+-----------------------------+ | ___ | | BOLT-LMM, v2.3.1 /_ / | | December 19, 2017 /_/ | | Po-Ru Loh // | | / | +-----------------------------+ Copyright (C) 2014-2017 Harvard University. Distributed under the GNU GPLv3 open source license. Compiled with USE_SSE: fast aligned memory access Compiled with USE_MKL: Intel Math Kernel Library linear algebra Boost version: 1_58 Command line options: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt \ --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed \ --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim \ --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam \ --allowX \ --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt \ --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab \ --phenoCol=blood_EOSINOPHIL_COUNT \ --covarFile=/n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz \ --covarCol=cov_ASSESS_CENTER \ --covarCol=cov_GENO_ARRAY \ --covarCol=cov_SEX \ --covarMaxLevels=30 \ --qCovarCol=cov_AGE \ --qCovarCol=cov_AGE_SQ \ --qCovarCol=PC{1:20} \ --LDscoresFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/LDSCORE.1000G_EUR.tab.gz \ --geneticMapFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz \ --lmmForceNonInf \ --numThreads=8 \ --predBetasFile=bolt_460K_selfRepWhite.blood_EOSINOPHIL_COUNT.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_EOSINOPHIL_COUNT.stats.gz \ --verboseStats Setting number of threads to 8 fam: /n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam bim(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim bed(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed === Reading genotype data === Total indivs in PLINK data: Nbed = 488377 Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt Removed 976 individual(s) Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt WARNING: Unable to find individual to remove: FID IID WARNING: Unable to find individual to remove: 6023494 6023494 WARNING: Unable to find individual to remove: 6022857 6022857 WARNING: Unable to find individual to remove: 6020026 6020026 WARNING: Unable to find individual to remove: 6017119 6017119 Removed 28074 individual(s) WARNING: 1684 individual(s) not found in data set Total indivs stored in memory: N = 459327 Reading bim file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim Read 63487 snps Reading bim file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim Read 61966 snps Reading bim file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim Read 52300 snps Reading bim file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim Read 47443 snps Reading bim file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim Read 46314 snps Reading bim file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim Read 53695 snps Reading bim file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim Read 42722 snps Reading bim file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim Read 38591 snps Reading bim file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim Read 34310 snps Reading bim file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim Read 38308 snps Reading bim file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim Read 40824 snps Reading bim file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim Read 37302 snps Reading bim file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim Read 26806 snps Reading bim file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim Read 25509 snps Reading bim file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim Read 24467 snps Reading bim file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim Read 28960 snps Reading bim file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim Read 28835 snps Reading bim file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim Read 21962 snps Reading bim file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim Read 26186 snps Reading bim file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim Read 19959 snps Reading bim file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim Read 11342 snps Reading bim file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim Read 12968 snps Reading bim file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim Read 20214 snps Total snps in PLINK data: Mbed = 804470 Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt Excluded 73451 SNP(s) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89009265 WARNING: Unable to find SNP to exclude: Affx-89005343 WARNING: Unable to find SNP to exclude: Affx-89020964 WARNING: Unable to find SNP to exclude: Affx-89013736 Excluded 16710 SNP(s) WARNING: 342 SNP(s) not found in data set Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89022776 WARNING: Unable to find SNP to exclude: Affx-89017694 WARNING: Unable to find SNP to exclude: Affx-89018603 WARNING: Unable to find SNP to exclude: Affx-79443721 Excluded 8428 SNP(s) WARNING: 112 SNP(s) not found in data set Breakdown of SNP pre-filtering results: 705881 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 98589 excluded SNPs Filling in genetic map coordinates using reference file: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz Allocating 705881 x 459328/4 bytes to store genotypes Reading genotypes and performing QC filtering on snps and indivs... Reading bed file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed Expecting 7751445265 (+3) bytes for 488377 indivs, 63487 snps Reading bed file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed Expecting 7565738770 (+3) bytes for 488377 indivs, 61966 snps Reading bed file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed Expecting 6385568500 (+3) bytes for 488377 indivs, 52300 snps Reading bed file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed Expecting 5792553085 (+3) bytes for 488377 indivs, 47443 snps Reading bed file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed Expecting 5654707830 (+3) bytes for 488377 indivs, 46314 snps Reading bed file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed Expecting 6555891025 (+3) bytes for 488377 indivs, 53695 snps Reading bed file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed Expecting 5216142590 (+3) bytes for 488377 indivs, 42722 snps Reading bed file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed Expecting 4711768145 (+3) bytes for 488377 indivs, 38591 snps Reading bed file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed Expecting 4189079450 (+3) bytes for 488377 indivs, 34310 snps Reading bed file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed Expecting 4677215260 (+3) bytes for 488377 indivs, 38308 snps Reading bed file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed Expecting 4984406280 (+3) bytes for 488377 indivs, 40824 snps Reading bed file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed Expecting 4554387690 (+3) bytes for 488377 indivs, 37302 snps Reading bed file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed Expecting 3272878570 (+3) bytes for 488377 indivs, 26806 snps Reading bed file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed Expecting 3114521355 (+3) bytes for 488377 indivs, 25509 snps Reading bed file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed Expecting 2987298365 (+3) bytes for 488377 indivs, 24467 snps Reading bed file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed Expecting 3535871200 (+3) bytes for 488377 indivs, 28960 snps Reading bed file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed Expecting 3520609325 (+3) bytes for 488377 indivs, 28835 snps Reading bed file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed Expecting 2681450390 (+3) bytes for 488377 indivs, 21962 snps Reading bed file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed Expecting 3197179670 (+3) bytes for 488377 indivs, 26186 snps Reading bed file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed Expecting 2436894105 (+3) bytes for 488377 indivs, 19959 snps Reading bed file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed Expecting 1384801490 (+3) bytes for 488377 indivs, 11342 snps Reading bed file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed Expecting 1583327960 (+3) bytes for 488377 indivs, 12968 snps Reading bed file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed Expecting 2468028330 (+3) bytes for 488377 indivs, 20214 snps Total indivs after QC: 459327 Total post-QC SNPs: M = 705881 Variance component 1: 705881 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 2605.83 sec === Reading phenotype and covariate data === WARNING: Ignoring indiv not in genotype data: FID=6018780, IID=6018780 WARNING: Ignoring indiv not in genotype data: FID=6012488, IID=6012488 WARNING: Ignoring indiv not in genotype data: FID=5998913, IID=5998913 WARNING: Ignoring indiv not in genotype data: FID=5989416, IID=5989416 WARNING: Ignoring indiv not in genotype data: FID=5985954, IID=5985954 Read data for 460238 indivs (ignored 914 without genotypes) from: /n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz WARNING: Ignoring indiv not in genotype data: FID=1000129, IID=1000129 WARNING: Ignoring indiv not in genotype data: FID=1000170, IID=1000170 WARNING: Ignoring indiv not in genotype data: FID=1000224, IID=1000224 WARNING: Ignoring indiv not in genotype data: FID=1000362, IID=1000362 WARNING: Ignoring indiv not in genotype data: FID=1000379, IID=1000379 Read data for 502655 indivs (ignored 43331 without genotypes) from: /n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab Number of indivs with no missing phenotype(s) to use: 439938 NOTE: Using all-1s vector (constant term) in addition to specified covariates Using categorical covariate: cov_ASSESS_CENTER (adding level 10003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11001) Using categorical covariate: cov_ASSESS_CENTER (adding level 11002) Using categorical covariate: cov_ASSESS_CENTER (adding level 11003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11004) Using categorical covariate: cov_ASSESS_CENTER (adding level 11005) Using categorical covariate: cov_ASSESS_CENTER (adding level 11006) Using categorical covariate: cov_ASSESS_CENTER (adding level 11007) Using categorical covariate: cov_ASSESS_CENTER (adding level 11008) Using categorical covariate: cov_ASSESS_CENTER (adding level 11009) Using categorical covariate: cov_ASSESS_CENTER (adding level 11010) Using categorical covariate: cov_ASSESS_CENTER (adding level 11011) Using categorical covariate: cov_ASSESS_CENTER (adding level 11012) Using categorical covariate: cov_ASSESS_CENTER (adding level 11013) Using categorical covariate: cov_ASSESS_CENTER (adding level 11014) Using categorical covariate: cov_ASSESS_CENTER (adding level 11016) Using categorical covariate: cov_ASSESS_CENTER (adding level 11017) Using categorical covariate: cov_ASSESS_CENTER (adding level 11018) Using categorical covariate: cov_ASSESS_CENTER (adding level 11020) Using categorical covariate: cov_ASSESS_CENTER (adding level 11021) Using categorical covariate: cov_ASSESS_CENTER (adding level 11022) Using categorical covariate: cov_ASSESS_CENTER (adding level 11023) WARNING: Covariate cov_ASSESS_CENTER has a large number of distinct values (23) Should this covariate be quantitative rather than categorical? Using categorical covariate: cov_GENO_ARRAY (adding level 1) Using categorical covariate: cov_GENO_ARRAY (adding level 2) Using categorical covariate: cov_SEX (adding level 0) Using categorical covariate: cov_SEX (adding level 1) Using quantitative covariate: cov_AGE Using quantitative covariate: cov_AGE_SQ Using quantitative covariate: PC1 Using quantitative covariate: PC2 Using quantitative covariate: PC3 Using quantitative covariate: PC4 Using quantitative covariate: PC5 Using quantitative covariate: PC6 Using quantitative covariate: PC7 Using quantitative covariate: PC8 Using quantitative covariate: PC9 Using quantitative covariate: PC10 Using quantitative covariate: PC11 Using quantitative covariate: PC12 Using quantitative covariate: PC13 Using quantitative covariate: PC14 Using quantitative covariate: PC15 Using quantitative covariate: PC16 Using quantitative covariate: PC17 Using quantitative covariate: PC18 Using quantitative covariate: PC19 Using quantitative covariate: PC20 Using quantitative covariate: CONST_ALL_ONES Number of individuals used in analysis: Nused = 439938 Singular values of covariate matrix: S[0] = 2.25877e+06 S[1] = 4737.29 S[2] = 467.228 S[3] = 291.793 S[4] = 198.931 S[5] = 193.475 S[6] = 182.76 S[7] = 174.111 S[8] = 169.76 S[9] = 164.411 S[10] = 162.025 S[11] = 152.869 S[12] = 144.813 S[13] = 142.872 S[14] = 139.77 S[15] = 135.118 S[16] = 131.918 S[17] = 129.686 S[18] = 125.707 S[19] = 115.806 S[20] = 111.621 S[21] = 99.3407 S[22] = 44.6385 S[23] = 23.8501 S[24] = 19.2041 S[25] = 0.979075 S[26] = 0.978743 S[27] = 0.978491 S[28] = 0.978431 S[29] = 0.978186 S[30] = 0.978101 S[31] = 0.977992 S[32] = 0.97781 S[33] = 0.977757 S[34] = 0.977689 S[35] = 0.977509 S[36] = 0.977391 S[37] = 0.977196 S[38] = 0.977074 S[39] = 0.977032 S[40] = 0.976629 S[41] = 0.976467 S[42] = 0.976206 S[43] = 0.959347 S[44] = 0.883297 S[45] = 4.48441e-12 S[46] = 7.17861e-13 S[47] = 4.75739e-13 S[48] = 0 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 45 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 437374.734191 Dimension of all-1s proj space (Nused-1): 439937 Time for covariate data setup + Bolt initialization = 4419.93 sec Phenotype 1: N = 439938 mean = -0.003169 std = 0.99608 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 519.122 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 439938) Estimating MC scaling f_REML at log(delta) = 1.09287, h2 = 0.25... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=452.24 rNorms/orig: (0.6,0.7) res2s: 718514..149112 iter 2: time=388.29 rNorms/orig: (0.5,0.6) res2s: 855290..189660 iter 3: time=380.32 rNorms/orig: (0.3,0.4) res2s: 984916..224688 iter 4: time=390.10 rNorms/orig: (0.2,0.2) res2s: 1.02832e+06..241773 iter 5: time=379.88 rNorms/orig: (0.1,0.2) res2s: 1.05326e+06..248573 iter 6: time=403.53 rNorms/orig: (0.09,0.1) res2s: 1.06515e+06..252493 iter 7: time=408.80 rNorms/orig: (0.05,0.07) res2s: 1.0709e+06..254498 iter 8: time=404.93 rNorms/orig: (0.03,0.04) res2s: 1.0738e+06..255267 iter 9: time=408.75 rNorms/orig: (0.02,0.03) res2s: 1.07509e+06..255726 iter 10: time=423.57 rNorms/orig: (0.01,0.02) res2s: 1.07572e+06..255928 iter 11: time=483.02 rNorms/orig: (0.01,0.01) res2s: 1.07602e+06..256011 iter 12: time=560.42 rNorms/orig: (0.006,0.007) res2s: 1.07615e+06..256049 iter 13: time=540.72 rNorms/orig: (0.004,0.004) res2s: 1.0762e+06..256065 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 48.2%, memory/overhead = 51.8% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0481862 Estimating MC scaling f_REML at log(delta) = -0.00574117, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=414.07 rNorms/orig: (1,1) res2s: 76742.3..44688.5 iter 2: time=398.42 rNorms/orig: (1,1) res2s: 111296..68929.4 iter 3: time=402.23 rNorms/orig: (0.8,0.9) res2s: 168445..101462 iter 4: time=412.80 rNorms/orig: (0.6,0.7) res2s: 201693..126681 iter 5: time=488.34 rNorms/orig: (0.5,0.5) res2s: 229878..141267 iter 6: time=541.96 rNorms/orig: (0.4,0.4) res2s: 249568..153104 iter 7: time=552.44 rNorms/orig: (0.3,0.3) res2s: 263664..161922 iter 8: time=562.29 rNorms/orig: (0.2,0.3) res2s: 273883..166693 iter 9: time=570.94 rNorms/orig: (0.2,0.2) res2s: 280411..170734 iter 10: time=575.00 rNorms/orig: (0.1,0.2) res2s: 284908..173350 iter 11: time=558.84 rNorms/orig: (0.1,0.1) res2s: 288040..174917 iter 12: time=441.51 rNorms/orig: (0.08,0.09) res2s: 289993..175946 iter 13: time=435.55 rNorms/orig: (0.07,0.07) res2s: 291133..176596 iter 14: time=480.06 rNorms/orig: (0.05,0.05) res2s: 291973..176990 iter 15: time=519.95 rNorms/orig: (0.04,0.04) res2s: 292504..177264 iter 16: time=508.14 rNorms/orig: (0.03,0.03) res2s: 292830..177417 iter 17: time=413.15 rNorms/orig: (0.02,0.03) res2s: 293017..177515 iter 18: time=415.34 rNorms/orig: (0.02,0.02) res2s: 293164..177588 iter 19: time=402.99 rNorms/orig: (0.01,0.01) res2s: 293250..177628 iter 20: time=467.91 rNorms/orig: (0.01,0.01) res2s: 293303..177655 iter 21: time=466.88 rNorms/orig: (0.008,0.009) res2s: 293333..177672 iter 22: time=415.33 rNorms/orig: (0.006,0.007) res2s: 293353..177682 iter 23: time=433.41 rNorms/orig: (0.005,0.005) res2s: 293364..177688 iter 24: time=458.16 rNorms/orig: (0.004,0.004) res2s: 293372..177692 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.9%, memory/overhead = 53.1% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.265235 Estimating MC scaling f_REML at log(delta) = 0.923968, h2 = 0.282984... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=499.75 rNorms/orig: (0.7,0.8) res2s: 534985..128779 iter 2: time=527.99 rNorms/orig: (0.6,0.7) res2s: 656296..168593 iter 3: time=525.69 rNorms/orig: (0.4,0.4) res2s: 782722..205641 iter 4: time=513.42 rNorms/orig: (0.2,0.3) res2s: 828875..225189 iter 5: time=454.88 rNorms/orig: (0.2,0.2) res2s: 857144..233492 iter 6: time=454.37 rNorms/orig: (0.1,0.1) res2s: 871538..238586 iter 7: time=434.00 rNorms/orig: (0.08,0.09) res2s: 879001..241380 iter 8: time=390.19 rNorms/orig: (0.05,0.06) res2s: 883027..242522 iter 9: time=434.60 rNorms/orig: (0.03,0.04) res2s: 884935..243251 iter 10: time=431.32 rNorms/orig: (0.02,0.02) res2s: 885928..243596 iter 11: time=366.22 rNorms/orig: (0.02,0.02) res2s: 886434..243749 iter 12: time=423.03 rNorms/orig: (0.01,0.01) res2s: 886669..243823 iter 13: time=464.01 rNorms/orig: (0.007,0.007) res2s: 886770..243858 iter 14: time=479.95 rNorms/orig: (0.004,0.005) res2s: 886827..243874 iter 15: time=493.99 rNorms/orig: (0.003,0.003) res2s: 886853..243882 Converged at iter 15: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.7%, memory/overhead = 53.3% MCscaling: logDelta = 0.92, h2 = 0.283, f = 0.00108237 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.283 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.275525, logDelta = 0.923968, f = 0.00108237 Time for fitting variance components = 24707.1 sec === Computing mixed model assoc stats (inf. model) === Selected 30 SNPs for computation of prospective stat Tried 30; threw out 0 with GRAMMAR chisq > 5 Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Batch-solving 53 systems of equations using conjugate gradient iteration iter 1: time=812.82 rNorms/orig: (0.5,0.9) res2s: 127667..220934 iter 2: time=777.70 rNorms/orig: (0.5,0.7) res2s: 168489..258739 iter 3: time=799.33 rNorms/orig: (0.3,0.5) res2s: 206913..277364 iter 4: time=803.44 rNorms/orig: (0.2,0.3) res2s: 228128..285281 iter 5: time=805.43 rNorms/orig: (0.1,0.2) res2s: 237244..290368 iter 6: time=801.15 rNorms/orig: (0.08,0.2) res2s: 242887..292727 iter 7: time=792.12 rNorms/orig: (0.06,0.1) res2s: 246164..293986 iter 8: time=787.71 rNorms/orig: (0.04,0.07) res2s: 247520..294691 iter 9: time=792.60 rNorms/orig: (0.02,0.05) res2s: 248413..295007 iter 10: time=792.91 rNorms/orig: (0.02,0.03) res2s: 248852..295178 iter 11: time=817.92 rNorms/orig: (0.01,0.02) res2s: 249053..295261 iter 12: time=815.20 rNorms/orig: (0.007,0.01) res2s: 249152..295302 iter 13: time=817.00 rNorms/orig: (0.004,0.009) res2s: 249201..295321 iter 14: time=794.28 rNorms/orig: (0.003,0.006) res2s: 249223..295331 iter 15: time=795.91 rNorms/orig: (0.002,0.004) res2s: 249235..295335 iter 16: time=805.30 rNorms/orig: (0.001,0.003) res2s: 249240..295337 iter 17: time=785.36 rNorms/orig: (0.0007,0.002) res2s: 249243..295339 iter 18: time=747.29 rNorms/orig: (0.0004,0.001) res2s: 249244..295339 iter 19: time=742.98 rNorms/orig: (0.0003,0.0009) res2s: 249245..295339 iter 20: time=768.03 rNorms/orig: (0.0002,0.0006) res2s: 249245..295339 iter 21: time=770.62 rNorms/orig: (0.0001,0.0004) res2s: 249245..295339 Converged at iter 21: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 73.0%, memory/overhead = 27.0% AvgPro: 1.921 AvgRetro: 1.896 Calibration: 1.013 (0.001) (30 SNPs) Ratio of medians: 1.018 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 17110.3 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 19.5967 sec === Reading LD Scores for calibration of Bayesian assoc stats === Looking up LD Scores... Looking for column header 'SNP': column number = 1 Looking for column header 'LDSCORE': column number = 5 Found LD Scores for 601289/705881 SNPs Estimating inflation of LINREG chisq stats using MLMe as reference... Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 439.9) # of SNPs remaining after outlier window removal: 573910/579570 Intercept of LD Score regression for ref stats: 1.200 (0.020) Estimated attenuation: 0.196 (0.020) Intercept of LD Score regression for cur stats: 1.188 (0.018) Calibration factor (ref/cur) to multiply by: 1.011 (0.003) LINREG intercept inflation = 0.989594 NOTE: LINREG stats in output are NOT corrected for estimated inflation === Estimating mixture parameters by cross-validation === Setting maximum number of iterations to 250 for this step Max CV folds to compute = 1 (to have > 10000 samples) ====> Starting CV fold 1 <==== NOTE: Using all-1s vector (constant term) in addition to specified covariates Using categorical covariate: cov_ASSESS_CENTER (adding level 10003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11001) Using categorical covariate: cov_ASSESS_CENTER (adding level 11002) Using categorical covariate: cov_ASSESS_CENTER (adding level 11003) Using categorical covariate: cov_ASSESS_CENTER (adding level 11004) Using categorical covariate: cov_ASSESS_CENTER (adding level 11005) Using categorical covariate: cov_ASSESS_CENTER (adding level 11006) Using categorical covariate: cov_ASSESS_CENTER (adding level 11007) Using categorical covariate: cov_ASSESS_CENTER (adding level 11008) Using categorical covariate: cov_ASSESS_CENTER (adding level 11009) Using categorical covariate: cov_ASSESS_CENTER (adding level 11010) Using categorical covariate: cov_ASSESS_CENTER (adding level 11011) Using categorical covariate: cov_ASSESS_CENTER (adding level 11012) Using categorical covariate: cov_ASSESS_CENTER (adding level 11013) Using categorical covariate: cov_ASSESS_CENTER (adding level 11014) Using categorical covariate: cov_ASSESS_CENTER (adding level 11016) Using categorical covariate: cov_ASSESS_CENTER (adding level 11017) Using categorical covariate: cov_ASSESS_CENTER (adding level 11018) Using categorical covariate: cov_ASSESS_CENTER (adding level 11020) Using categorical covariate: cov_ASSESS_CENTER (adding level 11021) Using categorical covariate: cov_ASSESS_CENTER (adding level 11022) Using categorical covariate: cov_ASSESS_CENTER (adding level 11023) WARNING: Covariate cov_ASSESS_CENTER has a large number of distinct values (23) Should this covariate be quantitative rather than categorical? Using categorical covariate: cov_GENO_ARRAY (adding level 1) Using categorical covariate: cov_GENO_ARRAY (adding level 2) Using categorical covariate: cov_SEX (adding level 0) Using categorical covariate: cov_SEX (adding level 1) Using quantitative covariate: cov_AGE Using quantitative covariate: cov_AGE_SQ Using quantitative covariate: PC1 Using quantitative covariate: PC2 Using quantitative covariate: PC3 Using quantitative covariate: PC4 Using quantitative covariate: PC5 Using quantitative covariate: PC6 Using quantitative covariate: PC7 Using quantitative covariate: PC8 Using quantitative covariate: PC9 Using quantitative covariate: PC10 Using quantitative covariate: PC11 Using quantitative covariate: PC12 Using quantitative covariate: PC13 Using quantitative covariate: PC14 Using quantitative covariate: PC15 Using quantitative covariate: PC16 Using quantitative covariate: PC17 Using quantitative covariate: PC18 Using quantitative covariate: PC19 Using quantitative covariate: PC20 Using quantitative covariate: CONST_ALL_ONES Number of individuals used in analysis: Nused = 351950 Singular values of covariate matrix: S[0] = 2.02069e+06 S[1] = 4237.15 S[2] = 417.919 S[3] = 260.996 S[4] = 177.9 S[5] = 173.055 S[6] = 163.55 S[7] = 155.792 S[8] = 151.87 S[9] = 147.142 S[10] = 144.862 S[11] = 136.757 S[12] = 129.66 S[13] = 127.894 S[14] = 124.806 S[15] = 120.688 S[16] = 117.828 S[17] = 116.165 S[18] = 112.369 S[19] = 103.497 S[20] = 99.8458 S[21] = 88.772 S[22] = 40.0058 S[23] = 21.3431 S[24] = 17.1791 S[25] = 0.877774 S[26] = 0.877083 S[27] = 0.876791 S[28] = 0.876033 S[29] = 0.875577 S[30] = 0.875399 S[31] = 0.875341 S[32] = 0.875193 S[33] = 0.875045 S[34] = 0.8747 S[35] = 0.874162 S[36] = 0.873965 S[37] = 0.873798 S[38] = 0.873216 S[39] = 0.872972 S[40] = 0.872411 S[41] = 0.871615 S[42] = 0.870864 S[43] = 0.859992 S[44] = 0.790504 S[45] = 3.43594e-12 S[46] = 6.85154e-13 S[47] = 1.15697e-13 S[48] = 0 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 45 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 349889.345246 Dimension of all-1s proj space (Nused-1): 351949 Beginning variational Bayes iter 1: time=788.07 for 18 active reps iter 2: time=510.86 for 18 active reps approxLL diffs: (9438.04,12209.33) iter 3: time=503.17 for 18 active reps approxLL diffs: (898.42,2255.74) iter 4: time=509.11 for 18 active reps approxLL diffs: (168.13,909.76) iter 5: time=513.26 for 18 active reps approxLL diffs: (50.79,463.53) iter 6: time=505.76 for 18 active reps approxLL diffs: (20.48,239.46) iter 7: time=507.38 for 18 active reps approxLL diffs: (10.12,161.22) iter 8: time=510.71 for 18 active reps approxLL diffs: (5.79,106.62) iter 9: time=512.38 for 18 active reps approxLL diffs: (3.62,71.85) iter 10: time=519.51 for 18 active reps approxLL diffs: (2.47,55.44) iter 11: time=516.07 for 18 active reps approxLL diffs: (1.80,45.90) iter 12: time=516.48 for 18 active reps approxLL diffs: (1.34,35.70) iter 13: time=514.03 for 18 active reps approxLL diffs: (1.02,26.04) iter 14: time=515.69 for 18 active reps approxLL diffs: (0.79,26.33) iter 15: time=514.32 for 18 active reps approxLL diffs: (0.64,21.98) iter 16: time=516.69 for 18 active reps approxLL diffs: (0.51,16.22) iter 17: time=517.85 for 18 active reps approxLL diffs: (0.41,16.48) iter 18: time=516.32 for 18 active reps approxLL diffs: (0.32,11.83) iter 19: time=526.16 for 18 active reps approxLL diffs: (0.25,7.04) iter 20: time=522.06 for 18 active reps approxLL diffs: (0.20,7.07) iter 21: time=521.13 for 18 active reps approxLL diffs: (0.15,6.96) iter 22: time=552.72 for 18 active reps approxLL diffs: (0.12,5.36) iter 23: time=543.45 for 18 active reps approxLL diffs: (0.09,5.19) iter 24: time=519.29 for 18 active reps approxLL diffs: (0.07,4.79) iter 25: time=526.75 for 18 active reps approxLL diffs: (0.05,2.67) iter 26: time=539.38 for 18 active reps approxLL diffs: (0.04,2.88) iter 27: time=543.10 for 18 active reps approxLL diffs: (0.03,2.85) iter 28: time=543.07 for 18 active reps approxLL diffs: (0.03,2.14) iter 29: time=549.99 for 18 active reps approxLL diffs: (0.02,1.61) iter 30: time=537.26 for 18 active reps approxLL diffs: (0.02,3.00) iter 31: time=545.61 for 18 active reps approxLL diffs: (0.01,3.26) iter 32: time=563.64 for 18 active reps approxLL diffs: (0.01,3.64) iter 33: time=549.85 for 18 active reps approxLL diffs: (0.01,1.00) iter 34: time=539.27 for 17 active reps approxLL diffs: (0.01,0.51) iter 35: time=540.17 for 17 active reps approxLL diffs: (0.01,0.50) iter 36: time=514.10 for 17 active reps approxLL diffs: (0.01,1.34) iter 37: time=498.41 for 16 active reps approxLL diffs: (0.01,1.44) iter 38: time=513.54 for 15 active reps approxLL diffs: (0.01,1.33) iter 39: time=520.56 for 15 active reps approxLL diffs: (0.01,1.91) iter 40: time=476.30 for 13 active reps approxLL diffs: (0.01,1.47) iter 41: time=485.87 for 13 active reps approxLL diffs: (0.01,0.85) iter 42: time=465.86 for 12 active reps approxLL diffs: (0.01,0.71) iter 43: time=481.92 for 11 active reps approxLL diffs: (0.01,1.69) iter 44: time=486.56 for 11 active reps approxLL diffs: (0.02,1.17) iter 45: time=488.73 for 11 active reps approxLL diffs: (0.02,3.13) iter 46: time=497.93 for 11 active reps approxLL diffs: (0.01,3.44) iter 47: time=494.75 for 11 active reps approxLL diffs: (0.01,3.11) iter 48: time=491.13 for 11 active reps approxLL diffs: (0.01,1.26) iter 49: time=487.32 for 11 active reps approxLL diffs: (0.01,2.27) iter 50: time=457.32 for 10 active reps approxLL diffs: (0.02,2.69) iter 51: time=466.73 for 10 active reps approxLL diffs: (0.01,0.23) iter 52: time=459.65 for 10 active reps approxLL diffs: (0.01,0.28) iter 53: time=443.19 for 9 active reps approxLL diffs: (0.01,0.11) iter 54: time=451.57 for 8 active reps approxLL diffs: (0.01,0.09) iter 55: time=440.89 for 7 active reps approxLL diffs: (0.01,0.12) iter 56: time=325.05 for 6 active reps approxLL diffs: (0.01,0.22) iter 57: time=316.24 for 5 active reps approxLL diffs: (0.01,0.26) iter 58: time=322.85 for 5 active reps approxLL diffs: (0.01,0.21) iter 59: time=311.83 for 5 active reps approxLL diffs: (0.01,0.13) iter 60: time=300.00 for 4 active reps approxLL diffs: (0.02,0.29) iter 61: time=306.05 for 4 active reps approxLL diffs: (0.02,0.66) iter 62: time=304.45 for 4 active reps approxLL diffs: (0.02,0.48) iter 63: time=304.01 for 4 active reps approxLL diffs: (0.01,0.15) iter 64: time=324.28 for 3 active reps approxLL diffs: (0.02,0.15) iter 65: time=308.91 for 3 active reps approxLL diffs: (0.02,0.19) iter 66: time=292.37 for 3 active reps approxLL diffs: (0.01,0.19) iter 67: time=296.23 for 3 active reps approxLL diffs: (0.01,0.19) iter 68: time=283.01 for 2 active reps approxLL diffs: (0.09,0.22) iter 69: time=272.43 for 2 active reps approxLL diffs: (0.14,0.35) iter 70: time=277.92 for 2 active reps approxLL diffs: (0.19,0.73) iter 71: time=279.55 for 2 active reps approxLL diffs: (0.14,0.80) iter 72: time=281.86 for 2 active reps approxLL diffs: (0.05,0.21) iter 73: time=296.44 for 2 active reps approxLL diffs: (0.02,0.03) iter 74: time=282.34 for 2 active reps approxLL diffs: (0.01,0.02) iter 75: time=271.13 for 1 active reps approxLL diffs: (0.03,0.03) iter 76: time=275.40 for 1 active reps approxLL diffs: (0.08,0.08) iter 77: time=248.95 for 1 active reps approxLL diffs: (0.20,0.20) iter 78: time=239.76 for 1 active reps approxLL diffs: (0.26,0.26) iter 79: time=241.20 for 1 active reps approxLL diffs: (0.14,0.14) iter 80: time=239.85 for 1 active reps approxLL diffs: (0.04,0.04) iter 81: time=255.18 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 81: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 71.6%, memory/overhead = 28.4% Computing predictions on left-out cross-validation fold Time for computing predictions = 9300.53 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.02: 0.157224 f2=0.5, p=0.02: 0.156965 f2=0.5, p=0.01: 0.156783 ... f2=0.5, p=0.5: 0.108483 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.992562 Absolute prediction MSE using standard LMM: 0.884886 Absolute prediction MSE, fold-best f2=0.3, p=0.02: 0.836508 Absolute pred MSE using f2=0.5, p=0.5: 0.884886 Absolute pred MSE using f2=0.5, p=0.2: 0.869109 Absolute pred MSE using f2=0.5, p=0.1: 0.854671 Absolute pred MSE using f2=0.5, p=0.05: 0.843359 Absolute pred MSE using f2=0.5, p=0.02: 0.836764 Absolute pred MSE using f2=0.5, p=0.01: 0.836945 Absolute pred MSE using f2=0.3, p=0.5: 0.879602 Absolute pred MSE using f2=0.3, p=0.2: 0.859107 Absolute pred MSE using f2=0.3, p=0.1: 0.845578 Absolute pred MSE using f2=0.3, p=0.05: 0.837638 Absolute pred MSE using f2=0.3, p=0.02: 0.836508 Absolute pred MSE using f2=0.3, p=0.01: 0.838388 Absolute pred MSE using f2=0.1, p=0.5: 0.872191 Absolute pred MSE using f2=0.1, p=0.2: 0.851211 Absolute pred MSE using f2=0.1, p=0.1: 0.840691 Absolute pred MSE using f2=0.1, p=0.05: 0.837583 Absolute pred MSE using f2=0.1, p=0.02: 0.842016 Absolute pred MSE using f2=0.1, p=0.01: 0.844523 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.108 Relative improvement in prediction MSE using non-inf model: 0.055 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.02 Time for estimating mixture parameters = 51582 sec === Computing Bayesian mixed model assoc stats with mixture prior === Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Beginning variational Bayes iter 1: time=843.04 for 23 active reps iter 2: time=594.86 for 23 active reps approxLL diffs: (13849.96,14974.92) iter 3: time=598.07 for 23 active reps approxLL diffs: (2632.78,2914.10) iter 4: time=607.55 for 23 active reps approxLL diffs: (944.74,1068.61) iter 5: time=617.50 for 23 active reps approxLL diffs: (472.20,550.23) iter 6: time=605.00 for 23 active reps approxLL diffs: (272.43,323.47) iter 7: time=614.12 for 23 active reps approxLL diffs: (181.62,209.37) iter 8: time=616.51 for 23 active reps approxLL diffs: (118.04,138.24) iter 9: time=593.40 for 23 active reps approxLL diffs: (76.97,91.50) iter 10: time=600.15 for 23 active reps approxLL diffs: (49.59,61.94) iter 11: time=602.13 for 23 active reps approxLL diffs: (29.24,44.85) iter 12: time=604.54 for 23 active reps approxLL diffs: (24.17,36.13) iter 13: time=599.48 for 23 active reps approxLL diffs: (26.15,37.59) iter 14: time=600.94 for 23 active reps approxLL diffs: (19.84,29.71) iter 15: time=599.80 for 23 active reps approxLL diffs: (16.32,24.43) iter 16: time=610.81 for 23 active reps approxLL diffs: (13.27,26.56) iter 17: time=605.04 for 23 active reps approxLL diffs: (13.38,20.62) iter 18: time=611.87 for 23 active reps approxLL diffs: (9.49,16.51) iter 19: time=605.99 for 23 active reps approxLL diffs: (7.03,14.74) iter 20: time=596.48 for 23 active reps approxLL diffs: (4.88,15.70) iter 21: time=588.57 for 23 active reps approxLL diffs: (5.14,11.28) iter 22: time=585.38 for 23 active reps approxLL diffs: (2.94,10.67) iter 23: time=587.29 for 23 active reps approxLL diffs: (1.93,8.57) iter 24: time=588.86 for 23 active reps approxLL diffs: (1.69,7.01) iter 25: time=618.61 for 23 active reps approxLL diffs: (1.23,5.16) iter 26: time=613.49 for 23 active reps approxLL diffs: (0.57,5.95) iter 27: time=610.07 for 23 active reps approxLL diffs: (0.49,5.57) iter 28: time=604.69 for 23 active reps approxLL diffs: (0.65,6.82) iter 29: time=584.59 for 23 active reps approxLL diffs: (0.46,6.61) iter 30: time=564.45 for 23 active reps approxLL diffs: (0.44,3.40) iter 31: time=556.19 for 23 active reps approxLL diffs: (0.31,3.82) iter 32: time=555.79 for 23 active reps approxLL diffs: (0.31,2.57) iter 33: time=552.35 for 23 active reps approxLL diffs: (0.37,5.77) iter 34: time=558.44 for 23 active reps approxLL diffs: (0.46,3.87) iter 35: time=565.56 for 23 active reps approxLL diffs: (0.32,2.84) iter 36: time=554.31 for 23 active reps approxLL diffs: (0.15,5.27) iter 37: time=550.51 for 23 active reps approxLL diffs: (0.07,2.51) iter 38: time=557.75 for 23 active reps approxLL diffs: (0.02,2.48) iter 39: time=560.52 for 23 active reps approxLL diffs: (0.02,5.62) iter 40: time=560.59 for 23 active reps approxLL diffs: (0.03,6.31) iter 41: time=560.15 for 23 active reps approxLL diffs: (0.03,3.71) iter 42: time=572.96 for 23 active reps approxLL diffs: (0.02,2.14) iter 43: time=553.86 for 23 active reps approxLL diffs: (0.03,2.45) iter 44: time=542.41 for 23 active reps approxLL diffs: (0.03,1.11) iter 45: time=578.68 for 23 active reps approxLL diffs: (0.03,1.16) iter 46: time=588.37 for 23 active reps approxLL diffs: (0.03,1.19) iter 47: time=585.20 for 23 active reps approxLL diffs: (0.04,1.51) iter 48: time=587.44 for 23 active reps approxLL diffs: (0.03,1.77) iter 49: time=595.85 for 23 active reps approxLL diffs: (0.02,2.92) iter 50: time=617.31 for 23 active reps approxLL diffs: (0.02,3.52) iter 51: time=671.45 for 23 active reps approxLL diffs: (0.02,4.51) iter 52: time=663.90 for 23 active reps approxLL diffs: (0.02,2.25) iter 53: time=657.47 for 23 active reps approxLL diffs: (0.01,0.87) iter 54: time=669.60 for 23 active reps approxLL diffs: (0.01,1.03) iter 55: time=590.84 for 22 active reps approxLL diffs: (0.01,1.20) iter 56: time=511.74 for 22 active reps approxLL diffs: (0.00,1.20) iter 57: time=554.72 for 20 active reps approxLL diffs: (0.00,0.94) iter 58: time=588.18 for 18 active reps approxLL diffs: (0.02,0.65) iter 59: time=594.10 for 18 active reps approxLL diffs: (0.01,0.55) iter 60: time=585.93 for 18 active reps approxLL diffs: (0.01,0.71) iter 61: time=571.95 for 17 active reps approxLL diffs: (0.01,1.27) iter 62: time=543.26 for 16 active reps approxLL diffs: (0.01,0.92) iter 63: time=549.36 for 16 active reps approxLL diffs: (0.01,0.95) iter 64: time=563.73 for 15 active reps approxLL diffs: (0.02,2.23) iter 65: time=555.19 for 15 active reps approxLL diffs: (0.01,1.62) iter 66: time=510.79 for 13 active reps approxLL diffs: (0.01,2.06) iter 67: time=488.70 for 12 active reps approxLL diffs: (0.01,0.67) iter 68: time=518.06 for 11 active reps approxLL diffs: (0.01,0.40) iter 69: time=527.65 for 10 active reps approxLL diffs: (0.01,1.20) iter 70: time=475.54 for 9 active reps approxLL diffs: (0.01,1.11) iter 71: time=439.72 for 8 active reps approxLL diffs: (0.00,0.51) iter 72: time=423.64 for 5 active reps approxLL diffs: (0.01,0.15) iter 73: time=425.86 for 5 active reps approxLL diffs: (0.01,0.05) iter 74: time=415.64 for 5 active reps approxLL diffs: (0.01,0.07) iter 75: time=405.45 for 4 active reps approxLL diffs: (0.02,0.11) iter 76: time=397.87 for 4 active reps approxLL diffs: (0.02,0.20) iter 77: time=389.35 for 4 active reps approxLL diffs: (0.02,0.44) iter 78: time=390.99 for 4 active reps approxLL diffs: (0.01,0.84) iter 79: time=415.69 for 3 active reps approxLL diffs: (0.04,0.61) iter 80: time=392.44 for 3 active reps approxLL diffs: (0.01,0.38) iter 81: time=398.34 for 3 active reps approxLL diffs: (0.00,0.18) iter 82: time=375.05 for 2 active reps approxLL diffs: (0.09,0.26) iter 83: time=292.05 for 2 active reps approxLL diffs: (0.05,0.33) iter 84: time=294.11 for 2 active reps approxLL diffs: (0.04,0.18) iter 85: time=292.14 for 2 active reps approxLL diffs: (0.04,0.08) iter 86: time=290.31 for 2 active reps approxLL diffs: (0.06,0.10) iter 87: time=291.03 for 2 active reps approxLL diffs: (0.16,0.23) iter 88: time=291.95 for 2 active reps approxLL diffs: (0.46,0.57) iter 89: time=294.77 for 2 active reps approxLL diffs: (0.45,0.88) iter 90: time=292.07 for 2 active reps approxLL diffs: (0.17,0.18) iter 91: time=290.31 for 2 active reps approxLL diffs: (0.03,0.04) iter 92: time=293.09 for 2 active reps approxLL diffs: (0.01,0.04) iter 93: time=253.54 for 1 active reps approxLL diffs: (0.04,0.04) iter 94: time=252.31 for 1 active reps approxLL diffs: (0.04,0.04) iter 95: time=251.00 for 1 active reps approxLL diffs: (0.03,0.03) iter 96: time=252.14 for 1 active reps approxLL diffs: (0.02,0.02) iter 97: time=255.38 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 97: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 73.4%, memory/overhead = 26.6% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 439.9) # of SNPs remaining after outlier window removal: 573910/579570 Intercept of LD Score regression for ref stats: 1.200 (0.020) Estimated attenuation: 0.196 (0.020) Intercept of LD Score regression for cur stats: 1.199 (0.021) Calibration factor (ref/cur) to multiply by: 1.002 (0.002) Time for computing Bayesian mixed model assoc stats = 50513.6 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=500.53 for 1 active reps iter 2: time=264.42 for 1 active reps approxLL diffs: (15111.44,15111.44) iter 3: time=264.39 for 1 active reps approxLL diffs: (2924.23,2924.23) iter 4: time=263.96 for 1 active reps approxLL diffs: (1070.85,1070.85) iter 5: time=266.10 for 1 active reps approxLL diffs: (548.31,548.31) iter 6: time=280.43 for 1 active reps approxLL diffs: (311.14,311.14) iter 7: time=282.92 for 1 active reps approxLL diffs: (196.26,196.26) iter 8: time=280.55 for 1 active reps approxLL diffs: (131.69,131.69) iter 9: time=280.73 for 1 active reps approxLL diffs: (86.95,86.95) iter 10: time=256.89 for 1 active reps approxLL diffs: (54.76,54.76) iter 11: time=233.97 for 1 active reps approxLL diffs: (32.37,32.37) iter 12: time=265.87 for 1 active reps approxLL diffs: (28.65,28.65) iter 13: time=265.16 for 1 active reps approxLL diffs: (31.55,31.55) iter 14: time=330.44 for 1 active reps approxLL diffs: (24.65,24.65) iter 15: time=303.36 for 1 active reps approxLL diffs: (20.60,20.60) iter 16: time=331.68 for 1 active reps approxLL diffs: (18.38,18.38) iter 17: time=317.96 for 1 active reps approxLL diffs: (15.24,15.24) iter 18: time=333.26 for 1 active reps approxLL diffs: (11.96,11.96) iter 19: time=329.61 for 1 active reps approxLL diffs: (8.47,8.47) iter 20: time=303.52 for 1 active reps approxLL diffs: (10.19,10.19) iter 21: time=317.87 for 1 active reps approxLL diffs: (8.59,8.59) iter 22: time=320.15 for 1 active reps approxLL diffs: (5.81,5.81) iter 23: time=308.25 for 1 active reps approxLL diffs: (4.30,4.30) iter 24: time=320.49 for 1 active reps approxLL diffs: (3.08,3.08) iter 25: time=313.28 for 1 active reps approxLL diffs: (3.20,3.20) iter 26: time=311.43 for 1 active reps approxLL diffs: (3.01,3.01) iter 27: time=302.85 for 1 active reps approxLL diffs: (1.84,1.84) iter 28: time=302.64 for 1 active reps approxLL diffs: (1.87,1.87) iter 29: time=298.90 for 1 active reps approxLL diffs: (1.62,1.62) iter 30: time=326.66 for 1 active reps approxLL diffs: (1.77,1.77) iter 31: time=309.17 for 1 active reps approxLL diffs: (1.99,1.99) iter 32: time=321.97 for 1 active reps approxLL diffs: (2.03,2.03) iter 33: time=319.80 for 1 active reps approxLL diffs: (0.79,0.79) iter 34: time=311.39 for 1 active reps approxLL diffs: (0.30,0.30) iter 35: time=311.71 for 1 active reps approxLL diffs: (0.19,0.19) iter 36: time=311.56 for 1 active reps approxLL diffs: (0.19,0.19) iter 37: time=303.01 for 1 active reps approxLL diffs: (0.37,0.37) iter 38: time=321.34 for 1 active reps approxLL diffs: (0.75,0.75) iter 39: time=316.23 for 1 active reps approxLL diffs: (0.76,0.76) iter 40: time=317.66 for 1 active reps approxLL diffs: (0.88,0.88) iter 41: time=298.61 for 1 active reps approxLL diffs: (1.29,1.29) iter 42: time=296.47 for 1 active reps approxLL diffs: (0.97,0.97) iter 43: time=290.23 for 1 active reps approxLL diffs: (0.47,0.47) iter 44: time=287.85 for 1 active reps approxLL diffs: (0.16,0.16) iter 45: time=291.99 for 1 active reps approxLL diffs: (0.06,0.06) iter 46: time=288.61 for 1 active reps approxLL diffs: (0.04,0.04) iter 47: time=299.80 for 1 active reps approxLL diffs: (0.04,0.04) iter 48: time=318.93 for 1 active reps approxLL diffs: (0.05,0.05) iter 49: time=295.49 for 1 active reps approxLL diffs: (0.07,0.07) iter 50: time=297.31 for 1 active reps approxLL diffs: (0.11,0.11) iter 51: time=292.77 for 1 active reps approxLL diffs: (0.29,0.29) iter 52: time=296.80 for 1 active reps approxLL diffs: (0.56,0.56) iter 53: time=310.20 for 1 active reps approxLL diffs: (0.21,0.21) iter 54: time=309.73 for 1 active reps approxLL diffs: (0.03,0.03) iter 55: time=303.69 for 1 active reps approxLL diffs: (0.01,0.01) iter 56: time=304.27 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 56: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 56.8%, memory/overhead = 43.2% Time for computing and writing betas = 17009.3 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.16538 (705850 good SNPs) lambdaGC: 1.40795 Mean BOLT_LMM_INF: 2.28282 (705850 good SNPs) lambdaGC: 1.4295 Mean BOLT_LMM: 2.35461 (705850 good SNPs) lambdaGC: 1.43316 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 9430.66 sec Total elapsed time for analysis = 177918 sec