********************************************************* * GCTB 2.5.6 beta * * Genome-wide Complex Trait Bayesian analysis * * For inquiries, contact: Jian Zeng * * Last updated: 24 May, 2026 * * MIT License * ********************************************************* Options: --ldm-eigen ldm_eigen --gwas-summary gwas.imputed.ma --sbayes R --thread 4 --out sbayesr Using 4 threads. Analysis started: Sat Jun 6 20:32:42 2026 Reading LD matrix eigen-decomposition data... Reading LDM info from file [ldm_eigen/ldm.info]. 590 LD Blocks to be included from [ldm_eigen/ldm.info]. Reading LDM SNP info from file [ldm_eigen/snp.info]. 272826 SNPs to be included from [ldm_eigen/snp.info]. Reading GWAS summary data from [gwas.imputed.ma]. 272826 matched SNPs in the GWAS summary data (in total 272826 SNPs). 272826 SNPs on 22 chromosomes are included. 590 LD blocks are included. GWAS effect scaling: estimated by (1/sqrt(n*SE^2+b^2) from summary stats. Reading eigenvectors from binary file and making W and Q matrices... Data summary: mean sd GWAS SNP Phenotypic variance 210.528 12.583 GWAS SNP heterozygosity 0.353 0.126 GWAS SNP sample size 2997 3 GWAS SNP effect (in genotype SD unit) -0.000 0.018 GWAS SNP SE 0.018 0.000 LD block size 462 163 LD block rank 328 96 Finding the best eigen cutoff from [0.995 0.99 0.95 0.9] based on pseudo summary data validation. Cutoff Prediction accuracy (r) Relative accuracy 0.995 0.556542 1 0.99 0.58244 1.04653 0.95 0.562921 1.01146 0.9 0.525175 0.943639 0.995 is selected to be the eigen cutoff to continue the analysis (time used: 0:0:33). SBayesR Using the low-rank model Gamma: 0 0.001 0.01 0.1 1 Fitting model assuming scaled genotypes MCMC launched ... Number of chains: 1 Chain length: 3000 iterations Burn-in: 1000 iterations Iter NumSnp1 NumSnp2 NumSnp3 NumSnp4 NumSnp5 Vg1 Vg2 Vg3 Vg4 Vg5 SigmaSq hsq ResVar NumSkeptSnp TimeLeft 100 270627 1528 494 99 78 0.0000 0.0101 0.0337 0.0699 0.8863 0.0063 0.6116 1.0113 0 0:2:54 200 271136 1311 187 82 110 0.0000 0.0095 0.0166 0.0554 0.9185 0.0069 0.6324 1.0111 0 0:2:34 300 269976 2391 343 28 88 0.0000 0.0205 0.0258 0.0273 0.9264 0.0070 0.5684 1.0095 2 0:2:33 400 268571 3554 562 48 89 0.0000 0.0226 0.0349 0.0315 0.9110 0.0062 0.6378 1.0155 0 0:2:23 500 269382 2861 395 107 78 0.0000 0.0155 0.0224 0.0519 0.9103 0.0060 0.6165 1.0126 0 0:2:20 600 267696 4476 519 28 104 0.0000 0.0247 0.0262 0.0174 0.9317 0.0063 0.7377 1.0156 0 0:2:16 700 268880 3615 246 7 74 0.0000 0.0224 0.0166 0.0012 0.9598 0.0055 0.5204 1.0128 0 0:2:8 800 267627 4936 106 66 86 0.0000 0.0361 0.0089 0.0620 0.8931 0.0055 0.5592 1.0218 0 0:2:3 900 268001 4715 7 6 92 0.0000 0.0313 0.0006 0.0036 0.9644 0.0051 0.5108 1.0165 2 0:1:56 1000 269502 3114 35 31 137 0.0000 0.0230 0.0022 0.0190 0.9559 0.0059 0.6710 1.0114 0 0:1:52 1100 269306 3230 86 120 77 0.0000 0.0274 0.0072 0.0855 0.8799 0.0053 0.5516 1.0120 0 0:1:45 1200 268346 4162 203 25 83 0.0000 0.0352 0.0194 0.0167 0.9288 0.0057 0.5555 1.0136 0 0:1:40 1300 268028 4622 61 38 70 0.0000 0.0432 0.0092 0.0608 0.8868 0.0059 0.5309 1.0144 0 0:1:35 1400 267940 4324 311 175 69 0.0000 0.0386 0.0255 0.1610 0.7749 0.0056 0.5734 1.0124 0 0:1:29 1500 267048 5426 228 7 110 0.0000 0.0461 0.0172 0.0040 0.9327 0.0061 0.6212 1.0224 0 0:1:24 1600 266918 5387 411 2 101 0.0000 0.0378 0.0260 0.0008 0.9353 0.0056 0.6008 1.0209 0 0:1:17 1700 268534 3946 234 1 104 0.0000 0.0330 0.0178 0.0000 0.9492 0.0047 0.5027 1.0156 0 0:1:12 1800 268207 4476 38 31 67 0.0000 0.0346 0.0029 0.0263 0.9361 0.0041 0.4739 1.0158 0 0:1:7 1900 267434 5164 103 13 105 0.0000 0.0470 0.0078 0.0106 0.9346 0.0058 0.5575 1.0189 0 0:1:1 2000 265043 7375 201 103 97 0.0000 0.0623 0.0172 0.0799 0.8406 0.0062 0.6314 1.0213 0 0:0:56 2100 266745 5859 103 24 88 0.0000 0.0506 0.0075 0.0149 0.9269 0.0049 0.5207 1.0177 0 0:0:50 2200 266088 6216 326 111 78 0.0000 0.0552 0.0329 0.1144 0.7975 0.0063 0.6319 1.0268 0 0:0:44 2300 266689 5551 475 10 94 0.0000 0.0491 0.0418 0.0053 0.9037 0.0060 0.6051 1.0202 0 0:0:39 2400 266393 5827 463 67 69 0.0000 0.0481 0.0394 0.0516 0.8609 0.0056 0.5699 1.0206 0 0:0:33 2500 266873 5674 194 3 75 0.0000 0.0513 0.0203 0.0017 0.9267 0.0050 0.5039 1.0202 0 0:0:28 2600 267238 5121 337 13 110 0.0000 0.0449 0.0272 0.0076 0.9203 0.0053 0.5645 1.0136 0 0:0:22 2700 267403 5089 202 51 74 0.0000 0.0400 0.0142 0.0515 0.8943 0.0053 0.5676 1.0168 0 0:0:16 2800 268292 4231 183 32 81 0.0000 0.0382 0.0169 0.0317 0.9132 0.0053 0.5009 1.0201 0 0:0:11 2900 268659 3847 127 102 84 0.0000 0.0300 0.0102 0.0693 0.8905 0.0064 0.6339 1.0127 0 0:0:5 3000 269590 2962 73 105 89 0.0000 0.0281 0.0067 0.1090 0.8561 0.0061 0.5701 1.0110 0 0:0:0 MCMC cycles completed. Posterior statistics from MCMC samples: Parameter Mean SD NumSnp1 267520.031250 1093.632446 NumSnp2 4945.390137 1037.244385 NumSnp3 217.995010 161.434113 NumSnp4 52.615005 41.300194 NumSnp5 82.980019 13.396121 Vg1 0.000000 0.000000 Vg2 0.042908 0.009999 Vg3 0.018774 0.013991 Vg4 0.044297 0.035876 Vg5 0.894021 0.040392 SigmaSq 0.005594 0.000493 hsq 0.553818 0.048600 ResVar 1.016600 0.003257 Ouput 7 skeptical SNPs in [sbayesr.skepticalSNPs], whose posterior joint effect sizes are remarkably greater than their marginal effect sizes. Since this may be due to poor convergence, their posterior effects will be set to be zero. Analysis finished: Sat Jun 6 20:36:10 2026 Computational time: 0:3:28