataValueInterfaces v1.0.0 [8bb1440f] + DelimitedFiles v1.9.1 [31c24e10] + Distributions v0.25.123 [ffbed154] + DocStringExtensions v0.9.5 [460bff9d] + ExceptionUnwrapping v0.1.11 [c87230d0] + FFMPEG v0.4.5 [48062228] + FilePathsBase v0.9.24 [1a297f60] + FillArrays v1.16.0 [53c48c17] + FixedPointNumbers v0.8.5 [1fa38f19] + Format v1.3.7 [28b8d3ca] + GR v0.73.24 [92c85e6c] + GSL v1.0.1 [42e2da0e] + Grisu v1.0.2 [cd3eb016] + HTTP v1.11.0 [34004b35] + HypergeometricFunctions v0.3.28 [842dd82b] + InlineStrings v1.4.5 [41ab1584] + InvertedIndices v1.3.1 [92d709cd] + IrrationalConstants v0.2.6 [82899510] + IteratorInterfaceExtensions v1.0.0 [1019f520] + JLFzf v0.1.11 [692b3bcd] + JLLWrappers v1.7.1 [682c06a0] + JSON v1.4.0 [c9a035f4] + JWAS v2.2.0 [aa1ae85d] + JuliaInterpreter v0.10.11 [2c470bb0] + Kronecker v0.5.5 [b964fa9f] + LaTeXStrings v1.4.0 [23fbe1c1] + Latexify v0.16.10 [2ab3a3ac] + LogExpFunctions v0.3.29 [e6f89c97] + LoggingExtras v1.2.0 [6f1432cf] + LoweredCodeUtils v3.5.1 [1914dd2f] + MacroTools v0.5.16 [739be429] + MbedTLS v1.1.10 [442fdcdd] + Measures v0.3.3 [e1d29d7a] + Missings v1.2.0 [77ba4419] + NaNMath v1.1.3 [356022a1] + NamedDims v1.2.3 [4d8831e6] + OpenSSL v1.6.1 [bac558e1] + OrderedCollections v1.8.1 ⌃ [90014a1f] + PDMats v0.11.35 [69de0a69] + Parsers v2.8.3 [ccf2f8ad] + PlotThemes v3.3.0 [995b91a9] + PlotUtils v1.4.4 [91a5bcdd] + Plots v1.41.6 [2dfb63ee] + PooledArrays v1.4.3 [aea7be01] + PrecompileTools v1.3.3 [21216c6a] + Preferences v1.5.2 [08abe8d2] + PrettyTables v3.2.3 [92933f4c] + ProgressMeter v1.11.0 [43287f4e] + PtrArrays v1.4.0 [1fd47b50] + QuadGK v2.11.2 [3cdcf5f2] + RecipesBase v1.3.4 [01d81517] + RecipesPipeline v0.6.12 [189a3867] + Reexport v1.2.2 [05181044] + RelocatableFolders v1.0.1 [ae029012] + Requires v1.3.1 [295af30f] + Revise v3.14.0 [79098fc4] + Rmath v0.9.0 [6c6a2e73] + Scratch v1.3.0 [91c51154] + SentinelArrays v1.4.9 [992d4aef] + Showoff v1.0.3 [777ac1f9] + SimpleBufferStream v1.2.0 [a2af1166] + SortingAlgorithms v1.2.2 [276daf66] + SpecialFunctions v2.7.1 [860ef19b] + StableRNGs v1.0.4 [10745b16] + Statistics v1.11.1 [82ae8749] + StatsAPI v1.8.0 ⌅ [2913bbd2] + StatsBase v0.33.21 [4c63d2b9] + StatsFuns v1.5.2 [892a3eda] + StringManipulation v0.4.4 [ec057cc2] + StructUtils v2.7.1 [3783bdb8] + TableTraits v1.0.1 [bd369af6] + Tables v1.12.1 [62fd8b95] + TensorCore v0.1.1 [3bb67fe8] + TranscodingStreams v0.11.3 [5c2747f8] + URIs v1.6.1 [1cfade01] + UnicodeFun v0.4.1 [41fe7b60] + Unzip v0.2.0 [ea10d353] + WeakRefStrings v1.4.2 [76eceee3] + WorkerUtilities v1.6.1 [6e34b625] + Bzip2_jll v1.0.9+0 [83423d85] + Cairo_jll v1.18.5+1 [ee1fde0b] + Dbus_jll v1.16.2+0 [2702e6a9] + EpollShim_jll v0.0.20230411+1 [2e619515] + Expat_jll v2.7.3+0 [b22a6f82] + FFMPEG_jll v8.0.1+1 [a3f928ae] + Fontconfig_jll v2.17.1+0 [d7e528f0] + FreeType2_jll v2.13.4+0 [559328eb] + FriBidi_jll v1.0.17+0 [0656b61e] + GLFW_jll v3.4.1+0 [d2c73de3] + GR_jll v0.73.24+0 [1b77fbbe] + GSL_jll v2.8.1+0 [b0724c58] + GettextRuntime_jll v0.22.4+0 [61579ee1] + Ghostscript_jll v9.55.1+0 [7746bdde] + Glib_jll v2.86.3+0 [3b182d85] + Graphite2_jll v1.3.15+0 [2e76f6c2] + HarfBuzz_jll v8.5.1+0 [aacddb02] + JpegTurbo_jll v3.1.4+0 [c1c5ebd0] + LAME_jll v3.100.3+0 [88015f11] + LERC_jll v4.0.1+0 [1d63c593] + LLVMOpenMP_jll v18.1.8+0 [dd4b983a] + LZO_jll v2.10.3+0 ⌅ [e9f186c6] + Libffi_jll v3.4.7+0 [7e76a0d4] + Libglvnd_jll v1.7.1+1 [94ce4f54] + Libiconv_jll v1.18.0+0 [4b2f31a3] + Libmount_jll v2.41.3+0 [89763e89] + Libtiff_jll v4.7.2+0 [38a345b3] + Libuuid_jll v2.41.3+0 [c8ffd9c3] + MbedTLS_jll v2.28.1010+0 [e7412a2a] + Ogg_jll v1.3.6+0 [efe28fd5] + OpenSpecFun_jll v0.5.6+0 [91d4177d] + Opus_jll v1.6.1+0 [36c8627f] + Pango_jll v1.57.0+0 ⌅ [30392449] + Pixman_jll v0.44.2+0 [c0090381] + Qt6Base_jll v6.10.2+1 [629bc702] + Qt6Declarative_jll v6.10.2+1 [ce943373] + Qt6ShaderTools_jll v6.10.2+1 [6de9746b] + Qt6Svg_jll v6.10.2+0 [e99dba38] + Qt6Wayland_jll v6.10.2+1 [f50d1b31] + Rmath_jll v0.5.1+0 [a44049a8] + Vulkan_Loader_jll v1.3.243+0 [a2964d1f] + Wayland_jll v1.24.0+0 [ffd25f8a] + XZ_jll v5.8.2+0 [f67eecfb] + Xorg_libICE_jll v1.1.2+0 [c834827a] + Xorg_libSM_jll v1.2.6+0 [4f6342f7] + Xorg_libX11_jll v1.8.13+0 [0c0b7dd1] + Xorg_libXau_jll v1.0.13+0 [935fb764] + Xorg_libXcursor_jll v1.2.4+0 [a3789734] + Xorg_libXdmcp_jll v1.1.6+0 [1082639a] + Xorg_libXext_jll v1.3.8+0 [d091e8ba] + Xorg_libXfixes_jll v6.0.2+0 [a51aa0fd] + Xorg_libXi_jll v1.8.3+0 [d1454406] + Xorg_libXinerama_jll v1.1.7+0 [ec84b674] + Xorg_libXrandr_jll v1.5.6+0 [ea2f1a96] + Xorg_libXrender_jll v0.9.12+0 [a65dc6b1] + Xorg_libpciaccess_jll v0.18.1+0 [c7cfdc94] + Xorg_libxcb_jll v1.17.1+0 [cc61e674] + Xorg_libxkbfile_jll v1.2.0+0 [e920d4aa] + Xorg_xcb_util_cursor_jll v0.1.6+0 [12413925] + Xorg_xcb_util_image_jll v0.4.1+0 [2def613f] + Xorg_xcb_util_jll v0.4.1+0 [975044d2] + Xorg_xcb_util_keysyms_jll v0.4.1+0 [0d47668e] + Xorg_xcb_util_renderutil_jll v0.3.10+0 [c22f9ab0] + Xorg_xcb_util_wm_jll v0.4.2+0 [35661453] + Xorg_xkbcomp_jll v1.4.7+0 [33bec58e] + Xorg_xkeyboard_config_jll v2.44.0+0 [c5fb5394] + Xorg_xtrans_jll v1.6.0+0 [35ca27e7] + eudev_jll v3.2.14+0 [214eeab7] + fzf_jll v0.61.1+0 [a4ae2306] + libaom_jll v3.13.1+0 [0ac62f75] + libass_jll v0.17.4+0 [1183f4f0] + libdecor_jll v0.2.2+0 [8e53e030] + libdrm_jll v2.4.125+1 [2db6ffa8] + libevdev_jll v1.13.4+0 [f638f0a6] + libfdk_aac_jll v2.0.4+0 [36db933b] + libinput_jll v1.28.1+0 [b53b4c65] + libpng_jll v1.6.55+0 [9a156e7d] + libva_jll v2.23.0+0 [f27f6e37] + libvorbis_jll v1.3.8+0 [009596ad] + mtdev_jll v1.1.7+0 ⌅ [1270edf5] + x264_jll v10164.0.1+0 [dfaa095f] + x265_jll v4.1.0+0 [d8fb68d0] + xkbcommon_jll v1.13.0+0 [0dad84c5] + ArgTools v1.1.2 [56f22d72] + Artifacts v1.11.0 [2a0f44e3] + Base64 v1.11.0 [ade2ca70] + Dates v1.11.0 [8ba89e20] + Distributed v1.11.0 [f43a241f] + Downloads v1.7.0 [7b1f6079] + FileWatching v1.11.0 [9fa8497b] + Future v1.11.0 [b77e0a4c] + InteractiveUtils v1.11.0 [ac6e5ff7] + JuliaSyntaxHighlighting v1.13.0 [b27032c2] + LibCURL v1.0.0 [76f85450] + LibGit2 v1.11.0 [8f399da3] + Libdl v1.11.0 [37e2e46d] + LinearAlgebra v1.13.0 [56ddb016] + Logging v1.11.0 [d6f4376e] + Markdown v1.11.0 [a63ad114] + Mmap v1.11.0 [ca575930] + NetworkOptions v1.3.0 [44cfe95a] + Pkg v1.14.0 [de0858da] + Printf v1.11.0 [3fa0cd96] + REPL v1.11.0 [9a3f8284] + Random v1.11.0 [ea8e919c] + SHA v1.0.0 [9e88b42a] + Serialization v1.11.0 [6462fe0b] + Sockets v1.11.0 [2f01184e] + SparseArrays v1.13.0 [f489334b] + StyledStrings v1.13.0 [4607b0f0] + SuiteSparse [fa267f1f] + TOML v1.0.3 [a4e569a6] + Tar v1.10.0 [8dfed614] + Test v1.11.0 [cf7118a7] + UUIDs v1.11.0 [4ec0a83e] + Unicode v1.11.0 [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1 [deac9b47] + LibCURL_jll v8.19.0+0 [e37daf67] + LibGit2_jll v1.9.2+0 [29816b5a] + LibSSH2_jll v1.11.3+1 [14a3606d] + MozillaCACerts_jll v2025.12.2 [4536629a] + OpenBLAS_jll v0.3.30+0 [05823500] + OpenLibm_jll v0.8.7+0 [458c3c95] + OpenSSL_jll v3.5.5+0 [efcefdf7] + PCRE2_jll v10.47.0+0 [bea87d4a] + SuiteSparse_jll v7.10.1+0 [83775a58] + Zlib_jll v1.3.2+0 [3161d3a3] + Zstd_jll v1.5.7+1 [8e850b90] + libblastrampoline_jll v5.15.0+0 [8e850ede] + nghttp2_jll v1.68.0+1 [3f19e933] + p7zip_jll v17.8.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m` Installation completed after 6.64s ################################################################################ # Precompilation # Precompiling PkgEval dependencies... Precompiling package dependencies... Precompiling packages... 15226.1 ms ✓ JWAS 1 dependency successfully precompiled in 20 seconds. 232 already precompiled. Precompilation completed after 35.09s ################################################################################ # Testing # Testing JWAS Status `/tmp/jl_LXVsRv/Project.toml` [336ed68f] CSV v0.10.16 [a93c6f00] DataFrames v1.8.1 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.123 [92c85e6c] GSL v1.0.1 [cd3eb016] HTTP v1.11.0 [c9a035f4] JWAS v2.2.0 [2c470bb0] Kronecker v0.5.5 [91a5bcdd] Plots v1.41.6 [92933f4c] ProgressMeter v1.11.0 [295af30f] Revise v3.14.0 [10745b16] Statistics v1.11.1 ⌅ [2913bbd2] StatsBase v0.33.21 [b77e0a4c] InteractiveUtils v1.11.0 [37e2e46d] LinearAlgebra v1.13.0 [de0858da] Printf v1.11.0 [9a3f8284] Random v1.11.0 [2f01184e] SparseArrays v1.13.0 [8dfed614] Test v1.11.0 Status `/tmp/jl_LXVsRv/Manifest.toml` [66dad0bd] AliasTables v1.1.3 [d1d4a3ce] BitFlags v0.1.9 [336ed68f] CSV v0.10.16 [da1fd8a2] CodeTracking v3.0.0 [944b1d66] CodecZlib v0.7.8 [35d6a980] ColorSchemes v3.31.0 [3da002f7] ColorTypes v0.12.1 [c3611d14] ColorVectorSpace v0.11.0 [5ae59095] Colors v0.13.1 [34da2185] Compat v4.18.1 [807dbc54] Compiler v0.1.1 [f0e56b4a] ConcurrentUtilities v2.5.1 [d38c429a] Contour v0.6.3 [a8cc5b0e] Crayons v4.1.1 [9a962f9c] DataAPI v1.16.0 [a93c6f00] DataFrames v1.8.1 ⌅ [864edb3b] DataStructures v0.18.22 [e2d170a0] DataValueInterfaces v1.0.0 [8bb1440f] DelimitedFiles v1.9.1 [31c24e10] Distributions v0.25.123 [ffbed154] DocStringExtensions v0.9.5 [460bff9d] ExceptionUnwrapping v0.1.11 [c87230d0] FFMPEG v0.4.5 [48062228] FilePathsBase v0.9.24 [1a297f60] FillArrays v1.16.0 [53c48c17] FixedPointNumbers v0.8.5 [1fa38f19] Format v1.3.7 [28b8d3ca] GR v0.73.24 [92c85e6c] GSL v1.0.1 [42e2da0e] Grisu v1.0.2 [cd3eb016] HTTP v1.11.0 [34004b35] HypergeometricFunctions v0.3.28 [842dd82b] InlineStrings v1.4.5 [41ab1584] InvertedIndices v1.3.1 [92d709cd] IrrationalConstants v0.2.6 [82899510] IteratorInterfaceExtensions v1.0.0 [1019f520] JLFzf v0.1.11 [692b3bcd] JLLWrappers v1.7.1 [682c06a0] JSON v1.4.0 [c9a035f4] JWAS v2.2.0 [aa1ae85d] JuliaInterpreter v0.10.11 [2c470bb0] Kronecker v0.5.5 [b964fa9f] LaTeXStrings v1.4.0 [23fbe1c1] Latexify v0.16.10 [2ab3a3ac] LogExpFunctions v0.3.29 [e6f89c97] LoggingExtras v1.2.0 [6f1432cf] LoweredCodeUtils v3.5.1 [1914dd2f] MacroTools v0.5.16 [739be429] MbedTLS v1.1.10 [442fdcdd] Measures v0.3.3 [e1d29d7a] Missings v1.2.0 [77ba4419] NaNMath v1.1.3 [356022a1] NamedDims v1.2.3 [4d8831e6] OpenSSL v1.6.1 [bac558e1] OrderedCollections v1.8.1 ⌃ [90014a1f] PDMats v0.11.35 [69de0a69] Parsers v2.8.3 [ccf2f8ad] PlotThemes v3.3.0 [995b91a9] PlotUtils v1.4.4 [91a5bcdd] Plots v1.41.6 [2dfb63ee] PooledArrays v1.4.3 [aea7be01] PrecompileTools v1.3.3 [21216c6a] Preferences v1.5.2 [08abe8d2] PrettyTables v3.2.3 [92933f4c] ProgressMeter v1.11.0 [43287f4e] PtrArrays v1.4.0 [1fd47b50] QuadGK v2.11.2 [3cdcf5f2] RecipesBase v1.3.4 [01d81517] RecipesPipeline v0.6.12 [189a3867] Reexport v1.2.2 [05181044] RelocatableFolders v1.0.1 [ae029012] Requires v1.3.1 [295af30f] Revise v3.14.0 [79098fc4] Rmath v0.9.0 [6c6a2e73] Scratch v1.3.0 [91c51154] SentinelArrays v1.4.9 [992d4aef] Showoff v1.0.3 [777ac1f9] SimpleBufferStream v1.2.0 [a2af1166] SortingAlgorithms v1.2.2 [276daf66] SpecialFunctions v2.7.1 [860ef19b] StableRNGs v1.0.4 [10745b16] Statistics v1.11.1 [82ae8749] StatsAPI v1.8.0 ⌅ [2913bbd2] StatsBase v0.33.21 [4c63d2b9] StatsFuns v1.5.2 [892a3eda] StringManipulation v0.4.4 [ec057cc2] StructUtils v2.7.1 [3783bdb8] TableTraits v1.0.1 [bd369af6] Tables v1.12.1 [62fd8b95] TensorCore v0.1.1 [3bb67fe8] TranscodingStreams v0.11.3 [5c2747f8] URIs v1.6.1 [1cfade01] UnicodeFun v0.4.1 [41fe7b60] Unzip v0.2.0 [ea10d353] WeakRefStrings v1.4.2 [76eceee3] WorkerUtilities v1.6.1 [6e34b625] Bzip2_jll v1.0.9+0 [83423d85] Cairo_jll v1.18.5+1 [ee1fde0b] Dbus_jll v1.16.2+0 [2702e6a9] EpollShim_jll v0.0.20230411+1 [2e619515] Expat_jll v2.7.3+0 [b22a6f82] FFMPEG_jll v8.0.1+1 [a3f928ae] Fontconfig_jll v2.17.1+0 [d7e528f0] FreeType2_jll v2.13.4+0 [559328eb] FriBidi_jll v1.0.17+0 [0656b61e] GLFW_jll v3.4.1+0 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[3f19e933] p7zip_jll v17.8.0+0 Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. Testing Running tests... ====================================================================== JWAS.jl Comprehensive Test Suite ====================================================================== Cleaning up old test artifacts... Cleanup complete. ====================================================================== Running tests in: test_run_59819 ====================================================================== Test Configuration: Unit Tests: Always run ✓ Integration Tests: Disabled (set RUN_INTEGRATION_TESTS=true) ====================================================================== ┌ Warning: Estimated marker memory usage exceeds configured guard threshold. │ context: test │ estimated: 600.00 B │ threshold (50.0% of RAM): 500.00 B │ system RAM: 1000.00 B │ Set memory_guard=:warn or :off to override, or reduce model/data size. └ @ JWAS ~/.julia/packages/JWAS/4fFdk/src/1.JWAS/src/markers/tools4genotypes.jl:231 The delimiter 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 guardrail_error_mode is created to save results. 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[] (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 guardrail_error_mode/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 The delimiter 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 guardrail_off_mode is created to save results. 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[] (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 guardrail_off_mode/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 10 burnin 0 starting_value true printout_frequency 11 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file guardrail_off_mode/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file guardrail_off_mode/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file guardrail_off_mode/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file guardrail_off_mode/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file guardrail_off_mode/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file guardrail_off_mode/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file guardrail_off_mode/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 20%|███████ | ETA: 0:00:19 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:10 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_qptKnf/geno_missing_stream. The delimiter in geno_missing.csv is ','. The header (marker IDs) is provided in geno_missing.csv. 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: 4; #individuals: 6 Genotype informatin: #markers: 4; #individuals: 6 (storage=:stream) Streaming genotype files are created with prefix /tmp/jl_qptKnf/geno_nomissing_stream. The delimiter in geno_nomissing.csv is ','. The header (marker IDs) is provided in geno_nomissing.csv. Genotype informatin: #markers: 4; #individuals: 6 The folder dense_results is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 6 observations are used in the analysis.These individual IDs are saved in the file dense_results/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: 0.503496 A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: 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: 0.503 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file dense_results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file dense_results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file dense_results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file dense_results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Genotype informatin: #markers: 4; #individuals: 6 (storage=:stream) The folder stream_results is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 6 observations are used in the analysis.These individual IDs are saved in the file stream_results/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: 0.503496 storage=:stream is enabled; genotype alignment is skipped and original ID order is used. A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 40 burnin 10 starting_value true printout_frequency 41 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: 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: 0.503 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file stream_results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file stream_results/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file stream_results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file stream_results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. running MCMC ... 5%|█▊ | ETA: 0:00:31 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:01 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Streaming genotype files are created with prefix /tmp/jl_hgdICA/geno_stream. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_fast_blocks is created to save results. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_weighted is created to save results. 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[] (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 stream_weighted/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: 0.680851 storage=:stream is enabled; genotype alignment is skipped and original ID order is used. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_mt is created to save results. Genotype informatin: #markers: 3; #individuals: 4 (storage=:stream) The folder stream_double is created to save results. 0 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_1ktJKA/geno_stream. The delimiter in geno.csv is ','. The header (marker IDs) is provided in geno.csv. 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: 6 Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) Streaming genotype files are created with prefix /tmp/jl_1ktJKA/geno_uncentered_stream. The argument center=true is ignored for storage=:stream. Backend metadata centered=false is used. Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) Streaming genotype files are created with prefix /tmp/jl_1ktJKA/cleanup_true_stream. Streaming genotype files are created with prefix /tmp/jl_1ktJKA/cleanup_false_stream. Auto conversion mode selected :dense (estimated dense bytes=120, auto_dense_max_bytes=10000). Streaming genotype files are created with prefix /tmp/jl_1ktJKA/auto_dense_stream. Auto conversion mode selected :lowmem (estimated dense bytes=120, auto_dense_max_bytes=1). Streaming genotype files are created with prefix /tmp/jl_1ktJKA/auto_lowmem_stream. Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Missing values (9.0) are replaced by column means. 1 loci which are fixed or have minor allele frequency < 0.01 are removed. Genotype informatin: #markers: 2; #individuals: 4 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. 1 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_jXDCHB/annotated_stream_qc_stream. Genotype informatin: #markers: 2; #individuals: 4 (storage=:stream) 1 loci which are fixed or have minor allele frequency < 0.01 are removed. Streaming genotype files are created with prefix /tmp/jl_OBEFnX/annotated_stream_legacy_stream. The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 20; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 20; #individuals: 2 The first column in the dataframe should be individual IDs. The remaining columns are markers with the data type Number. Genotype informatin: #markers: 1; #individuals: 2 The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. 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[] (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 results/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 + plain_geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 12 burnin 2 starting_value true printout_frequency 99 output_samples_frequency 1 constraint on residual variance false constraint on marker effect variance for plain_geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category plain_geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 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_marker_effects_plain_geno_y1.txt is created to save MCMC samples for marker_effects_plain_geno_y1. The file results/MCMC_samples_marker_effects_variances_plain_geno.txt is created to save MCMC samples for marker_effects_variances_plain_geno. The file results/MCMC_samples_pi_plain_geno.txt is created to save MCMC samples for pi_plain_geno. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. 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[] (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 results/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 + annotated_geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 30 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_geno false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_geno Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 [ Info: Annotated BayesC initialization: starting pi=0.0 is degenerate; using 10% excluded markers. The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_geno_y1.txt is created to save MCMC samples for marker_effects_annotated_geno_y1. The file results/MCMC_samples_marker_effects_variances_annotated_geno.txt is created to save MCMC samples for marker_effects_variances_annotated_geno. The file results/MCMC_samples_pi_annotated_geno.txt is created to save MCMC samples for pi_annotated_geno. 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. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The folder results is created to save results. 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[] (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 results/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 BLOCK SIZE: 2 A Linear Mixed Model was build using model equations: y1 = intercept + annotated_blocks Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 15 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_blocks false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_blocks Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.492 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 [ Info: Annotated BayesC initialization: starting pi=0.0 is degenerate; using 10% excluded markers. The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_blocks_y1.txt is created to save MCMC samples for marker_effects_annotated_blocks_y1. The file results/MCMC_samples_marker_effects_variances_annotated_blocks.txt is created to save MCMC samples for marker_effects_variances_annotated_blocks. The file results/MCMC_samples_pi_annotated_blocks.txt is created to save MCMC samples for pi_annotated_blocks. 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 ... 13%|████▋ | ETA: 0:00:34 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:05 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Streaming genotype files are created with prefix /tmp/jl_OYNwhd/annotated_stream_stream. Genotype informatin: #markers: 5; #individuals: 6 (storage=:stream) The folder results is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 6 observations are used in the analysis.These individual IDs are saved in the file results/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: 0.402235 storage=:stream is enabled; genotype alignment is skipped and original ID order is used. A Linear Mixed Model was build using model equations: y1 = intercept + annotated_stream Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 30 burnin 10 starting_value true printout_frequency 31 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for annotated_stream false missing_phenotypes true update_priors_frequency 0 seed 2026 Hyper-parameters Information: residual variances: 1.000 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category annotated_stream Method BayesC genetic variances (genomic): 1.000 marker effect variances: 0.402 π_j (min/mean/max) 0.000 / 0.000 / 0.000 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 [ Info: Annotated BayesC initialization: starting pi=0.0 is degenerate; using 10% excluded markers. The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_annotated_stream_y1.txt is created to save MCMC samples for marker_effects_annotated_stream_y1. The file results/MCMC_samples_marker_effects_variances_annotated_stream.txt is created to save MCMC samples for marker_effects_variances_annotated_stream. The file results/MCMC_samples_pi_annotated_stream.txt is created to save MCMC samples for pi_annotated_stream. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in pedigree.txt is ','. coding pedigree... 17%|█████▍ | ETA: 0:00:01 coding pedigree... 100%|████████████████████████████████| Time: 0:00:00 calculating inbreeding... 17%|████▏ | ETA: 0:00:02 calculating inbreeding... 100%|█████████████████████████| Time: 0:00:00 Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The folder test_set_random_ped is created to save results. Checking pedigree... 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"] (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 test_set_random_ped/IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept + ID Model Information: Term C/F F/R nLevels intercept factor fixed 1 ID factor random 12 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 1 constraint on residual variance false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: random effect variances (y1:ID): [1.600000023841858;;] genetic variances (polygenic): 1.6f0 residual variances: 1.000 Genomic Information: Degree of freedom for hyper-parameters: residual variances: 4.000 polygenic effect variances: 5.000 The file test_set_random_ped/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_set_random_ped/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file test_set_random_ped/MCMC_samples_y1.ID_variances.txt is created to save MCMC samples for y1:ID_variances. The file test_set_random_ped/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_set_random_ped/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_set_random_ped/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 4%|█▍ | ETA: 0:03:33 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:08 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_ebv_output is created to save results. 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[] (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 test_ebv_output/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_ebv_output/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_ebv_output/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_ebv_output/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_ebv_output/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_ebv_output/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_ebv_output/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_ebv_output/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_ebv_h2 is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 test_ebv_h2/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_ebv_h2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_ebv_h2/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_ebv_h2/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_ebv_h2/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_ebv_h2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_ebv_h2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_ebv_h2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:26 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:00 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_mcmc_samples is created to save results. 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[] (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 test_mcmc_samples/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_mcmc_samples/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mcmc_samples/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mcmc_samples/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mcmc_samples/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mcmc_samples/MCMC_samples_y1.intercept.txt is created to save MCMC samples for y1:intercept. The file test_mcmc_samples/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mcmc_samples/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mcmc_samples/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 Get individual IDs, inverse of numerator relationship matrix, and inbreeding coefficients. Pedigree information: #individuals: 3 #sires: 1 #dams: 1 #founders: 2 The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The delimiter 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 test_invalid_method is created to save results. The folder test_ss_no_geno is created to save results. The delimiter 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 test_freq_zero is created to save results. A Linear Mixed Model was build using model equations: y1 = intercept + x1 Model Information: Term C/F F/R nLevels intercept factor fixed 0 x1 factor fixed 0 The delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 test_mt_bayesc is created to save results. 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[] (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 test_mt_bayesc/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesc/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesc/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_bayesc/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_bayesc/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_bayesc/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_bayesc/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesc/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesc/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesc/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:23:25 running MCMC ... 64%|██████████████████████▍ | ETA: 0:00:18 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:32 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_mt_rrblup is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 test_mt_rrblup/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_rrblup/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_rrblup/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_rrblup/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_rrblup/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_rrblup/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_rrblup/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_rrblup/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_rrblup/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_rrblup/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:02:54 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:03 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The folder test_mt_ped is created to save results. Checking pedigree... 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", "y2:ID"] (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 test_mt_ped/IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept + ID y2 = intercept + ID Model Information: Term C/F F/R nLevels intercept factor fixed 1 ID factor random 12 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: random effect variances (y1:ID,y2:ID): 1.0f0 0.5f0 0.5f0 1.0f0 genetic variances (polygenic): 1.0f0 0.5f0 0.5f0 1.0f0 residual variances: 1.0f0 0.5f0 0.5f0 1.0f0 Genomic Information: Degree of freedom for hyper-parameters: residual variances: 6.000 polygenic effect variances: 6.000 The file test_mt_ped/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_ped/MCMC_samples_polygenic_effects_variance.txt is created to save MCMC samples for polygenic_effects_variance. The file test_mt_ped/MCMC_samples_y1.ID_y2.ID_variances.txt is created to save MCMC samples for y1:ID_y2:ID_variances. The file test_mt_ped/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_ped/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_ped/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_ped/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_mt_iid is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 test_mt_iid/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + x2 + geno y2 = intercept + x2 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 x2 factor random 2 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: random effect variances (y1:x2,y2:x2): 0.5f0 0.1f0 0.1f0 0.5f0 residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 random effect variances: 6.000 marker effect variances: 6.000 The file test_mt_iid/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_iid/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_iid/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_iid/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_iid/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_iid/MCMC_samples_y1.x2_y2.x2_variances.txt is created to save MCMC samples for y1:x2_y2:x2_variances. The file test_mt_iid/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_iid/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_iid/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_iid/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_bayesb is created to save results. 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[] (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 test_bayesb/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_bayesb/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_bayesb/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_bayesb/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_bayesb/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_bayesb/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_bayesb/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_bayesb/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_bayesa is created to save results. BayesA runs with estimatePi = false. BayesA is equivalent to BayesB with known π=0. BayesB with known π=0 runs. 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[] (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 test_bayesa/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_bayesa/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_bayesa/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_bayesa/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_bayesa/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_bayesa/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_bayesa/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_bayesa/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_bayesl is created to save results. BayesL runs with estimatePi = false. 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[] (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 test_bayesl/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_bayesl/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_bayesl/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_bayesl/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_bayesl/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_bayesl/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_bayesl/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_bayesl/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:02:18 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:02 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_gblup is created to save results. GBLUP runs with estimatePi = false. 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[] (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 test_gblup/IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_gblup/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_gblup/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_gblup/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_gblup/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_gblup/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_gblup/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_gblup/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:03:33 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:04 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_gwas_win is created to save results. 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[] (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 test_gwas_win/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_gwas_win/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_gwas_win/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_gwas_win/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_gwas_win/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_gwas_win/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_gwas_win/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_gwas_win/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance. running GWAS... 25%|█████████ | ETA: 0:00:01 running GWAS... 100%|████████████████████████████████████| Time: 0:00:00 Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance. Compute the posterior probability of association of the genomic window that explains more than 0.001 of the total genetic variance. Compute the posterior probability of association of the genomic window that explains more than 0.01 of the total genetic variance. The delimiter 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 test_repro is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 test_repro/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_repro/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_repro/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_repro/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_repro/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_repro/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_repro/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_repro/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_multi_samples is created to save results. 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[] (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 test_multi_samples/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 x1 covariate fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_multi_samples/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_multi_samples/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_multi_samples/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_multi_samples/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_multi_samples/MCMC_samples_y1.intercept.txt is created to save MCMC samples for y1:intercept. The file test_multi_samples/MCMC_samples_y1.x1.txt is created to save MCMC samples for y1:x1. The file test_multi_samples/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_multi_samples/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_multi_samples/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. A Linear Mixed Model was build using model equations: y1 = intercept + x1 Model Information: Term C/F F/R nLevels intercept factor fixed 0 x1 factor fixed 0 The delimiter 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 test_mt_describe is created to save results. 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[] (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 test_mt_describe/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_describe/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_describe/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_describe/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_describe/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_describe/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_describe/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_describe/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_describe/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_describe/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 0.623f0 -0.089f0 -0.089f0 0.681f0 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 1.0 marker effect variances: 0.406f0 0.323f0 0.323f0 0.573f0 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"]0.058298823501844996 ["N", "N"] 0.43521148638625434 ["Y", "Y"] 0.1038532014971991 ["Y", "N"] 0.40263648861470164 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The delimiter 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 test_mt_bayesb is created to save results. 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[] (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 test_mt_bayesb/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesb/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesb/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_bayesb/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_bayesb/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_bayesb/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_bayesb/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesb/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesb/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesb/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:00:44 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:01 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_mt_bayesl is created to save results. BayesL runs with estimatePi = false. 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[] (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 test_mt_bayesl/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_bayesl/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_bayesl/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_bayesl/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_bayesl/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_bayesl/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_bayesl/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_bayesl/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_bayesl/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_bayesl/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:02:24 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:02 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_mt_gblup is created to save results. GBLUP runs with estimatePi = false. 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[] (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 test_mt_gblup/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 + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_gblup/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_gblup/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_gblup/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_gblup/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_gblup/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_gblup/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_gblup/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_gblup/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_gblup/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:05:29 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:06 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_constraint is created to save results. 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[] (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 test_constraint/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 0.0 0.0 0.492462 Constraint on marker effect variance-covariance matrix (i.e., zero covariance) for geno A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno true missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 0.0f0 0.0f0 1.0f0 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 1.0 0.0 0.0 1.0 marker effect variances: 0.492 0.0 0.0 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 4.000 The file test_constraint/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_constraint/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_constraint/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_constraint/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_constraint/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_constraint/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_constraint/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_constraint/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_constraint/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:03:34 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:06 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_missing_pheno is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 test_missing_pheno/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_missing_pheno/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_missing_pheno/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_missing_pheno/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_missing_pheno/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_missing_pheno/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_missing_pheno/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_missing_pheno/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_missing_pheno/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_missing_pheno/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_pi_est is created to save results. 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[] (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 test_pi_est/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_pi_est/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_pi_est/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_pi_est/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_pi_est/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_pi_est/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_pi_est/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_pi_est/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_pi_est/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_pi_est/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_est_scale is created to save results. 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[] (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 test_est_scale/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale true Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_est_scale/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_est_scale/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_est_scale/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_est_scale/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_est_scale/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_est_scale/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_est_scale/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_mt_ebv is created to save results. 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[] (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 test_mt_ebv/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 0.492462 A Linear Mixed Model was build using model equations: y1 = intercept + geno y2 = intercept + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.0f0 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 1.0 marker effect variances: 0.492 0.246 0.246 0.492 Π: (Y(yes):included; N(no):excluded) ["y1", "y2"] probability ["N", "Y"] 0.0 ["N", "N"] 0.0 ["Y", "Y"] 1.0 ["Y", "N"] 0.0 estimatePi true estimate_scale false Degree of freedom for hyper-parameters: residual variances: 6.000 marker effect variances: 6.000 The file test_mt_ebv/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_mt_ebv/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_mt_ebv/MCMC_samples_marker_effects_geno_y2.txt is created to save MCMC samples for marker_effects_geno_y2. The file test_mt_ebv/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_mt_ebv/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_mt_ebv/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_mt_ebv/MCMC_samples_EBV_y2.txt is created to save MCMC samples for EBV_y2. The file test_mt_ebv/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_mt_ebv/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_het_res is created to save results. 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[] (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 test_het_res/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 100 burnin 20 starting_value true printout_frequency 101 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_het_res/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_het_res/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_het_res/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_het_res/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_het_res/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_het_res/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_het_res/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 2%|▊ | ETA: 0:01:04 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:01 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_double_prec is created to save results. 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[] (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 test_double_prec/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_double_prec/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_double_prec/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_double_prec/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_double_prec/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_double_prec/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_double_prec/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_double_prec/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. running MCMC ... 4%|█▍ | ETA: 0:01:21 running MCMC ... 24%|████████▍ | ETA: 0:00:13 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:04 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_sem_issue162 is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_issue162/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: Constraint on residual variance-covariance matrix (i.e., zero covariance) A Linear Mixed Model was build using model equations: y1 = intercept + genotypes y2 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_sem_issue162/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_sem_issue162/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_sem_issue162/MCMC_samples_marker_effects_genotypes_y2.txt is created to save MCMC samples for marker_effects_genotypes_y2. The file test_sem_issue162/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_sem_issue162/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. running MCMC ... 10%|███▌ | ETA: 0:01:51 running MCMC ... 60%|█████████████████████ | ETA: 0:00:09 running MCMC ... 100%|███████████████████████████████████| Time: 0:00:13 The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). The delimiter 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 test_sem_validation_1 is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_validation_1/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: The delimiter 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 test_sem_validation_2 is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_validation_2/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 Constraint on residual variance-covariance matrix (i.e., zero covariance) The delimiter 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 test_sem_validation_3 is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_validation_3/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: Constraint on residual variance-covariance matrix (i.e., zero covariance) A Linear Mixed Model was build using model equations: y1 = intercept + genotypes y2 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_sem_validation_3/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_sem_validation_3/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_sem_validation_3/MCMC_samples_marker_effects_genotypes_y2.txt is created to save MCMC samples for marker_effects_genotypes_y2. The file test_sem_validation_3/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_sem_validation_3/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). The delimiter 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 test_sem_2trait is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_2trait/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: Constraint on residual variance-covariance matrix (i.e., zero covariance) A Linear Mixed Model was build using model equations: y1 = intercept + genotypes y2 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_sem_2trait/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_sem_2trait/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_sem_2trait/MCMC_samples_marker_effects_genotypes_y2.txt is created to save MCMC samples for marker_effects_genotypes_y2. The file test_sem_2trait/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_sem_2trait/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). The delimiter 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 test_sem_3trait is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_3trait/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: Constraint on residual variance-covariance matrix (i.e., zero covariance) A Linear Mixed Model was build using model equations: y1 = intercept + genotypes y2 = intercept + genotypes y3 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_sem_3trait/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_sem_3trait/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_sem_3trait/MCMC_samples_marker_effects_genotypes_y2.txt is created to save MCMC samples for marker_effects_genotypes_y2. The file test_sem_3trait/MCMC_samples_marker_effects_genotypes_y3.txt is created to save MCMC samples for marker_effects_genotypes_y3. The file test_sem_3trait/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_sem_3trait/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). The delimiter 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 test_sem_repro_1 is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_repro_1/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: Constraint on residual variance-covariance matrix (i.e., zero covariance) A Linear Mixed Model was build using model equations: y1 = intercept + genotypes y2 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_sem_repro_1/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_sem_repro_1/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_sem_repro_1/MCMC_samples_marker_effects_genotypes_y2.txt is created to save MCMC samples for marker_effects_genotypes_y2. The file test_sem_repro_1/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_sem_repro_1/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). The delimiter 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 test_sem_repro_2 is created to save results. 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[] (Note that genomic data is always included for now).Phenotypes for 3 observations are used in the analysis.These individual IDs are saved in the file test_sem_repro_2/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: Constraint on residual variance-covariance matrix (i.e., zero covariance) A Linear Mixed Model was build using model equations: y1 = intercept + genotypes y2 = intercept + genotypes Model Information: Term C/F F/R nLevels intercept factor fixed 1 The file test_sem_repro_2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_sem_repro_2/MCMC_samples_marker_effects_genotypes_y1.txt is created to save MCMC samples for marker_effects_genotypes_y1. The file test_sem_repro_2/MCMC_samples_marker_effects_genotypes_y2.txt is created to save MCMC samples for marker_effects_genotypes_y2. The file test_sem_repro_2/MCMC_samples_marker_effects_variances_genotypes.txt is created to save MCMC samples for marker_effects_variances_genotypes. The file test_sem_repro_2/MCMC_samples_pi_genotypes.txt is created to save MCMC samples for pi_genotypes. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). Compute the model frequency for each marker (the probability the marker is included in the model). The delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter 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 delimiter in genotypes.txt is ','. The header (marker IDs) is provided in genotypes.txt. Genotype informatin: #markers: 5; #individuals: 7 The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The delimiter in pedigree.txt is ','. Pedigree information: #individuals: 12 #sires: 4 #dams: 5 #founders: 3 The delimiter 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 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[] (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 results/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 10 starting_value true printout_frequency 51 output_samples_frequency 10 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 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_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_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. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 test_results_temp is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 test_results_temp/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 1 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file test_results_temp/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file test_results_temp/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file test_results_temp/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file test_results_temp/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file test_results_temp/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file test_results_temp/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file test_results_temp/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 temp1 is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 temp1/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 1 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 999 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file temp1/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file temp1/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file temp1/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file temp1/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file temp1/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file temp1/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file temp1/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 temp2 is created to save results. RR-BLUP runs with estimatePi = false. 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[] (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 temp2/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 1 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 999 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file temp2/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file temp2/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file temp2/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file temp2/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file temp2/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file temp2/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file temp2/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 gwas_test is created to save results. 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[] (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 gwas_test/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 + geno Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 50 burnin 0 starting_value true printout_frequency 51 output_samples_frequency 1 constraint on residual variance false constraint on marker effect variance for geno false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: 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 estimate_scale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file gwas_test/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file gwas_test/MCMC_samples_marker_effects_geno_y1.txt is created to save MCMC samples for marker_effects_geno_y1. The file gwas_test/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file gwas_test/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file gwas_test/MCMC_samples_EBV_y1.txt is created to save MCMC samples for EBV_y1. The file gwas_test/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance. The file gwas_test/MCMC_samples_heritability.txt is created to save MCMC samples for heritability. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv 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). The folder missing_test is created to save results. 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[] (Note that genomic data is always included for now).Default or user-defined prediction equation are not available. Phenotypes for 4 observations are used in the analysis.These individual IDs are saved in the file missing_test/IDs_for_individuals_with_phenotypes.txt. A Linear Mixed Model was build using model equations: y1 = intercept Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 10 burnin 0 starting_value true printout_frequency 11 output_samples_frequency 1 constraint on residual variance false missing_phenotypes true update_priors_frequency 0 seed 123 Hyper-parameters Information: residual variances: 1.000 Genomic Information: Degree of freedom for hyper-parameters: residual variances: 4.000 The file missing_test/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The version of Julia and Platform in use: Julia Version 1.14.0-DEV.1918 Commit 78a0dc1151* (2026-03-19 04:57 UTC) Platform Info: OS: Linux (x86_64-linux-gnu) CPU: 128 × AMD EPYC 7502 32-Core Processor WORD_SIZE: 64 LLVM: libLLVM-20.1.8 (ORCJIT, znver2) GC: Built with stock GC 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_LXVsRv The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files. The delimiter 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 delimiter 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 [ Info: Skipping integration tests (set RUN_INTEGRATION_TESTS=true to run) Test Summary: | Pass Total Time JWAS.jl Full Test Suite | 470 470 9m03.5s ====================================================================== Test Run Summary ====================================================================== Test directory: test_run_59819 Cleaning up test files... ✓ Test directory cleaned up ====================================================================== ✓ All tests completed successfully! ====================================================================== Testing JWAS tests passed Testing completed after 516.29s PkgEval succeeded after 690.46s