+-----------------------------+ | ___ | | 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_DIASTOLICadjMEDz \ --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_DIASTOLICadjMEDz.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.bp_DIASTOLICadjMEDz.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 = 4659.63 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] = 6.16844e-12 S[47] = 8.15829e-13 S[48] = 1.75429e-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 = 4957.57 sec Phenotype 1: N = 422771 mean = -0.00735217 std = 0.995556 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 438.269 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=389.77 rNorms/orig: (0.6,0.6) res2s: 698013..156038 iter 2: time=606.57 rNorms/orig: (0.5,0.6) res2s: 825059..192959 iter 3: time=577.16 rNorms/orig: (0.3,0.3) res2s: 945068..225113 iter 4: time=587.82 rNorms/orig: (0.2,0.2) res2s: 991392..236822 iter 5: time=590.32 rNorms/orig: (0.1,0.1) res2s: 1.01389e+06..242814 iter 6: time=592.10 rNorms/orig: (0.08,0.09) res2s: 1.02412e+06..245485 iter 7: time=557.53 rNorms/orig: (0.05,0.05) res2s: 1.02946e+06..246789 iter 8: time=481.68 rNorms/orig: (0.03,0.04) res2s: 1.03167e+06..247397 iter 9: time=497.62 rNorms/orig: (0.02,0.02) res2s: 1.03259e+06..247696 iter 10: time=463.25 rNorms/orig: (0.01,0.01) res2s: 1.03306e+06..247823 iter 11: time=478.55 rNorms/orig: (0.009,0.01) res2s: 1.03327e+06..247888 iter 12: time=461.39 rNorms/orig: (0.005,0.006) res2s: 1.03337e+06..247915 iter 13: time=378.55 rNorms/orig: (0.004,0.004) res2s: 1.03341e+06..247927 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 44.9%, memory/overhead = 55.1% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0321519 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=383.13 rNorms/orig: (1,1) res2s: 76051..49600.6 iter 2: time=378.29 rNorms/orig: (1,1) res2s: 108191..73928.4 iter 3: time=384.65 rNorms/orig: (0.8,0.9) res2s: 161743..107991 iter 4: time=367.92 rNorms/orig: (0.6,0.6) res2s: 198054..127531 iter 5: time=360.80 rNorms/orig: (0.5,0.5) res2s: 223949..142117 iter 6: time=359.35 rNorms/orig: (0.4,0.4) res2s: 242004..151525 iter 7: time=359.75 rNorms/orig: (0.3,0.3) res2s: 256016..158073 iter 8: time=361.81 rNorms/orig: (0.2,0.2) res2s: 264212..162409 iter 9: time=364.67 rNorms/orig: (0.2,0.2) res2s: 269425..165473 iter 10: time=384.76 rNorms/orig: (0.1,0.1) res2s: 273689..167329 iter 11: time=387.42 rNorms/orig: (0.1,0.1) res2s: 276146..168690 iter 12: time=377.19 rNorms/orig: (0.08,0.08) res2s: 277878..169489 iter 13: time=373.99 rNorms/orig: (0.06,0.07) res2s: 278889..170015 iter 14: time=363.55 rNorms/orig: (0.05,0.05) res2s: 279585..170368 iter 15: time=374.14 rNorms/orig: (0.04,0.04) res2s: 279995..170589 iter 16: time=365.14 rNorms/orig: (0.03,0.03) res2s: 280258..170722 iter 17: time=376.10 rNorms/orig: (0.02,0.02) res2s: 280415..170806 iter 18: time=366.57 rNorms/orig: (0.02,0.02) res2s: 280510..170860 iter 19: time=367.50 rNorms/orig: (0.01,0.01) res2s: 280577..170896 iter 20: time=365.57 rNorms/orig: (0.01,0.01) res2s: 280613..170916 iter 21: time=364.82 rNorms/orig: (0.008,0.008) res2s: 280640..170929 iter 22: time=370.31 rNorms/orig: (0.006,0.006) res2s: 280655..170938 iter 23: time=364.32 rNorms/orig: (0.004,0.005) res2s: 280665..170942 Converged at iter 23: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 43.2%, memory/overhead = 56.8% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.268474 Estimating MC scaling f_REML at log(delta) = 0.975358, h2 = 0.27267... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=362.43 rNorms/orig: (0.7,0.7) res2s: 570637..142009 iter 2: time=362.17 rNorms/orig: (0.6,0.6) res2s: 687750..178947 iter 3: time=359.19 rNorms/orig: (0.3,0.4) res2s: 806292..212975 iter 4: time=361.36 rNorms/orig: (0.2,0.2) res2s: 854939..226056 iter 5: time=357.14 rNorms/orig: (0.1,0.2) res2s: 879613..233073 iter 6: time=354.00 rNorms/orig: (0.1,0.1) res2s: 891446..236354 iter 7: time=366.34 rNorms/orig: (0.06,0.07) res2s: 897940..238032 iter 8: time=365.40 rNorms/orig: (0.04,0.05) res2s: 900745..238852 iter 9: time=363.23 rNorms/orig: (0.03,0.03) res2s: 901982..239275 iter 10: time=353.42 rNorms/orig: (0.02,0.02) res2s: 902652..239464 iter 11: time=357.23 rNorms/orig: (0.01,0.01) res2s: 902964..239566 iter 12: time=368.96 rNorms/orig: (0.008,0.009) res2s: 903120..239609 iter 13: time=369.71 rNorms/orig: (0.005,0.006) res2s: 903189..239630 iter 14: time=369.33 rNorms/orig: (0.003,0.004) res2s: 903223..239640 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 43.3%, memory/overhead = 56.7% MCscaling: logDelta = 0.98, h2 = 0.273, f = 0.00102868 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.273 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.266226, logDelta = 0.975358, f = 0.00102868 Time for fitting variance components = 20975.3 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=1027.24 rNorms/orig: (0.4,0.7) res2s: 141969..212614 iter 2: time=958.22 rNorms/orig: (0.3,0.7) res2s: 179564..236786 iter 3: time=967.19 rNorms/orig: (0.2,0.4) res2s: 214829..266446 iter 4: time=1075.64 rNorms/orig: (0.2,0.3) res2s: 229232..273618 iter 5: time=1091.66 rNorms/orig: (0.1,0.2) res2s: 237059..279165 iter 6: time=1013.40 rNorms/orig: (0.07,0.1) res2s: 240766..281660 iter 7: time=964.86 rNorms/orig: (0.05,0.08) res2s: 242757..283011 iter 8: time=801.90 rNorms/orig: (0.03,0.05) res2s: 243783..283664 iter 9: time=682.93 rNorms/orig: (0.02,0.04) res2s: 244283..283943 iter 10: time=704.95 rNorms/orig: (0.01,0.02) res2s: 244548..284089 iter 11: time=695.92 rNorms/orig: (0.008,0.02) res2s: 244674..284161 iter 12: time=695.31 rNorms/orig: (0.005,0.01) res2s: 244736..284192 iter 13: time=692.03 rNorms/orig: (0.003,0.007) res2s: 244765..284206 iter 14: time=677.54 rNorms/orig: (0.002,0.005) res2s: 244780..284213 iter 15: time=678.80 rNorms/orig: (0.001,0.003) res2s: 244786..284216 iter 16: time=686.92 rNorms/orig: (0.0007,0.002) res2s: 244790..284218 iter 17: time=684.56 rNorms/orig: (0.0004,0.001) res2s: 244791..284219 iter 18: time=684.81 rNorms/orig: (0.0003,0.0009) res2s: 244792..284219 iter 19: time=690.23 rNorms/orig: (0.0002,0.0006) res2s: 244792..284219 iter 20: time=676.02 rNorms/orig: (9e-05,0.0004) res2s: 244792..284219 Converged at iter 20: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 66.7%, memory/overhead = 33.3% AvgPro: 2.464 AvgRetro: 2.412 Calibration: 1.022 (0.010) (30 SNPs) Ratio of medians: 1.011 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 16554.8 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 13.7563 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: 582625/583483 Intercept of LD Score regression for ref stats: 1.191 (0.013) Estimated attenuation: 0.175 (0.013) Intercept of LD Score regression for cur stats: 1.184 (0.012) Calibration factor (ref/cur) to multiply by: 1.006 (0.002) LINREG intercept inflation = 0.994429 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] = 4.00183e-12 S[47] = 4.36388e-13 S[48] = 2.14606e-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=641.93 for 18 active reps iter 2: time=423.91 for 18 active reps approxLL diffs: (6850.92,8971.92) iter 3: time=426.82 for 18 active reps approxLL diffs: (631.81,1226.81) iter 4: time=423.40 for 18 active reps approxLL diffs: (99.10,426.03) iter 5: time=433.07 for 18 active reps approxLL diffs: (24.81,230.35) iter 6: time=429.12 for 18 active reps approxLL diffs: (8.79,124.78) iter 7: time=425.26 for 18 active reps approxLL diffs: (4.10,75.68) iter 8: time=423.57 for 18 active reps approxLL diffs: (2.27,52.45) iter 9: time=422.81 for 18 active reps approxLL diffs: (1.38,43.44) iter 10: time=426.96 for 18 active reps approxLL diffs: (0.87,31.98) iter 11: time=424.17 for 18 active reps approxLL diffs: (0.57,24.61) iter 12: time=422.95 for 18 active reps approxLL diffs: (0.38,17.29) iter 13: time=424.08 for 18 active reps approxLL diffs: (0.25,19.01) iter 14: time=424.77 for 18 active reps approxLL diffs: (0.17,14.41) iter 15: time=420.60 for 18 active reps approxLL diffs: (0.11,11.28) iter 16: time=433.07 for 18 active reps approxLL diffs: (0.07,10.88) iter 17: time=430.29 for 18 active reps approxLL diffs: (0.05,9.50) iter 18: time=434.62 for 18 active reps approxLL diffs: (0.04,5.48) iter 19: time=432.01 for 18 active reps approxLL diffs: (0.03,6.04) iter 20: time=423.30 for 18 active reps approxLL diffs: (0.02,5.28) iter 21: time=424.44 for 18 active reps approxLL diffs: (0.01,4.18) iter 22: time=438.71 for 18 active reps approxLL diffs: (0.01,4.52) iter 23: time=427.78 for 17 active reps approxLL diffs: (0.01,3.03) iter 24: time=419.82 for 17 active reps approxLL diffs: (0.01,3.14) iter 25: time=396.73 for 16 active reps approxLL diffs: (0.01,2.53) iter 26: time=414.98 for 15 active reps approxLL diffs: (0.01,2.02) iter 27: time=381.48 for 14 active reps approxLL diffs: (0.01,2.52) iter 28: time=366.67 for 13 active reps approxLL diffs: (0.01,4.22) iter 29: time=366.02 for 11 active reps approxLL diffs: (0.03,2.39) iter 30: time=369.66 for 11 active reps approxLL diffs: (0.03,1.80) iter 31: time=379.17 for 11 active reps approxLL diffs: (0.03,1.81) iter 32: time=375.37 for 11 active reps approxLL diffs: (0.03,2.27) iter 33: time=372.10 for 11 active reps approxLL diffs: (0.03,3.19) iter 34: time=380.75 for 11 active reps approxLL diffs: (0.02,5.67) iter 35: time=371.75 for 11 active reps approxLL diffs: (0.02,3.15) iter 36: time=370.37 for 11 active reps approxLL diffs: (0.00,1.63) iter 37: time=345.53 for 10 active reps approxLL diffs: (0.01,1.59) iter 38: time=341.37 for 10 active reps approxLL diffs: (0.01,1.06) iter 39: time=330.42 for 9 active reps approxLL diffs: (0.02,2.03) iter 40: time=332.66 for 9 active reps approxLL diffs: (0.02,1.68) iter 41: time=329.80 for 9 active reps approxLL diffs: (0.01,1.13) iter 42: time=334.26 for 9 active reps approxLL diffs: (0.01,0.34) iter 43: time=328.71 for 9 active reps approxLL diffs: (0.01,1.27) iter 44: time=300.55 for 8 active reps approxLL diffs: (0.01,3.37) iter 45: time=294.52 for 8 active reps approxLL diffs: (0.01,1.43) iter 46: time=298.22 for 8 active reps approxLL diffs: (0.01,0.68) iter 47: time=301.91 for 8 active reps approxLL diffs: (0.01,0.67) iter 48: time=295.24 for 8 active reps approxLL diffs: (0.01,0.55) iter 49: time=304.05 for 8 active reps approxLL diffs: (0.02,0.47) iter 50: time=293.27 for 8 active reps approxLL diffs: (0.02,0.36) iter 51: time=289.53 for 8 active reps approxLL diffs: (0.02,0.25) iter 52: time=286.02 for 8 active reps approxLL diffs: (0.01,0.42) iter 53: time=286.15 for 8 active reps approxLL diffs: (0.01,0.48) iter 54: time=313.25 for 7 active reps approxLL diffs: (0.02,0.28) iter 55: time=318.31 for 7 active reps approxLL diffs: (0.01,0.12) iter 56: time=303.69 for 6 active reps approxLL diffs: (0.01,0.11) iter 57: time=299.78 for 6 active reps approxLL diffs: (0.01,0.11) iter 58: time=297.03 for 6 active reps approxLL diffs: (0.01,0.08) iter 59: time=261.11 for 4 active reps approxLL diffs: (0.03,0.04) iter 60: time=265.84 for 4 active reps approxLL diffs: (0.02,0.12) iter 61: time=275.37 for 4 active reps approxLL diffs: (0.02,0.45) iter 62: time=274.05 for 4 active reps approxLL diffs: (0.03,0.62) iter 63: time=260.14 for 4 active reps approxLL diffs: (0.02,0.26) iter 64: time=263.82 for 4 active reps approxLL diffs: (0.01,0.69) iter 65: time=268.88 for 3 active reps approxLL diffs: (0.01,2.24) iter 66: time=256.08 for 2 active reps approxLL diffs: (0.10,0.73) iter 67: time=264.02 for 2 active reps approxLL diffs: (0.04,0.95) iter 68: time=264.14 for 2 active reps approxLL diffs: (0.02,1.38) iter 69: time=256.18 for 2 active reps approxLL diffs: (0.02,0.29) iter 70: time=254.52 for 2 active reps approxLL diffs: (0.02,0.09) iter 71: time=256.85 for 2 active reps approxLL diffs: (0.02,0.07) iter 72: time=264.75 for 2 active reps approxLL diffs: (0.02,0.07) iter 73: time=253.57 for 2 active reps approxLL diffs: (0.03,0.07) iter 74: time=255.85 for 2 active reps approxLL diffs: (0.05,0.08) iter 75: time=251.73 for 2 active reps approxLL diffs: (0.08,0.12) iter 76: time=249.98 for 2 active reps approxLL diffs: (0.12,0.27) iter 77: time=268.29 for 2 active reps approxLL diffs: (0.14,0.72) iter 78: time=266.38 for 2 active reps approxLL diffs: (0.11,1.12) iter 79: time=254.78 for 2 active reps approxLL diffs: (0.07,1.03) iter 80: time=252.05 for 2 active reps approxLL diffs: (0.04,0.72) iter 81: time=260.59 for 2 active reps approxLL diffs: (0.02,0.59) iter 82: time=268.54 for 2 active reps approxLL diffs: (0.02,0.32) iter 83: time=262.14 for 2 active reps approxLL diffs: (0.02,0.19) iter 84: time=258.31 for 2 active reps approxLL diffs: (0.01,0.15) iter 85: time=264.17 for 2 active reps approxLL diffs: (0.01,0.24) iter 86: time=258.85 for 2 active reps approxLL diffs: (0.01,0.74) iter 87: time=277.88 for 2 active reps approxLL diffs: (0.01,0.94) iter 88: time=231.80 for 1 active reps approxLL diffs: (0.66,0.66) iter 89: time=212.33 for 1 active reps approxLL diffs: (0.57,0.57) iter 90: time=212.51 for 1 active reps approxLL diffs: (0.14,0.14) iter 91: time=212.67 for 1 active reps approxLL diffs: (0.02,0.02) iter 92: time=218.54 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 92: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 67.4%, memory/overhead = 32.6% Computing predictions on left-out cross-validation fold Time for computing predictions = 8167.14 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.05: 0.117648 f2=0.5, p=0.02: 0.117499 f2=0.1, p=0.1: 0.116586 ... f2=0.5, p=0.5: 0.095733 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.97853 Absolute prediction MSE using standard LMM: 0.884853 Absolute prediction MSE, fold-best f2=0.3, p=0.05: 0.863408 Absolute pred MSE using f2=0.5, p=0.5: 0.884853 Absolute pred MSE using f2=0.5, p=0.2: 0.878412 Absolute pred MSE using f2=0.5, p=0.1: 0.871182 Absolute pred MSE using f2=0.5, p=0.05: 0.865278 Absolute pred MSE using f2=0.5, p=0.02: 0.863554 Absolute pred MSE using f2=0.5, p=0.01: 0.865452 Absolute pred MSE using f2=0.3, p=0.5: 0.882836 Absolute pred MSE using f2=0.3, p=0.2: 0.873054 Absolute pred MSE using f2=0.3, p=0.1: 0.866223 Absolute pred MSE using f2=0.3, p=0.05: 0.863408 Absolute pred MSE using f2=0.3, p=0.02: 0.867645 Absolute pred MSE using f2=0.3, p=0.01: 0.871633 Absolute pred MSE using f2=0.1, p=0.5: 0.879158 Absolute pred MSE using f2=0.1, p=0.2: 0.868795 Absolute pred MSE using f2=0.1, p=0.1: 0.864447 Absolute pred MSE using f2=0.1, p=0.05: 0.867737 Absolute pred MSE using f2=0.1, p=0.02: 0.878936 Absolute pred MSE using f2=0.1, p=0.01: 0.884434 ====> 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: 0.024 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.05 Time for estimating mixture parameters = 43555.7 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=676.12 for 23 active reps iter 2: time=465.57 for 23 active reps approxLL diffs: (11035.10,11627.87) iter 3: time=467.28 for 23 active reps approxLL diffs: (1542.43,1643.15) iter 4: time=470.82 for 23 active reps approxLL diffs: (392.53,431.36) iter 5: time=474.88 for 23 active reps approxLL diffs: (154.93,174.23) iter 6: time=474.32 for 23 active reps approxLL diffs: (76.12,85.51) iter 7: time=465.24 for 23 active reps approxLL diffs: (44.55,52.11) iter 8: time=459.35 for 23 active reps approxLL diffs: (31.54,37.39) iter 9: time=462.51 for 23 active reps approxLL diffs: (21.37,27.58) iter 10: time=481.33 for 23 active reps approxLL diffs: (14.83,21.14) iter 11: time=457.15 for 23 active reps approxLL diffs: (10.73,15.19) iter 12: time=491.70 for 23 active reps approxLL diffs: (8.31,12.06) iter 13: time=476.15 for 23 active reps approxLL diffs: (5.76,9.15) iter 14: time=473.89 for 23 active reps approxLL diffs: (4.08,6.76) iter 15: time=467.65 for 23 active reps approxLL diffs: (2.45,5.58) iter 16: time=454.55 for 23 active reps approxLL diffs: (1.75,4.56) iter 17: time=476.66 for 23 active reps approxLL diffs: (1.60,4.43) iter 18: time=462.18 for 23 active reps approxLL diffs: (1.42,2.94) iter 19: time=494.14 for 23 active reps approxLL diffs: (1.04,3.04) iter 20: time=464.22 for 23 active reps approxLL diffs: (0.75,2.58) iter 21: time=477.64 for 23 active reps approxLL diffs: (0.60,2.39) iter 22: time=483.02 for 23 active reps approxLL diffs: (0.50,2.18) iter 23: time=461.51 for 23 active reps approxLL diffs: (0.46,2.59) iter 24: time=484.49 for 23 active reps approxLL diffs: (0.45,2.01) iter 25: time=461.31 for 23 active reps approxLL diffs: (0.29,1.88) iter 26: time=480.72 for 23 active reps approxLL diffs: (0.20,1.80) iter 27: time=463.24 for 23 active reps approxLL diffs: (0.17,1.76) iter 28: time=466.93 for 23 active reps approxLL diffs: (0.17,1.80) iter 29: time=463.25 for 23 active reps approxLL diffs: (0.11,1.14) iter 30: time=482.18 for 23 active reps approxLL diffs: (0.07,1.64) iter 31: time=468.09 for 23 active reps approxLL diffs: (0.06,1.02) iter 32: time=458.18 for 23 active reps approxLL diffs: (0.06,0.65) iter 33: time=473.11 for 23 active reps approxLL diffs: (0.09,0.80) iter 34: time=468.25 for 23 active reps approxLL diffs: (0.11,1.26) iter 35: time=458.39 for 23 active reps approxLL diffs: (0.10,1.25) iter 36: time=481.14 for 23 active reps approxLL diffs: (0.07,0.77) iter 37: time=465.39 for 23 active reps approxLL diffs: (0.05,0.59) iter 38: time=455.37 for 23 active reps approxLL diffs: (0.02,0.77) iter 39: time=487.70 for 23 active reps approxLL diffs: (0.01,0.70) iter 40: time=442.87 for 22 active reps approxLL diffs: (0.01,1.06) iter 41: time=434.63 for 22 active reps approxLL diffs: (0.01,0.36) iter 42: time=453.37 for 21 active reps approxLL diffs: (0.01,0.49) iter 43: time=404.26 for 20 active reps approxLL diffs: (0.03,0.45) iter 44: time=403.56 for 20 active reps approxLL diffs: (0.02,0.36) iter 45: time=412.39 for 20 active reps approxLL diffs: (0.01,0.41) iter 46: time=443.00 for 19 active reps approxLL diffs: (0.01,0.39) iter 47: time=407.64 for 18 active reps approxLL diffs: (0.02,0.29) iter 48: time=411.85 for 18 active reps approxLL diffs: (0.02,0.18) iter 49: time=414.39 for 18 active reps approxLL diffs: (0.01,0.30) iter 50: time=405.77 for 18 active reps approxLL diffs: (0.01,0.59) iter 51: time=394.20 for 17 active reps approxLL diffs: (0.01,0.78) iter 52: time=366.36 for 16 active reps approxLL diffs: (0.00,0.55) iter 53: time=351.26 for 13 active reps approxLL diffs: (0.01,0.23) iter 54: time=335.79 for 12 active reps approxLL diffs: (0.01,0.35) iter 55: time=353.84 for 11 active reps approxLL diffs: (0.01,0.21) iter 56: time=330.03 for 10 active reps approxLL diffs: (0.01,0.18) iter 57: time=282.55 for 8 active reps approxLL diffs: (0.01,0.23) iter 58: time=283.95 for 8 active reps approxLL diffs: (0.02,0.17) iter 59: time=292.20 for 8 active reps approxLL diffs: (0.02,0.15) iter 60: time=279.22 for 8 active reps approxLL diffs: (0.01,0.33) iter 61: time=279.75 for 8 active reps approxLL diffs: (0.01,0.59) iter 62: time=306.88 for 7 active reps approxLL diffs: (0.01,0.47) iter 63: time=306.60 for 7 active reps approxLL diffs: (0.01,0.26) iter 64: time=266.10 for 5 active reps approxLL diffs: (0.01,0.18) iter 65: time=266.36 for 5 active reps approxLL diffs: (0.00,0.06) iter 66: time=260.19 for 3 active reps approxLL diffs: (0.02,0.03) iter 67: time=265.70 for 3 active reps approxLL diffs: (0.02,0.03) iter 68: time=261.52 for 3 active reps approxLL diffs: (0.02,0.04) iter 69: time=260.28 for 3 active reps approxLL diffs: (0.02,0.07) iter 70: time=267.09 for 3 active reps approxLL diffs: (0.02,0.10) iter 71: time=274.14 for 3 active reps approxLL diffs: (0.02,0.09) iter 72: time=260.59 for 3 active reps approxLL diffs: (0.01,0.04) iter 73: time=259.99 for 3 active reps approxLL diffs: (0.01,0.02) iter 74: time=213.97 for 1 active reps approxLL diffs: (0.01,0.01) iter 75: time=221.87 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 75: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 74.0%, memory/overhead = 26.0% 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: 582625/583483 Intercept of LD Score regression for ref stats: 1.191 (0.013) Estimated attenuation: 0.175 (0.013) Intercept of LD Score regression for cur stats: 1.173 (0.013) Calibration factor (ref/cur) to multiply by: 1.015 (0.001) Time for computing Bayesian mixed model assoc stats = 30820.3 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=415.33 for 1 active reps iter 2: time=213.90 for 1 active reps approxLL diffs: (11688.50,11688.50) iter 3: time=210.99 for 1 active reps approxLL diffs: (1635.95,1635.95) iter 4: time=209.92 for 1 active reps approxLL diffs: (426.81,426.81) iter 5: time=207.68 for 1 active reps approxLL diffs: (167.95,167.95) iter 6: time=207.15 for 1 active reps approxLL diffs: (81.63,81.63) iter 7: time=210.38 for 1 active reps approxLL diffs: (47.77,47.77) iter 8: time=207.97 for 1 active reps approxLL diffs: (34.46,34.46) iter 9: time=207.10 for 1 active reps approxLL diffs: (23.40,23.40) iter 10: time=207.97 for 1 active reps approxLL diffs: (17.51,17.51) iter 11: time=211.96 for 1 active reps approxLL diffs: (14.25,14.25) iter 12: time=217.80 for 1 active reps approxLL diffs: (10.69,10.69) iter 13: time=215.86 for 1 active reps approxLL diffs: (7.23,7.23) iter 14: time=213.88 for 1 active reps approxLL diffs: (4.19,4.19) iter 15: time=212.52 for 1 active reps approxLL diffs: (2.93,2.93) iter 16: time=208.76 for 1 active reps approxLL diffs: (3.40,3.40) iter 17: time=210.64 for 1 active reps approxLL diffs: (3.29,3.29) iter 18: time=208.38 for 1 active reps approxLL diffs: (1.86,1.86) iter 19: time=209.22 for 1 active reps approxLL diffs: (1.48,1.48) iter 20: time=212.08 for 1 active reps approxLL diffs: (1.42,1.42) iter 21: time=220.04 for 1 active reps approxLL diffs: (1.30,1.30) iter 22: time=219.53 for 1 active reps approxLL diffs: (1.29,1.29) iter 23: time=213.30 for 1 active reps approxLL diffs: (1.33,1.33) iter 24: time=209.75 for 1 active reps approxLL diffs: (1.29,1.29) iter 25: time=208.29 for 1 active reps approxLL diffs: (0.93,0.93) iter 26: time=212.80 for 1 active reps approxLL diffs: (0.50,0.50) iter 27: time=212.11 for 1 active reps approxLL diffs: (0.35,0.35) iter 28: time=211.95 for 1 active reps approxLL diffs: (0.26,0.26) iter 29: time=210.71 for 1 active reps approxLL diffs: (0.18,0.18) iter 30: time=211.71 for 1 active reps approxLL diffs: (0.17,0.17) iter 31: time=214.31 for 1 active reps approxLL diffs: (0.25,0.25) iter 32: time=211.27 for 1 active reps approxLL diffs: (0.38,0.38) iter 33: time=210.59 for 1 active reps approxLL diffs: (0.31,0.31) iter 34: time=210.49 for 1 active reps approxLL diffs: (0.19,0.19) iter 35: time=213.39 for 1 active reps approxLL diffs: (0.16,0.16) iter 36: time=213.49 for 1 active reps approxLL diffs: (0.18,0.18) iter 37: time=210.28 for 1 active reps approxLL diffs: (0.22,0.22) iter 38: time=209.68 for 1 active reps approxLL diffs: (0.17,0.17) iter 39: time=210.25 for 1 active reps approxLL diffs: (0.08,0.08) iter 40: time=214.37 for 1 active reps approxLL diffs: (0.06,0.06) iter 41: time=212.91 for 1 active reps approxLL diffs: (0.06,0.06) iter 42: time=209.23 for 1 active reps approxLL diffs: (0.06,0.06) iter 43: time=210.04 for 1 active reps approxLL diffs: (0.04,0.04) iter 44: time=210.65 for 1 active reps approxLL diffs: (0.02,0.02) iter 45: time=212.79 for 1 active reps approxLL diffs: (0.01,0.01) iter 46: time=210.02 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 46: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 53.1%, memory/overhead = 46.9% Time for computing and writing betas = 9938.63 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 1.90581 (709963 good SNPs) lambdaGC: 1.51807 Mean BOLT_LMM_INF: 1.99777 (709963 good SNPs) lambdaGC: 1.54894 Mean BOLT_LMM: 2.01891 (709963 good SNPs) lambdaGC: 1.55341 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 11571 sec Total elapsed time for analysis = 143485 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_DIASTOLICadjMEDz --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_DIASTOLICadjMEDz.predbetas.txt.gz --statsFile=bolt_460K_selfRepWhite.bp_DIASTOLICadjMEDz.stats.gz --verboseStats" User time (seconds): 1032337.37 System time (seconds): 14285.43 Percent of CPU this job got: 729% Elapsed (wall clock) time (h:mm:ss or m:ss): 39:51:58 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): 86974552 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 62377 Minor (reclaiming a frame) page faults: 2459469252 Voluntary context switches: 2888085 Involuntary context switches: 8232233 Swaps: 0 File system inputs: 389021032 File system outputs: 101456 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0