+-----------------------------+ | ___ | | 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.hwe1000.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 \ --maxMissingPerSnp=0.15 \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab \ --phenoCol=bp_SYSTOLICadjMEDz \ --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.bp_SYSTOLICadjMEDz.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.bp_SYSTOLICadjMEDz.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.hwe1000.txt Excluded 69333 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: 709999 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 94471 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 709999 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 = 709999 Variance component 1: 709999 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 2954.51 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_v3.061518.tab Number of indivs with no missing phenotype(s) to use: 422771 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 = 422771 Singular values of covariate matrix: S[0] = 2.20863e+06 S[1] = 4649.28 S[2] = 458.305 S[3] = 286.099 S[4] = 196.173 S[5] = 190.238 S[6] = 179.002 S[7] = 173.477 S[8] = 168.153 S[9] = 162.701 S[10] = 157.349 S[11] = 149.901 S[12] = 143.481 S[13] = 141.843 S[14] = 137.871 S[15] = 131.345 S[16] = 127.561 S[17] = 125.425 S[18] = 117.213 S[19] = 109.594 S[20] = 107.032 S[21] = 98.2106 S[22] = 42.809 S[23] = 23.3812 S[24] = 18.996 S[25] = 18.2196 S[26] = 0.959826 S[27] = 0.959751 S[28] = 0.959399 S[29] = 0.9593 S[30] = 0.959128 S[31] = 0.959063 S[32] = 0.958786 S[33] = 0.958681 S[34] = 0.958523 S[35] = 0.958244 S[36] = 0.958029 S[37] = 0.957942 S[38] = 0.957858 S[39] = 0.957537 S[40] = 0.957395 S[41] = 0.957284 S[42] = 0.956903 S[43] = 0.956521 S[44] = 0.941244 S[45] = 0.865359 S[46] = 1.79777e-11 S[47] = 9.24377e-12 S[48] = 2.0723e-13 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 46 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 420297.867862 Dimension of all-1s proj space (Nused-1): 422770 Time for covariate data setup + Bolt initialization = 4463.12 sec Phenotype 1: N = 422771 mean = -0.0036961 std = 0.998643 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 426.657 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 422771) Estimating MC scaling f_REML at log(delta) = 1.09285, h2 = 0.25... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=343.48 rNorms/orig: (0.6,0.6) res2s: 698013..157232 iter 2: time=331.62 rNorms/orig: (0.5,0.6) res2s: 825059..191081 iter 3: time=335.60 rNorms/orig: (0.3,0.3) res2s: 945068..226331 iter 4: time=328.19 rNorms/orig: (0.2,0.2) res2s: 991392..237411 iter 5: time=332.08 rNorms/orig: (0.1,0.1) res2s: 1.01389e+06..243526 iter 6: time=318.47 rNorms/orig: (0.08,0.09) res2s: 1.02412e+06..246203 iter 7: time=319.93 rNorms/orig: (0.05,0.06) res2s: 1.02946e+06..247660 iter 8: time=324.37 rNorms/orig: (0.03,0.04) res2s: 1.03167e+06..248280 iter 9: time=320.65 rNorms/orig: (0.02,0.02) res2s: 1.03259e+06..248597 iter 10: time=319.42 rNorms/orig: (0.01,0.01) res2s: 1.03306e+06..248738 iter 11: time=337.66 rNorms/orig: (0.009,0.01) res2s: 1.03327e+06..248800 iter 12: time=329.66 rNorms/orig: (0.005,0.006) res2s: 1.03337e+06..248828 iter 13: time=329.34 rNorms/orig: (0.004,0.004) res2s: 1.03341e+06..248840 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 54.4%, memory/overhead = 45.6% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0366033 Estimating MC scaling f_REML at log(delta) = -0.00575818, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=353.70 rNorms/orig: (1,1) res2s: 76051..50067.9 iter 2: time=327.15 rNorms/orig: (1,1) res2s: 108191..71989.5 iter 3: time=326.03 rNorms/orig: (0.8,0.9) res2s: 161743..108833 iter 4: time=327.36 rNorms/orig: (0.6,0.6) res2s: 198054..127244 iter 5: time=324.50 rNorms/orig: (0.5,0.5) res2s: 223949..141903 iter 6: time=326.34 rNorms/orig: (0.4,0.4) res2s: 242004..151084 iter 7: time=326.30 rNorms/orig: (0.3,0.3) res2s: 256016..158201 iter 8: time=332.61 rNorms/orig: (0.2,0.2) res2s: 264212..162530 iter 9: time=331.51 rNorms/orig: (0.2,0.2) res2s: 269425..165704 iter 10: time=327.98 rNorms/orig: (0.1,0.1) res2s: 273689..167732 iter 11: time=325.35 rNorms/orig: (0.1,0.1) res2s: 276146..169024 iter 12: time=326.77 rNorms/orig: (0.08,0.08) res2s: 277878..169874 iter 13: time=333.10 rNorms/orig: (0.06,0.07) res2s: 278889..170394 iter 14: time=326.25 rNorms/orig: (0.05,0.05) res2s: 279585..170751 iter 15: time=331.95 rNorms/orig: (0.04,0.04) res2s: 279995..170972 iter 16: time=327.01 rNorms/orig: (0.03,0.03) res2s: 280258..171098 iter 17: time=330.03 rNorms/orig: (0.02,0.02) res2s: 280415..171187 iter 18: time=333.76 rNorms/orig: (0.02,0.02) res2s: 280510..171241 iter 19: time=324.22 rNorms/orig: (0.01,0.01) res2s: 280577..171279 iter 20: time=321.93 rNorms/orig: (0.01,0.01) res2s: 280613..171298 iter 21: time=324.50 rNorms/orig: (0.007,0.008) res2s: 280640..171311 iter 22: time=332.20 rNorms/orig: (0.006,0.006) res2s: 280655..171319 iter 23: time=475.38 rNorms/orig: (0.004,0.005) res2s: 280665..171324 Converged at iter 23: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 54.6%, memory/overhead = 45.4% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.263579 Estimating MC scaling f_REML at log(delta) = 0.958893, h2 = 0.275948... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=477.41 rNorms/orig: (0.7,0.7) res2s: 554355..141174 iter 2: time=476.83 rNorms/orig: (0.6,0.7) res2s: 670000..174935 iter 3: time=472.24 rNorms/orig: (0.4,0.4) res2s: 788179..212430 iter 4: time=477.25 rNorms/orig: (0.2,0.2) res2s: 837095..224980 iter 5: time=478.53 rNorms/orig: (0.2,0.2) res2s: 862054..232277 iter 6: time=478.09 rNorms/orig: (0.1,0.1) res2s: 874111..235643 iter 7: time=477.99 rNorms/orig: (0.06,0.07) res2s: 880772..237574 iter 8: time=469.41 rNorms/orig: (0.04,0.05) res2s: 883668..238441 iter 9: time=609.48 rNorms/orig: (0.03,0.03) res2s: 884954..238910 iter 10: time=812.97 rNorms/orig: (0.02,0.02) res2s: 885657..239130 iter 11: time=890.18 rNorms/orig: (0.01,0.01) res2s: 885987..239232 iter 12: time=913.53 rNorms/orig: (0.008,0.009) res2s: 886152..239282 iter 13: time=892.63 rNorms/orig: (0.006,0.006) res2s: 886226..239304 iter 14: time=884.78 rNorms/orig: (0.003,0.004) res2s: 886262..239315 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 52.7%, memory/overhead = 47.3% MCscaling: logDelta = 0.96, h2 = 0.276, f = 0.00116669 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.276 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.270961, logDelta = 0.958893, f = 0.00116669 Time for fitting variance components = 22163.3 sec === Computing mixed model assoc stats (inf. model) === Selected 30 SNPs for computation of prospective stat Tried 31; threw out 1 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=1963.34 rNorms/orig: (0.5,0.7) res2s: 140929..215935 iter 2: time=1902.99 rNorms/orig: (0.3,0.7) res2s: 175005..244379 iter 3: time=1783.93 rNorms/orig: (0.2,0.4) res2s: 214238..265869 iter 4: time=1782.72 rNorms/orig: (0.2,0.3) res2s: 228006..274475 iter 5: time=1784.27 rNorms/orig: (0.1,0.2) res2s: 236038..279546 iter 6: time=1774.20 rNorms/orig: (0.07,0.1) res2s: 239895..282157 iter 7: time=1756.41 rNorms/orig: (0.05,0.08) res2s: 242184..283270 iter 8: time=1740.84 rNorms/orig: (0.03,0.06) res2s: 243244..283960 iter 9: time=1740.26 rNorms/orig: (0.02,0.04) res2s: 243819..284276 iter 10: time=1742.05 rNorms/orig: (0.01,0.02) res2s: 244111..284399 iter 11: time=1744.70 rNorms/orig: (0.009,0.02) res2s: 244241..284463 iter 12: time=1746.00 rNorms/orig: (0.005,0.01) res2s: 244311..284495 iter 13: time=1746.18 rNorms/orig: (0.004,0.008) res2s: 244341..284509 iter 14: time=1745.51 rNorms/orig: (0.002,0.005) res2s: 244358..284515 iter 15: time=1737.97 rNorms/orig: (0.001,0.003) res2s: 244365..284519 iter 16: time=1735.91 rNorms/orig: (0.0008,0.002) res2s: 244369..284520 iter 17: time=1723.57 rNorms/orig: (0.0005,0.002) res2s: 244370..284521 iter 18: time=1738.25 rNorms/orig: (0.0003,0.001) res2s: 244371..284521 iter 19: time=1573.34 rNorms/orig: (0.0002,0.0006) res2s: 244372..284521 iter 20: time=1459.05 rNorms/orig: (0.0001,0.0005) res2s: 244372..284521 Converged at iter 20: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 77.6%, memory/overhead = 22.4% AvgPro: 1.975 AvgRetro: 1.929 Calibration: 1.024 (0.012) (30 SNPs) Ratio of medians: 1.010 Median of ratios: 1.012 WARNING: Calibration std error is high; consider increasing --numCalibSnps Using ratio of medians instead: 1.01032 Time for computing infinitesimal model assoc stats = 35630.6 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 23.0517 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 605211/709999 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: 583483/709999 Masking windows around outlier snps (chisq > 422.8) # of SNPs remaining after outlier window removal: 583109/583483 Intercept of LD Score regression for ref stats: 1.168 (0.013) Estimated attenuation: 0.156 (0.015) Intercept of LD Score regression for cur stats: 1.170 (0.012) Calibration factor (ref/cur) to multiply by: 0.998 (0.002) LINREG intercept inflation = 1.00162 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 = 338216 Singular values of covariate matrix: S[0] = 1.97545e+06 S[1] = 4157.24 S[2] = 409.945 S[3] = 255.894 S[4] = 175.451 S[5] = 170.16 S[6] = 160.081 S[7] = 155.241 S[8] = 150.468 S[9] = 145.597 S[10] = 140.731 S[11] = 134.087 S[12] = 128.432 S[13] = 126.828 S[14] = 123.299 S[15] = 117.414 S[16] = 114.088 S[17] = 112.157 S[18] = 104.782 S[19] = 98.0175 S[20] = 95.7561 S[21] = 87.6596 S[22] = 38.1044 S[23] = 20.8855 S[24] = 17.0115 S[25] = 16.3939 S[26] = 0.86048 S[27] = 0.859587 S[28] = 0.859212 S[29] = 0.858911 S[30] = 0.858715 S[31] = 0.858331 S[32] = 0.857782 S[33] = 0.857534 S[34] = 0.857067 S[35] = 0.856911 S[36] = 0.856637 S[37] = 0.856409 S[38] = 0.856085 S[39] = 0.855697 S[40] = 0.855624 S[41] = 0.854903 S[42] = 0.854674 S[43] = 0.854412 S[44] = 0.842616 S[45] = 0.773647 S[46] = 1.45801e-11 S[47] = 5.37671e-12 S[48] = 6.96393e-13 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 46 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 336227.952302 Dimension of all-1s proj space (Nused-1): 338215 Beginning variational Bayes iter 1: time=1202.62 for 18 active reps iter 2: time=699.55 for 18 active reps approxLL diffs: (6865.29,9116.86) iter 3: time=666.51 for 18 active reps approxLL diffs: (640.31,1258.30) iter 4: time=622.80 for 18 active reps approxLL diffs: (100.19,467.71) iter 5: time=583.79 for 18 active reps approxLL diffs: (24.90,224.81) iter 6: time=583.76 for 18 active reps approxLL diffs: (9.07,126.61) iter 7: time=578.80 for 18 active reps approxLL diffs: (4.40,76.48) iter 8: time=580.32 for 18 active reps approxLL diffs: (2.52,50.81) iter 9: time=590.88 for 18 active reps approxLL diffs: (1.46,32.36) iter 10: time=581.57 for 18 active reps approxLL diffs: (0.89,29.97) iter 11: time=582.42 for 18 active reps approxLL diffs: (0.58,22.26) iter 12: time=580.01 for 18 active reps approxLL diffs: (0.39,21.72) iter 13: time=581.30 for 18 active reps approxLL diffs: (0.26,15.41) iter 14: time=581.16 for 18 active reps approxLL diffs: (0.18,10.64) iter 15: time=558.84 for 18 active reps approxLL diffs: (0.13,7.62) iter 16: time=564.33 for 18 active reps approxLL diffs: (0.09,8.78) iter 17: time=575.64 for 18 active reps approxLL diffs: (0.07,9.54) iter 18: time=578.61 for 18 active reps approxLL diffs: (0.05,6.57) iter 19: time=576.42 for 18 active reps approxLL diffs: (0.04,6.54) iter 20: time=580.84 for 18 active reps approxLL diffs: (0.03,3.01) iter 21: time=589.43 for 18 active reps approxLL diffs: (0.02,1.89) iter 22: time=583.70 for 18 active reps approxLL diffs: (0.02,2.22) iter 23: time=581.04 for 18 active reps approxLL diffs: (0.01,3.59) iter 24: time=580.53 for 18 active reps approxLL diffs: (0.01,2.94) iter 25: time=563.96 for 17 active reps approxLL diffs: (0.02,3.02) iter 26: time=559.27 for 17 active reps approxLL diffs: (0.01,2.34) iter 27: time=554.31 for 17 active reps approxLL diffs: (0.01,2.15) iter 28: time=553.12 for 17 active reps approxLL diffs: (0.01,3.32) iter 29: time=635.28 for 15 active reps approxLL diffs: (0.01,1.97) iter 30: time=635.59 for 15 active reps approxLL diffs: (0.01,3.76) iter 31: time=636.26 for 15 active reps approxLL diffs: (0.01,2.03) iter 32: time=584.91 for 13 active reps approxLL diffs: (0.01,1.89) iter 33: time=585.21 for 13 active reps approxLL diffs: (0.01,0.77) iter 34: time=514.00 for 9 active reps approxLL diffs: (0.07,1.03) iter 35: time=515.71 for 9 active reps approxLL diffs: (0.04,1.63) iter 36: time=515.88 for 9 active reps approxLL diffs: (0.02,0.79) iter 37: time=517.05 for 9 active reps approxLL diffs: (0.01,1.12) iter 38: time=514.89 for 9 active reps approxLL diffs: (0.01,1.80) iter 39: time=479.83 for 8 active reps approxLL diffs: (0.04,2.87) iter 40: time=432.26 for 8 active reps approxLL diffs: (0.02,2.33) iter 41: time=436.36 for 8 active reps approxLL diffs: (0.01,1.61) iter 42: time=438.33 for 8 active reps approxLL diffs: (0.01,1.47) iter 43: time=437.70 for 8 active reps approxLL diffs: (0.01,4.38) iter 44: time=388.44 for 7 active reps approxLL diffs: (0.04,3.00) iter 45: time=390.47 for 7 active reps approxLL diffs: (0.04,1.51) iter 46: time=389.95 for 7 active reps approxLL diffs: (0.04,0.35) iter 47: time=390.40 for 7 active reps approxLL diffs: (0.04,0.20) iter 48: time=390.70 for 7 active reps approxLL diffs: (0.02,0.27) iter 49: time=391.08 for 7 active reps approxLL diffs: (0.02,0.89) iter 50: time=391.33 for 7 active reps approxLL diffs: (0.02,2.01) iter 51: time=392.63 for 7 active reps approxLL diffs: (0.01,0.37) iter 52: time=372.69 for 7 active reps approxLL diffs: (0.01,0.40) iter 53: time=330.11 for 6 active reps approxLL diffs: (0.01,1.52) iter 54: time=322.89 for 5 active reps approxLL diffs: (0.01,1.88) iter 55: time=311.91 for 4 active reps approxLL diffs: (0.02,1.36) iter 56: time=300.94 for 4 active reps approxLL diffs: (0.02,1.04) iter 57: time=311.42 for 4 active reps approxLL diffs: (0.02,3.05) iter 58: time=339.84 for 4 active reps approxLL diffs: (0.01,3.63) iter 59: time=334.37 for 4 active reps approxLL diffs: (0.00,0.59) iter 60: time=814.38 for 3 active reps approxLL diffs: (0.13,0.33) iter 61: time=809.38 for 3 active reps approxLL diffs: (0.07,0.45) iter 62: time=781.90 for 3 active reps approxLL diffs: (0.01,1.10) iter 63: time=476.62 for 2 active reps approxLL diffs: (0.37,0.89) iter 64: time=476.62 for 2 active reps approxLL diffs: (0.21,1.49) iter 65: time=474.13 for 2 active reps approxLL diffs: (0.03,0.87) iter 66: time=484.58 for 2 active reps approxLL diffs: (0.01,0.03) iter 67: time=489.62 for 2 active reps approxLL diffs: (0.01,0.01) iter 68: time=265.30 for 1 active reps approxLL diffs: (0.02,0.02) iter 69: time=265.05 for 1 active reps approxLL diffs: (0.02,0.02) iter 70: time=264.23 for 1 active reps approxLL diffs: (0.03,0.03) iter 71: time=269.70 for 1 active reps approxLL diffs: (0.04,0.04) iter 72: time=256.10 for 1 active reps approxLL diffs: (0.05,0.05) iter 73: time=226.93 for 1 active reps approxLL diffs: (0.03,0.03) iter 74: time=227.56 for 1 active reps approxLL diffs: (0.01,0.01) iter 75: time=228.87 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 = 76.3%, memory/overhead = 23.7% Computing predictions on left-out cross-validation fold Time for computing predictions = 6778.69 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.05: 0.116186 f2=0.5, p=0.02: 0.115790 f2=0.1, p=0.1: 0.115118 ... f2=0.1, p=0.01: 0.093516 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.980929 Absolute prediction MSE using standard LMM: 0.886903 Absolute prediction MSE, fold-best f2=0.3, p=0.05: 0.866959 Absolute pred MSE using f2=0.5, p=0.5: 0.886903 Absolute pred MSE using f2=0.5, p=0.2: 0.880671 Absolute pred MSE using f2=0.5, p=0.1: 0.873706 Absolute pred MSE using f2=0.5, p=0.05: 0.868201 Absolute pred MSE using f2=0.5, p=0.02: 0.867347 Absolute pred MSE using f2=0.5, p=0.01: 0.869968 Absolute pred MSE using f2=0.3, p=0.5: 0.884945 Absolute pred MSE using f2=0.3, p=0.2: 0.875521 Absolute pred MSE using f2=0.3, p=0.1: 0.869096 Absolute pred MSE using f2=0.3, p=0.05: 0.866959 Absolute pred MSE using f2=0.3, p=0.02: 0.871641 Absolute pred MSE using f2=0.3, p=0.01: 0.875778 Absolute pred MSE using f2=0.1, p=0.5: 0.881397 Absolute pred MSE using f2=0.1, p=0.2: 0.871538 Absolute pred MSE using f2=0.1, p=0.1: 0.868006 Absolute pred MSE using f2=0.1, p=0.05: 0.872203 Absolute pred MSE using f2=0.1, p=0.02: 0.883685 Absolute pred MSE using f2=0.1, p=0.01: 0.889197 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.096 Relative improvement in prediction MSE using non-inf model: 0.022 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.05 Time for estimating mixture parameters = 50879.9 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=1215.43 for 23 active reps iter 2: time=681.47 for 23 active reps approxLL diffs: (11131.33,11714.64) iter 3: time=610.28 for 23 active reps approxLL diffs: (1571.76,1649.56) iter 4: time=609.02 for 23 active reps approxLL diffs: (409.84,437.97) iter 5: time=600.39 for 23 active reps approxLL diffs: (163.82,178.29) iter 6: time=603.70 for 23 active reps approxLL diffs: (82.14,89.50) iter 7: time=612.12 for 23 active reps approxLL diffs: (42.88,51.09) iter 8: time=604.36 for 23 active reps approxLL diffs: (26.88,34.14) iter 9: time=594.97 for 23 active reps approxLL diffs: (17.05,22.24) iter 10: time=604.39 for 23 active reps approxLL diffs: (11.11,16.94) iter 11: time=598.26 for 23 active reps approxLL diffs: (7.67,13.42) iter 12: time=599.24 for 23 active reps approxLL diffs: (6.71,10.80) iter 13: time=605.57 for 23 active reps approxLL diffs: (5.30,8.22) iter 14: time=598.88 for 23 active reps approxLL diffs: (4.17,7.04) iter 15: time=595.63 for 23 active reps approxLL diffs: (2.99,6.40) iter 16: time=599.74 for 23 active reps approxLL diffs: (2.35,5.28) iter 17: time=596.88 for 23 active reps approxLL diffs: (1.97,4.44) iter 18: time=583.77 for 23 active reps approxLL diffs: (1.29,3.95) iter 19: time=592.26 for 23 active reps approxLL diffs: (1.27,3.11) iter 20: time=616.12 for 23 active reps approxLL diffs: (0.89,2.55) iter 21: time=596.63 for 23 active reps approxLL diffs: (0.73,2.94) iter 22: time=616.92 for 23 active reps approxLL diffs: (0.49,3.74) iter 23: time=594.60 for 23 active reps approxLL diffs: (0.40,1.80) iter 24: time=608.36 for 23 active reps approxLL diffs: (0.27,1.65) iter 25: time=602.56 for 23 active reps approxLL diffs: (0.22,1.82) iter 26: time=599.90 for 23 active reps approxLL diffs: (0.14,1.14) iter 27: time=600.49 for 23 active reps approxLL diffs: (0.11,1.40) iter 28: time=603.75 for 23 active reps approxLL diffs: (0.13,2.20) iter 29: time=600.52 for 23 active reps approxLL diffs: (0.12,1.86) iter 30: time=594.42 for 23 active reps approxLL diffs: (0.10,1.36) iter 31: time=601.11 for 23 active reps approxLL diffs: (0.09,1.88) iter 32: time=595.94 for 23 active reps approxLL diffs: (0.09,1.46) iter 33: time=595.51 for 23 active reps approxLL diffs: (0.09,0.97) iter 34: time=609.91 for 23 active reps approxLL diffs: (0.07,0.73) iter 35: time=595.80 for 23 active reps approxLL diffs: (0.06,0.82) iter 36: time=595.83 for 23 active reps approxLL diffs: (0.05,0.86) iter 37: time=603.86 for 23 active reps approxLL diffs: (0.02,1.78) iter 38: time=594.93 for 23 active reps approxLL diffs: (0.02,1.65) iter 39: time=596.81 for 23 active reps approxLL diffs: (0.02,0.85) iter 40: time=604.42 for 23 active reps approxLL diffs: (0.02,0.94) iter 41: time=593.98 for 23 active reps approxLL diffs: (0.01,0.53) iter 42: time=596.57 for 23 active reps approxLL diffs: (0.01,0.54) iter 43: time=569.60 for 22 active reps approxLL diffs: (0.02,1.32) iter 44: time=564.75 for 22 active reps approxLL diffs: (0.01,0.98) iter 45: time=551.99 for 21 active reps approxLL diffs: (0.01,0.34) iter 46: time=520.10 for 20 active reps approxLL diffs: (0.01,0.40) iter 47: time=530.91 for 20 active reps approxLL diffs: (0.01,0.40) iter 48: time=519.19 for 20 active reps approxLL diffs: (0.01,0.22) iter 49: time=520.86 for 20 active reps approxLL diffs: (0.01,0.28) iter 50: time=494.00 for 18 active reps approxLL diffs: (0.02,0.39) iter 51: time=482.91 for 18 active reps approxLL diffs: (0.02,0.36) iter 52: time=484.95 for 18 active reps approxLL diffs: (0.01,0.39) iter 53: time=546.34 for 15 active reps approxLL diffs: (0.01,0.40) iter 54: time=522.36 for 14 active reps approxLL diffs: (0.01,0.27) iter 55: time=511.34 for 14 active reps approxLL diffs: (0.01,0.18) iter 56: time=511.15 for 14 active reps approxLL diffs: (0.02,0.19) iter 57: time=512.82 for 14 active reps approxLL diffs: (0.01,0.27) iter 58: time=510.20 for 14 active reps approxLL diffs: (0.01,0.22) iter 59: time=502.15 for 13 active reps approxLL diffs: (0.00,0.29) iter 60: time=476.60 for 12 active reps approxLL diffs: (0.01,0.43) iter 61: time=482.31 for 11 active reps approxLL diffs: (0.01,0.31) iter 62: time=430.94 for 9 active reps approxLL diffs: (0.03,0.24) iter 63: time=429.32 for 9 active reps approxLL diffs: (0.01,0.31) iter 64: time=426.77 for 9 active reps approxLL diffs: (0.01,0.73) iter 65: time=404.09 for 8 active reps approxLL diffs: (0.00,1.21) iter 66: time=316.98 for 6 active reps approxLL diffs: (0.01,0.71) iter 67: time=305.98 for 5 active reps approxLL diffs: (0.00,0.83) iter 68: time=286.34 for 4 active reps approxLL diffs: (0.01,0.39) iter 69: time=776.47 for 3 active reps approxLL diffs: (0.02,0.04) iter 70: time=773.85 for 3 active reps approxLL diffs: (0.01,0.05) iter 71: time=479.98 for 2 active reps approxLL diffs: (0.04,0.07) iter 72: time=483.58 for 2 active reps approxLL diffs: (0.09,0.15) iter 73: time=479.35 for 2 active reps approxLL diffs: (0.12,0.53) iter 74: time=478.90 for 2 active reps approxLL diffs: (0.10,0.63) iter 75: time=474.08 for 2 active reps approxLL diffs: (0.05,0.15) iter 76: time=485.03 for 2 active reps approxLL diffs: (0.01,0.01) iter 77: time=477.34 for 2 active reps approxLL diffs: (0.00,0.00) Converged at iter 77: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 85.7%, memory/overhead = 14.3% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 583483/709999 Masking windows around outlier snps (chisq > 422.8) # of SNPs remaining after outlier window removal: 583109/583483 Intercept of LD Score regression for ref stats: 1.168 (0.013) Estimated attenuation: 0.156 (0.015) Intercept of LD Score regression for cur stats: 1.165 (0.013) Calibration factor (ref/cur) to multiply by: 1.003 (0.001) Time for computing Bayesian mixed model assoc stats = 43701.7 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=698.21 for 1 active reps iter 2: time=223.27 for 1 active reps approxLL diffs: (11784.30,11784.30) iter 3: time=221.26 for 1 active reps approxLL diffs: (1646.22,1646.22) iter 4: time=217.78 for 1 active reps approxLL diffs: (431.43,431.43) iter 5: time=220.59 for 1 active reps approxLL diffs: (172.77,172.77) iter 6: time=220.54 for 1 active reps approxLL diffs: (86.13,86.13) iter 7: time=220.57 for 1 active reps approxLL diffs: (48.50,48.50) iter 8: time=220.33 for 1 active reps approxLL diffs: (29.69,29.69) iter 9: time=222.02 for 1 active reps approxLL diffs: (18.19,18.19) iter 10: time=225.39 for 1 active reps approxLL diffs: (13.64,13.64) iter 11: time=215.70 for 1 active reps approxLL diffs: (10.49,10.49) iter 12: time=216.58 for 1 active reps approxLL diffs: (8.16,8.16) iter 13: time=219.38 for 1 active reps approxLL diffs: (6.88,6.88) iter 14: time=215.00 for 1 active reps approxLL diffs: (5.60,5.60) iter 15: time=215.96 for 1 active reps approxLL diffs: (5.63,5.63) iter 16: time=215.36 for 1 active reps approxLL diffs: (4.61,4.61) iter 17: time=215.51 for 1 active reps approxLL diffs: (2.77,2.77) iter 18: time=222.97 for 1 active reps approxLL diffs: (2.09,2.09) iter 19: time=216.99 for 1 active reps approxLL diffs: (2.13,2.13) iter 20: time=216.10 for 1 active reps approxLL diffs: (1.68,1.68) iter 21: time=218.16 for 1 active reps approxLL diffs: (1.39,1.39) iter 22: time=218.77 for 1 active reps approxLL diffs: (1.27,1.27) iter 23: time=218.63 for 1 active reps approxLL diffs: (1.25,1.25) iter 24: time=219.78 for 1 active reps approxLL diffs: (1.38,1.38) iter 25: time=219.36 for 1 active reps approxLL diffs: (1.98,1.98) iter 26: time=223.98 for 1 active reps approxLL diffs: (1.98,1.98) iter 27: time=216.07 for 1 active reps approxLL diffs: (1.27,1.27) iter 28: time=217.92 for 1 active reps approxLL diffs: (0.76,0.76) iter 29: time=217.89 for 1 active reps approxLL diffs: (0.41,0.41) iter 30: time=217.25 for 1 active reps approxLL diffs: (0.27,0.27) iter 31: time=216.63 for 1 active reps approxLL diffs: (0.19,0.19) iter 32: time=211.01 for 1 active reps approxLL diffs: (0.13,0.13) iter 33: time=207.81 for 1 active reps approxLL diffs: (0.09,0.09) iter 34: time=208.75 for 1 active reps approxLL diffs: (0.05,0.05) iter 35: time=208.89 for 1 active reps approxLL diffs: (0.02,0.02) iter 36: time=208.32 for 1 active reps approxLL diffs: (0.01,0.01) iter 37: time=208.44 for 1 active reps approxLL diffs: (0.01,0.01) iter 38: time=208.57 for 1 active reps approxLL diffs: (0.01,0.01) iter 39: time=208.08 for 1 active reps approxLL diffs: (0.02,0.02) iter 40: time=208.44 for 1 active reps approxLL diffs: (0.03,0.03) iter 41: time=208.21 for 1 active reps approxLL diffs: (0.09,0.09) iter 42: time=207.66 for 1 active reps approxLL diffs: (0.21,0.21) iter 43: time=219.68 for 1 active reps approxLL diffs: (0.35,0.35) iter 44: time=210.95 for 1 active reps approxLL diffs: (0.26,0.26) iter 45: time=212.29 for 1 active reps approxLL diffs: (0.08,0.08) iter 46: time=210.59 for 1 active reps approxLL diffs: (0.02,0.02) iter 47: time=210.95 for 1 active reps approxLL diffs: (0.02,0.02) iter 48: time=209.85 for 1 active reps approxLL diffs: (0.03,0.03) iter 49: time=209.35 for 1 active reps approxLL diffs: (0.04,0.04) iter 50: time=210.18 for 1 active reps approxLL diffs: (0.07,0.07) iter 51: time=211.34 for 1 active reps approxLL diffs: (0.14,0.14) iter 52: time=212.99 for 1 active reps approxLL diffs: (0.31,0.31) iter 53: time=208.09 for 1 active reps approxLL diffs: (0.62,0.62) iter 54: time=206.25 for 1 active reps approxLL diffs: (0.45,0.45) iter 55: time=205.80 for 1 active reps approxLL diffs: (0.24,0.24) iter 56: time=204.33 for 1 active reps approxLL diffs: (0.24,0.24) iter 57: time=204.48 for 1 active reps approxLL diffs: (0.23,0.23) iter 58: time=204.74 for 1 active reps approxLL diffs: (0.14,0.14) iter 59: time=203.06 for 1 active reps approxLL diffs: (0.04,0.04) iter 60: time=213.14 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 60: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 63.5%, memory/overhead = 36.5% Time for computing and writing betas = 13330.5 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 1.90209 (709963 good SNPs) lambdaGC: 1.51407 Mean BOLT_LMM_INF: 1.97981 (709963 good SNPs) lambdaGC: 1.53789 Mean BOLT_LMM: 1.99617 (709963 good SNPs) lambdaGC: 1.54188 Note that LINREG may be confounded by a factor of 1.00162 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 8114.52 sec Total elapsed time for analysis = 181688 sec Command being timed: "/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.hwe1000.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 --maxMissingPerSnp=0.15 --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab --phenoCol=bp_SYSTOLICadjMEDz --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.bp_SYSTOLICadjMEDz.predbetas.txt.gz --statsFile=bolt_460K_selfRepWhite.bp_SYSTOLICadjMEDz.stats.gz --verboseStats" User time (seconds): 1314909.07 System time (seconds): 56078.35 Percent of CPU this job got: 754% Elapsed (wall clock) time (h:mm:ss or m:ss): 50:28:41 Average shared text size (kbytes): 0 Average unshared data size (kbytes): 0 Average stack size (kbytes): 0 Average total size (kbytes): 0 Maximum resident set size (kbytes): 86917968 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 53 Minor (reclaiming a frame) page faults: 6354518962 Voluntary context switches: 2023870 Involuntary context switches: 58801927 Swaps: 0 File system inputs: 385475304 File system outputs: 96064 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0