+-----------------------------+ | ___ | | 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_MEAN_SPHERED_CELL_VOL \ --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_MEAN_SPHERED_CELL_VOL.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_MEAN_SPHERED_CELL_VOL.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 = 2922.88 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: 437542 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 = 437542 Singular values of covariate matrix: S[0] = 2.25234e+06 S[1] = 4726.7 S[2] = 466.026 S[3] = 291.388 S[4] = 199.052 S[5] = 193.558 S[6] = 183.033 S[7] = 174.299 S[8] = 169.441 S[9] = 163.114 S[10] = 161.397 S[11] = 151.515 S[12] = 142.368 S[13] = 139.728 S[14] = 138.774 S[15] = 135.088 S[16] = 132.412 S[17] = 130.196 S[18] = 126.131 S[19] = 116.061 S[20] = 112.022 S[21] = 99.7184 S[22] = 43.1683 S[23] = 23.9789 S[24] = 19.1576 S[25] = 0.976093 S[26] = 0.975937 S[27] = 0.975752 S[28] = 0.975708 S[29] = 0.975592 S[30] = 0.975504 S[31] = 0.975394 S[32] = 0.975283 S[33] = 0.975146 S[34] = 0.97502 S[35] = 0.974866 S[36] = 0.974715 S[37] = 0.974575 S[38] = 0.974359 S[39] = 0.974262 S[40] = 0.974083 S[41] = 0.973896 S[42] = 0.973797 S[43] = 0.955288 S[44] = 0.88075 S[45] = 5.97891e-12 S[46] = 6.48596e-13 S[47] = 2.48994e-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: 434992.674881 Dimension of all-1s proj space (Nused-1): 437541 Time for covariate data setup + Bolt initialization = 3996.03 sec Phenotype 1: N = 437542 mean = 0.00744625 std = 0.988839 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 417.63 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 437542) 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=318.07 rNorms/orig: (0.6,0.7) res2s: 716449..139562 iter 2: time=310.15 rNorms/orig: (0.5,0.6) res2s: 854943..177247 iter 3: time=296.42 rNorms/orig: (0.3,0.3) res2s: 981371..216995 iter 4: time=290.94 rNorms/orig: (0.2,0.2) res2s: 1.02419e+06..230858 iter 5: time=289.54 rNorms/orig: (0.1,0.2) res2s: 1.04859e+06..236622 iter 6: time=284.99 rNorms/orig: (0.09,0.09) res2s: 1.06051e+06..240460 iter 7: time=284.88 rNorms/orig: (0.06,0.06) res2s: 1.0663e+06..242081 iter 8: time=289.54 rNorms/orig: (0.04,0.04) res2s: 1.06922e+06..242793 iter 9: time=293.58 rNorms/orig: (0.02,0.03) res2s: 1.0705e+06..243133 iter 10: time=386.15 rNorms/orig: (0.01,0.02) res2s: 1.07109e+06..243313 iter 11: time=457.42 rNorms/orig: (0.009,0.01) res2s: 1.07137e+06..243388 iter 12: time=420.46 rNorms/orig: (0.006,0.007) res2s: 1.0715e+06..243421 iter 13: time=386.12 rNorms/orig: (0.004,0.004) res2s: 1.07155e+06..243435 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 49.3%, memory/overhead = 50.7% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.10827 Estimating MC scaling f_REML at log(delta) = -0.00574066, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=340.25 rNorms/orig: (1,1) res2s: 76743.3..40547.1 iter 2: time=318.46 rNorms/orig: (1,1) res2s: 111812..62022.7 iter 3: time=304.10 rNorms/orig: (0.8,0.8) res2s: 168108..99229.8 iter 4: time=307.44 rNorms/orig: (0.6,0.7) res2s: 201061..120582 iter 5: time=308.84 rNorms/orig: (0.5,0.6) res2s: 228781..133273 iter 6: time=316.16 rNorms/orig: (0.4,0.4) res2s: 248452..145320 iter 7: time=302.25 rNorms/orig: (0.3,0.3) res2s: 262506..152796 iter 8: time=308.86 rNorms/orig: (0.2,0.3) res2s: 272690..157490 iter 9: time=308.36 rNorms/orig: (0.2,0.2) res2s: 279189..160648 iter 10: time=312.64 rNorms/orig: (0.1,0.2) res2s: 283544..163029 iter 11: time=321.14 rNorms/orig: (0.1,0.1) res2s: 286572..164481 iter 12: time=329.52 rNorms/orig: (0.08,0.09) res2s: 288515..165400 iter 13: time=327.54 rNorms/orig: (0.07,0.07) res2s: 289627..165953 iter 14: time=329.73 rNorms/orig: (0.05,0.05) res2s: 290430..166363 iter 15: time=321.35 rNorms/orig: (0.04,0.04) res2s: 290954..166610 iter 16: time=309.76 rNorms/orig: (0.03,0.03) res2s: 291270..166755 iter 17: time=302.65 rNorms/orig: (0.02,0.03) res2s: 291449..166858 iter 18: time=309.84 rNorms/orig: (0.02,0.02) res2s: 291588..166923 iter 19: time=313.20 rNorms/orig: (0.01,0.01) res2s: 291672..166960 iter 20: time=307.44 rNorms/orig: (0.01,0.01) res2s: 291725..166985 iter 21: time=316.40 rNorms/orig: (0.008,0.009) res2s: 291754..166999 iter 22: time=309.72 rNorms/orig: (0.006,0.007) res2s: 291773..167009 iter 23: time=316.76 rNorms/orig: (0.005,0.005) res2s: 291784..167015 iter 24: time=319.78 rNorms/orig: (0.004,0.004) res2s: 291791..167018 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.1%, memory/overhead = 49.9% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.222722 Estimating MC scaling f_REML at log(delta) = 0.733507, h2 = 0.323169... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=317.59 rNorms/orig: (0.7,0.8) res2s: 374177..99401.8 iter 2: time=319.23 rNorms/orig: (0.7,0.8) res2s: 477367..134175 iter 3: time=316.51 rNorms/orig: (0.5,0.5) res2s: 592664..177860 iter 4: time=324.11 rNorms/orig: (0.3,0.3) res2s: 639517..196021 iter 5: time=335.31 rNorms/orig: (0.2,0.3) res2s: 670019..204617 iter 6: time=330.36 rNorms/orig: (0.2,0.2) res2s: 687026..211147 iter 7: time=327.89 rNorms/orig: (0.1,0.1) res2s: 696532..214337 iter 8: time=319.95 rNorms/orig: (0.07,0.08) res2s: 702007..215942 iter 9: time=312.76 rNorms/orig: (0.05,0.06) res2s: 704779..216818 iter 10: time=306.02 rNorms/orig: (0.04,0.04) res2s: 706249..217349 iter 11: time=312.55 rNorms/orig: (0.02,0.03) res2s: 707060..217605 iter 12: time=329.52 rNorms/orig: (0.02,0.02) res2s: 707481..217734 iter 13: time=323.61 rNorms/orig: (0.01,0.01) res2s: 707668..217797 iter 14: time=319.56 rNorms/orig: (0.008,0.009) res2s: 707777..217835 iter 15: time=316.47 rNorms/orig: (0.006,0.006) res2s: 707834..217853 iter 16: time=325.78 rNorms/orig: (0.004,0.004) res2s: 707862..217861 Converged at iter 16: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.2%, memory/overhead = 49.8% MCscaling: logDelta = 0.73, h2 = 0.323, f = 0.00142548 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.323 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.308917, logDelta = 0.733507, f = 0.00142548 Time for fitting variance components = 17680 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=671.66 rNorms/orig: (0.6,0.9) res2s: 102184..195127 iter 2: time=661.42 rNorms/orig: (0.6,0.9) res2s: 137381..235230 iter 3: time=682.91 rNorms/orig: (0.3,0.5) res2s: 182647..256183 iter 4: time=658.70 rNorms/orig: (0.2,0.4) res2s: 202258..265867 iter 5: time=657.48 rNorms/orig: (0.2,0.3) res2s: 211549..272397 iter 6: time=663.65 rNorms/orig: (0.1,0.2) res2s: 218931..275594 iter 7: time=660.71 rNorms/orig: (0.08,0.1) res2s: 222598..277417 iter 8: time=646.85 rNorms/orig: (0.06,0.09) res2s: 224496..278479 iter 9: time=653.28 rNorms/orig: (0.03,0.07) res2s: 225533..279014 iter 10: time=655.88 rNorms/orig: (0.03,0.04) res2s: 226202..279312 iter 11: time=680.27 rNorms/orig: (0.02,0.03) res2s: 226527..279471 iter 12: time=666.08 rNorms/orig: (0.01,0.02) res2s: 226697..279556 iter 13: time=677.82 rNorms/orig: (0.008,0.02) res2s: 226781..279598 iter 14: time=722.07 rNorms/orig: (0.005,0.01) res2s: 226832..279621 iter 15: time=675.12 rNorms/orig: (0.003,0.008) res2s: 226858..279632 iter 16: time=639.17 rNorms/orig: (0.002,0.006) res2s: 226870..279638 iter 17: time=643.06 rNorms/orig: (0.001,0.004) res2s: 226878..279641 iter 18: time=695.20 rNorms/orig: (0.0009,0.003) res2s: 226881..279643 iter 19: time=732.51 rNorms/orig: (0.0006,0.002) res2s: 226883..279643 iter 20: time=732.85 rNorms/orig: (0.0004,0.001) res2s: 226884..279644 iter 21: time=672.11 rNorms/orig: (0.0003,0.0008) res2s: 226884..279644 iter 22: time=646.77 rNorms/orig: (0.0002,0.0006) res2s: 226885..279644 iter 23: time=643.93 rNorms/orig: (0.0001,0.0004) res2s: 226885..279644 Converged at iter 23: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 75.0%, memory/overhead = 25.0% AvgPro: 2.135 AvgRetro: 2.094 Calibration: 1.019 (0.005) (30 SNPs) Ratio of medians: 1.014 Median of ratios: 1.013 Time for computing infinitesimal model assoc stats = 15861.2 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 15.5093 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 > 437.5) # of SNPs remaining after outlier window removal: 569587/579570 Intercept of LD Score regression for ref stats: 1.156 (0.018) Estimated attenuation: 0.144 (0.019) Intercept of LD Score regression for cur stats: 1.158 (0.016) Calibration factor (ref/cur) to multiply by: 0.998 (0.003) LINREG intercept inflation = 1.00177 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 = 350033 Singular values of covariate matrix: S[0] = 2.01427e+06 S[1] = 4229.04 S[2] = 416.83 S[3] = 260.616 S[4] = 178.074 S[5] = 173.114 S[6] = 163.659 S[7] = 155.851 S[8] = 151.49 S[9] = 145.817 S[10] = 144.395 S[11] = 135.525 S[12] = 127.306 S[13] = 125.017 S[14] = 124.125 S[15] = 121.001 S[16] = 118.539 S[17] = 116.448 S[18] = 112.841 S[19] = 103.682 S[20] = 100.187 S[21] = 89.2529 S[22] = 38.5086 S[23] = 21.3646 S[24] = 17.1353 S[25] = 0.875054 S[26] = 0.874113 S[27] = 0.873907 S[28] = 0.873571 S[29] = 0.873239 S[30] = 0.872947 S[31] = 0.872871 S[32] = 0.872705 S[33] = 0.872205 S[34] = 0.871976 S[35] = 0.871846 S[36] = 0.871429 S[37] = 0.871162 S[38] = 0.871077 S[39] = 0.870218 S[40] = 0.869734 S[41] = 0.869592 S[42] = 0.869367 S[43] = 0.85047 S[44] = 0.787403 S[45] = 6.50869e-12 S[46] = 5.9725e-13 S[47] = 2.85389e-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: 347985.729129 Dimension of all-1s proj space (Nused-1): 350032 Beginning variational Bayes iter 1: time=665.11 for 18 active reps iter 2: time=434.35 for 18 active reps approxLL diffs: (13095.08,16418.42) iter 3: time=455.74 for 18 active reps approxLL diffs: (1333.83,3291.67) iter 4: time=448.89 for 18 active reps approxLL diffs: (256.72,1239.03) iter 5: time=446.07 for 18 active reps approxLL diffs: (74.22,673.66) iter 6: time=435.82 for 18 active reps approxLL diffs: (27.78,424.94) iter 7: time=429.09 for 18 active reps approxLL diffs: (12.95,252.38) iter 8: time=416.88 for 18 active reps approxLL diffs: (7.11,178.15) iter 9: time=421.00 for 18 active reps approxLL diffs: (4.40,125.24) iter 10: time=420.00 for 18 active reps approxLL diffs: (3.00,97.66) iter 11: time=422.10 for 18 active reps approxLL diffs: (2.17,69.36) iter 12: time=468.20 for 18 active reps approxLL diffs: (1.63,55.81) iter 13: time=428.93 for 18 active reps approxLL diffs: (1.25,38.55) iter 14: time=432.19 for 18 active reps approxLL diffs: (0.99,30.97) iter 15: time=427.06 for 18 active reps approxLL diffs: (0.80,33.31) iter 16: time=429.47 for 18 active reps approxLL diffs: (0.66,30.35) iter 17: time=426.33 for 18 active reps approxLL diffs: (0.54,20.78) iter 18: time=418.05 for 18 active reps approxLL diffs: (0.43,22.65) iter 19: time=417.90 for 18 active reps approxLL diffs: (0.33,18.38) iter 20: time=417.60 for 18 active reps approxLL diffs: (0.26,14.72) iter 21: time=418.10 for 18 active reps approxLL diffs: (0.21,17.97) iter 22: time=419.85 for 18 active reps approxLL diffs: (0.17,12.74) iter 23: time=418.41 for 18 active reps approxLL diffs: (0.14,5.84) iter 24: time=415.79 for 18 active reps approxLL diffs: (0.11,7.49) iter 25: time=412.04 for 18 active reps approxLL diffs: (0.09,8.36) iter 26: time=416.87 for 18 active reps approxLL diffs: (0.07,5.16) iter 27: time=418.09 for 18 active reps approxLL diffs: (0.06,6.54) iter 28: time=420.66 for 18 active reps approxLL diffs: (0.05,3.16) iter 29: time=432.33 for 18 active reps approxLL diffs: (0.04,2.63) iter 30: time=434.87 for 18 active reps approxLL diffs: (0.03,2.32) iter 31: time=435.21 for 18 active reps approxLL diffs: (0.03,1.66) iter 32: time=422.63 for 18 active reps approxLL diffs: (0.02,2.30) iter 33: time=425.68 for 18 active reps approxLL diffs: (0.02,1.10) iter 34: time=436.86 for 18 active reps approxLL diffs: (0.02,0.64) iter 35: time=437.28 for 18 active reps approxLL diffs: (0.02,1.90) iter 36: time=421.99 for 18 active reps approxLL diffs: (0.01,1.67) iter 37: time=422.74 for 18 active reps approxLL diffs: (0.01,2.37) iter 38: time=422.11 for 18 active reps approxLL diffs: (0.01,0.81) iter 39: time=422.72 for 18 active reps approxLL diffs: (0.01,0.59) iter 40: time=415.20 for 17 active reps approxLL diffs: (0.01,1.63) iter 41: time=409.93 for 17 active reps approxLL diffs: (0.01,1.16) iter 42: time=406.80 for 17 active reps approxLL diffs: (0.01,1.07) iter 43: time=389.67 for 16 active reps approxLL diffs: (0.01,1.04) iter 44: time=384.86 for 14 active reps approxLL diffs: (0.02,3.64) iter 45: time=383.16 for 14 active reps approxLL diffs: (0.01,3.04) iter 46: time=393.11 for 14 active reps approxLL diffs: (0.01,2.11) iter 47: time=393.23 for 14 active reps approxLL diffs: (0.01,1.35) iter 48: time=380.54 for 14 active reps approxLL diffs: (0.01,1.73) iter 49: time=374.66 for 14 active reps approxLL diffs: (0.01,2.55) iter 50: time=337.89 for 12 active reps approxLL diffs: (0.01,1.52) iter 51: time=338.67 for 12 active reps approxLL diffs: (0.01,1.28) iter 52: time=349.74 for 11 active reps approxLL diffs: (0.01,0.85) iter 53: time=306.18 for 9 active reps approxLL diffs: (0.01,1.19) iter 54: time=287.39 for 7 active reps approxLL diffs: (0.04,0.83) iter 55: time=288.17 for 7 active reps approxLL diffs: (0.03,0.95) iter 56: time=286.59 for 7 active reps approxLL diffs: (0.02,0.92) iter 57: time=287.04 for 7 active reps approxLL diffs: (0.01,0.47) iter 58: time=288.67 for 7 active reps approxLL diffs: (0.01,1.01) iter 59: time=265.73 for 6 active reps approxLL diffs: (0.02,0.63) iter 60: time=265.58 for 6 active reps approxLL diffs: (0.02,0.33) iter 61: time=268.84 for 6 active reps approxLL diffs: (0.01,0.33) iter 62: time=246.80 for 5 active reps approxLL diffs: (0.01,0.56) iter 63: time=237.78 for 3 active reps approxLL diffs: (0.01,0.64) iter 64: time=242.72 for 3 active reps approxLL diffs: (0.00,1.23) iter 65: time=221.66 for 2 active reps approxLL diffs: (0.08,1.07) iter 66: time=220.02 for 2 active reps approxLL diffs: (0.04,0.11) iter 67: time=220.34 for 2 active reps approxLL diffs: (0.02,0.03) iter 68: time=218.00 for 2 active reps approxLL diffs: (0.01,0.02) iter 69: time=218.66 for 2 active reps approxLL diffs: (0.01,0.02) iter 70: time=217.45 for 2 active reps approxLL diffs: (0.01,0.06) iter 71: time=170.39 for 1 active reps approxLL diffs: (0.20,0.20) iter 72: time=167.51 for 1 active reps approxLL diffs: (0.51,0.51) iter 73: time=168.13 for 1 active reps approxLL diffs: (0.32,0.32) iter 74: time=171.15 for 1 active reps approxLL diffs: (0.03,0.03) iter 75: time=183.45 for 1 active reps approxLL diffs: (0.00,0.00) Converged at iter 75: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 78.6%, memory/overhead = 21.4% Computing predictions on left-out cross-validation fold Time for computing predictions = 8370.46 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.02: 0.199362 f2=0.3, p=0.01: 0.198572 f2=0.5, p=0.01: 0.197560 ... f2=0.5, p=0.5: 0.133661 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.975314 Absolute prediction MSE using standard LMM: 0.844952 Absolute prediction MSE, fold-best f2=0.3, p=0.02: 0.780873 Absolute pred MSE using f2=0.5, p=0.5: 0.844952 Absolute pred MSE using f2=0.5, p=0.2: 0.822312 Absolute pred MSE using f2=0.5, p=0.1: 0.804513 Absolute pred MSE using f2=0.5, p=0.05: 0.791105 Absolute pred MSE using f2=0.5, p=0.02: 0.783537 Absolute pred MSE using f2=0.5, p=0.01: 0.782631 Absolute pred MSE using f2=0.3, p=0.5: 0.836792 Absolute pred MSE using f2=0.3, p=0.2: 0.809713 Absolute pred MSE using f2=0.3, p=0.1: 0.793176 Absolute pred MSE using f2=0.3, p=0.05: 0.784181 Absolute pred MSE using f2=0.3, p=0.02: 0.780873 Absolute pred MSE using f2=0.3, p=0.01: 0.781644 Absolute pred MSE using f2=0.1, p=0.5: 0.826778 Absolute pred MSE using f2=0.1, p=0.2: 0.799659 Absolute pred MSE using f2=0.1, p=0.1: 0.787014 Absolute pred MSE using f2=0.1, p=0.05: 0.784212 Absolute pred MSE using f2=0.1, p=0.02: 0.785800 Absolute pred MSE using f2=0.1, p=0.01: 0.786337 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.134 Relative improvement in prediction MSE using non-inf model: 0.076 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.02 Time for estimating mixture parameters = 39832.5 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=678.58 for 23 active reps iter 2: time=461.90 for 23 active reps approxLL diffs: (17219.87,19806.91) iter 3: time=463.22 for 23 active reps approxLL diffs: (3565.89,4301.02) iter 4: time=460.35 for 23 active reps approxLL diffs: (1290.15,1582.13) iter 5: time=461.18 for 23 active reps approxLL diffs: (609.29,780.60) iter 6: time=462.23 for 23 active reps approxLL diffs: (347.10,476.84) iter 7: time=459.48 for 23 active reps approxLL diffs: (220.15,291.70) iter 8: time=458.39 for 23 active reps approxLL diffs: (156.01,204.39) iter 9: time=459.12 for 23 active reps approxLL diffs: (107.67,133.62) iter 10: time=462.02 for 23 active reps approxLL diffs: (77.18,99.74) iter 11: time=459.32 for 23 active reps approxLL diffs: (58.69,81.25) iter 12: time=460.97 for 23 active reps approxLL diffs: (43.67,65.44) iter 13: time=458.62 for 23 active reps approxLL diffs: (28.08,48.54) iter 14: time=459.43 for 23 active reps approxLL diffs: (25.46,37.74) iter 15: time=458.80 for 23 active reps approxLL diffs: (15.53,36.89) iter 16: time=461.33 for 23 active reps approxLL diffs: (12.49,30.04) iter 17: time=465.41 for 23 active reps approxLL diffs: (14.79,27.61) iter 18: time=475.08 for 23 active reps approxLL diffs: (11.51,22.59) iter 19: time=470.90 for 23 active reps approxLL diffs: (9.75,17.39) iter 20: time=460.96 for 23 active reps approxLL diffs: (9.04,16.08) iter 21: time=465.91 for 23 active reps approxLL diffs: (6.49,13.58) iter 22: time=463.33 for 23 active reps approxLL diffs: (4.14,11.84) iter 23: time=464.23 for 23 active reps approxLL diffs: (3.26,8.80) iter 24: time=463.76 for 23 active reps approxLL diffs: (2.52,8.27) iter 25: time=465.82 for 23 active reps approxLL diffs: (1.90,10.80) iter 26: time=463.75 for 23 active reps approxLL diffs: (1.59,6.73) iter 27: time=468.05 for 23 active reps approxLL diffs: (1.26,7.02) iter 28: time=485.57 for 23 active reps approxLL diffs: (1.17,10.56) iter 29: time=488.48 for 23 active reps approxLL diffs: (0.89,6.58) iter 30: time=483.94 for 23 active reps approxLL diffs: (0.79,3.75) iter 31: time=462.54 for 23 active reps approxLL diffs: (0.57,4.19) iter 32: time=468.23 for 23 active reps approxLL diffs: (0.33,2.73) iter 33: time=467.65 for 23 active reps approxLL diffs: (0.19,3.08) iter 34: time=469.78 for 23 active reps approxLL diffs: (0.13,3.39) iter 35: time=467.42 for 23 active reps approxLL diffs: (0.10,3.61) iter 36: time=466.57 for 23 active reps approxLL diffs: (0.10,2.66) iter 37: time=476.58 for 23 active reps approxLL diffs: (0.08,1.89) iter 38: time=486.39 for 23 active reps approxLL diffs: (0.06,2.15) iter 39: time=501.48 for 23 active reps approxLL diffs: (0.05,1.04) iter 40: time=493.99 for 23 active reps approxLL diffs: (0.03,1.04) iter 41: time=491.14 for 23 active reps approxLL diffs: (0.03,1.70) iter 42: time=482.57 for 23 active reps approxLL diffs: (0.03,1.77) iter 43: time=480.25 for 23 active reps approxLL diffs: (0.03,2.36) iter 44: time=481.87 for 23 active reps approxLL diffs: (0.03,1.92) iter 45: time=500.43 for 23 active reps approxLL diffs: (0.02,1.54) iter 46: time=530.77 for 23 active reps approxLL diffs: (0.01,1.55) iter 47: time=561.22 for 23 active reps approxLL diffs: (0.01,3.38) iter 48: time=543.38 for 23 active reps approxLL diffs: (0.01,3.42) iter 49: time=484.96 for 22 active reps approxLL diffs: (0.01,1.71) iter 50: time=470.59 for 22 active reps approxLL diffs: (0.02,3.99) iter 51: time=476.18 for 22 active reps approxLL diffs: (0.01,1.07) iter 52: time=503.22 for 22 active reps approxLL diffs: (0.01,0.72) iter 53: time=462.95 for 21 active reps approxLL diffs: (0.01,1.25) iter 54: time=464.51 for 19 active reps approxLL diffs: (0.01,1.65) iter 55: time=413.01 for 17 active reps approxLL diffs: (0.01,1.00) iter 56: time=399.50 for 15 active reps approxLL diffs: (0.01,1.52) iter 57: time=372.56 for 14 active reps approxLL diffs: (0.02,0.59) iter 58: time=372.43 for 14 active reps approxLL diffs: (0.01,0.61) iter 59: time=353.09 for 13 active reps approxLL diffs: (0.02,0.28) iter 60: time=349.39 for 13 active reps approxLL diffs: (0.01,0.88) iter 61: time=349.78 for 13 active reps approxLL diffs: (0.01,1.87) iter 62: time=347.98 for 11 active reps approxLL diffs: (0.01,0.42) iter 63: time=329.31 for 10 active reps approxLL diffs: (0.01,0.19) iter 64: time=309.96 for 9 active reps approxLL diffs: (0.01,0.21) iter 65: time=267.22 for 8 active reps approxLL diffs: (0.01,0.53) iter 66: time=268.89 for 8 active reps approxLL diffs: (0.01,1.69) iter 67: time=278.26 for 8 active reps approxLL diffs: (0.01,3.16) iter 68: time=298.23 for 7 active reps approxLL diffs: (0.01,0.48) iter 69: time=280.82 for 6 active reps approxLL diffs: (0.01,0.32) iter 70: time=285.67 for 6 active reps approxLL diffs: (0.01,1.53) iter 71: time=280.89 for 6 active reps approxLL diffs: (0.01,1.21) iter 72: time=255.32 for 4 active reps approxLL diffs: (0.01,0.06) iter 73: time=254.07 for 3 active reps approxLL diffs: (0.01,0.02) iter 74: time=240.57 for 2 active reps approxLL diffs: (0.01,0.01) Converged at iter 74: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 81.8%, memory/overhead = 18.2% 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 > 437.5) # of SNPs remaining after outlier window removal: 569587/579570 Intercept of LD Score regression for ref stats: 1.156 (0.018) Estimated attenuation: 0.144 (0.019) Intercept of LD Score regression for cur stats: 1.146 (0.018) Calibration factor (ref/cur) to multiply by: 1.009 (0.002) Time for computing Bayesian mixed model assoc stats = 32581.1 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=422.12 for 1 active reps iter 2: time=187.01 for 1 active reps approxLL diffs: (19879.21,19879.21) iter 3: time=180.64 for 1 active reps approxLL diffs: (4305.85,4305.85) iter 4: time=186.41 for 1 active reps approxLL diffs: (1581.16,1581.16) iter 5: time=192.73 for 1 active reps approxLL diffs: (778.09,778.09) iter 6: time=198.72 for 1 active reps approxLL diffs: (471.27,471.27) iter 7: time=192.98 for 1 active reps approxLL diffs: (280.82,280.82) iter 8: time=195.38 for 1 active reps approxLL diffs: (193.15,193.15) iter 9: time=197.24 for 1 active reps approxLL diffs: (133.13,133.13) iter 10: time=192.19 for 1 active reps approxLL diffs: (92.02,92.02) iter 11: time=183.10 for 1 active reps approxLL diffs: (74.33,74.33) iter 12: time=189.58 for 1 active reps approxLL diffs: (57.66,57.66) iter 13: time=192.01 for 1 active reps approxLL diffs: (41.94,41.94) iter 14: time=186.38 for 1 active reps approxLL diffs: (34.29,34.29) iter 15: time=190.55 for 1 active reps approxLL diffs: (25.08,25.08) iter 16: time=189.99 for 1 active reps approxLL diffs: (20.82,20.82) iter 17: time=180.75 for 1 active reps approxLL diffs: (23.05,23.05) iter 18: time=174.56 for 1 active reps approxLL diffs: (23.07,23.07) iter 19: time=175.93 for 1 active reps approxLL diffs: (20.45,20.45) iter 20: time=181.17 for 1 active reps approxLL diffs: (15.91,15.91) iter 21: time=190.88 for 1 active reps approxLL diffs: (11.21,11.21) iter 22: time=181.32 for 1 active reps approxLL diffs: (7.65,7.65) iter 23: time=188.07 for 1 active reps approxLL diffs: (6.46,6.46) iter 24: time=192.21 for 1 active reps approxLL diffs: (5.72,5.72) iter 25: time=192.20 for 1 active reps approxLL diffs: (4.19,4.19) iter 26: time=193.56 for 1 active reps approxLL diffs: (3.05,3.05) iter 27: time=201.14 for 1 active reps approxLL diffs: (2.51,2.51) iter 28: time=198.69 for 1 active reps approxLL diffs: (1.90,1.90) iter 29: time=180.50 for 1 active reps approxLL diffs: (1.06,1.06) iter 30: time=179.81 for 1 active reps approxLL diffs: (0.92,0.92) iter 31: time=181.41 for 1 active reps approxLL diffs: (0.79,0.79) iter 32: time=191.15 for 1 active reps approxLL diffs: (0.74,0.74) iter 33: time=180.72 for 1 active reps approxLL diffs: (0.83,0.83) iter 34: time=189.55 for 1 active reps approxLL diffs: (0.81,0.81) iter 35: time=185.91 for 1 active reps approxLL diffs: (1.25,1.25) iter 36: time=185.15 for 1 active reps approxLL diffs: (1.43,1.43) iter 37: time=188.55 for 1 active reps approxLL diffs: (1.10,1.10) iter 38: time=192.92 for 1 active reps approxLL diffs: (0.96,0.96) iter 39: time=189.63 for 1 active reps approxLL diffs: (0.28,0.28) iter 40: time=186.22 for 1 active reps approxLL diffs: (0.08,0.08) iter 41: time=184.50 for 1 active reps approxLL diffs: (0.05,0.05) iter 42: time=183.47 for 1 active reps approxLL diffs: (0.04,0.04) iter 43: time=185.42 for 1 active reps approxLL diffs: (0.03,0.03) iter 44: time=185.10 for 1 active reps approxLL diffs: (0.03,0.03) iter 45: time=179.19 for 1 active reps approxLL diffs: (0.04,0.04) iter 46: time=180.01 for 1 active reps approxLL diffs: (0.06,0.06) iter 47: time=181.43 for 1 active reps approxLL diffs: (0.12,0.12) iter 48: time=183.23 for 1 active reps approxLL diffs: (0.26,0.26) iter 49: time=183.30 for 1 active reps approxLL diffs: (0.46,0.46) iter 50: time=183.90 for 1 active reps approxLL diffs: (0.43,0.43) iter 51: time=180.54 for 1 active reps approxLL diffs: (0.28,0.28) iter 52: time=186.95 for 1 active reps approxLL diffs: (0.13,0.13) iter 53: time=187.36 for 1 active reps approxLL diffs: (0.05,0.05) iter 54: time=184.56 for 1 active reps approxLL diffs: (0.05,0.05) iter 55: time=181.33 for 1 active reps approxLL diffs: (0.18,0.18) iter 56: time=184.21 for 1 active reps approxLL diffs: (0.58,0.58) iter 57: time=187.17 for 1 active reps approxLL diffs: (0.70,0.70) iter 58: time=180.62 for 1 active reps approxLL diffs: (0.35,0.35) iter 59: time=182.13 for 1 active reps approxLL diffs: (0.13,0.13) iter 60: time=186.80 for 1 active reps approxLL diffs: (0.04,0.04) iter 61: time=182.83 for 1 active reps approxLL diffs: (0.02,0.02) iter 62: time=191.64 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 62: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 54.5%, memory/overhead = 45.5% Time for computing and writing betas = 11809.9 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.27161 (705850 good SNPs) lambdaGC: 1.4269 Mean BOLT_LMM_INF: 2.44176 (705850 good SNPs) lambdaGC: 1.45005 Mean BOLT_LMM: 2.53704 (705850 good SNPs) lambdaGC: 1.4711 Note that LINREG may be confounded by a factor of 1.00177 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 6842.7 sec Total elapsed time for analysis = 131960 sec