+-----------------------------+ | ___ | | 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=lung_FEV1FVCzSMOKE \ --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.lung_FEV1FVCzSMOKE.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.lung_FEV1FVCzSMOKE.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 = 2978.13 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: 371949 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 = 371949 Singular values of covariate matrix: S[0] = 2.05313e+06 S[1] = 4391.54 S[2] = 429.512 S[3] = 289.878 S[4] = 182.899 S[5] = 176.599 S[6] = 166.074 S[7] = 162.661 S[8] = 157.641 S[9] = 153.682 S[10] = 149.613 S[11] = 141.667 S[12] = 134.976 S[13] = 131.41 S[14] = 129.138 S[15] = 123.32 S[16] = 119.087 S[17] = 115.655 S[18] = 113.152 S[19] = 105.623 S[20] = 101.272 S[21] = 93.6447 S[22] = 41.1553 S[23] = 22.0198 S[24] = 17.7541 S[25] = 0.901645 S[26] = 0.900881 S[27] = 0.900726 S[28] = 0.900129 S[29] = 0.899705 S[30] = 0.899389 S[31] = 0.89935 S[32] = 0.89897 S[33] = 0.898686 S[34] = 0.898641 S[35] = 0.898118 S[36] = 0.89801 S[37] = 0.897649 S[38] = 0.897516 S[39] = 0.897199 S[40] = 0.897037 S[41] = 0.896208 S[42] = 0.895275 S[43] = 0.871277 S[44] = 0.806181 S[45] = 1.95359e-11 S[46] = 4.78058e-12 S[47] = 4.76082e-13 S[48] = 0 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 45 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 369774.214622 Dimension of all-1s proj space (Nused-1): 371948 Time for covariate data setup + Bolt initialization = 5291.62 sec Phenotype 1: N = 371949 mean = -0.00836307 std = 0.996107 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 435.947 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 371949) Estimating MC scaling f_REML at log(delta) = 1.09287, h2 = 0.25... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=371.79 rNorms/orig: (0.5,0.6) res2s: 638371..129701 iter 2: time=352.29 rNorms/orig: (0.5,0.5) res2s: 747012..163531 iter 3: time=342.56 rNorms/orig: (0.2,0.3) res2s: 843471..187289 iter 4: time=342.90 rNorms/orig: (0.2,0.2) res2s: 876252..196658 iter 5: time=348.63 rNorms/orig: (0.1,0.1) res2s: 891743..201559 iter 6: time=344.49 rNorms/orig: (0.06,0.07) res2s: 898782..203800 iter 7: time=356.27 rNorms/orig: (0.04,0.04) res2s: 902096..204785 iter 8: time=343.19 rNorms/orig: (0.02,0.03) res2s: 903489..205250 iter 9: time=345.46 rNorms/orig: (0.02,0.02) res2s: 904113..205417 iter 10: time=353.66 rNorms/orig: (0.01,0.01) res2s: 904406..205490 iter 11: time=347.10 rNorms/orig: (0.006,0.007) res2s: 904526..205521 iter 12: time=348.12 rNorms/orig: (0.004,0.004) res2s: 904578..205535 Converged at iter 12: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.7%, memory/overhead = 49.3% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0832048 Estimating MC scaling f_REML at log(delta) = -0.00574317, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=355.14 rNorms/orig: (0.9,1) res2s: 74438.6..40277.3 iter 2: time=348.35 rNorms/orig: (1,1) res2s: 104377..62373.5 iter 3: time=350.13 rNorms/orig: (0.7,0.8) res2s: 152358..87435.5 iter 4: time=353.64 rNorms/orig: (0.5,0.6) res2s: 181517..102725 iter 5: time=357.66 rNorms/orig: (0.4,0.4) res2s: 201548..114491 iter 6: time=352.70 rNorms/orig: (0.3,0.3) res2s: 215534..122478 iter 7: time=348.26 rNorms/orig: (0.2,0.2) res2s: 225476..127648 iter 8: time=349.02 rNorms/orig: (0.2,0.2) res2s: 231438..131338 iter 9: time=348.43 rNorms/orig: (0.1,0.1) res2s: 235417..133339 iter 10: time=351.53 rNorms/orig: (0.1,0.1) res2s: 238149..134619 iter 11: time=344.41 rNorms/orig: (0.08,0.09) res2s: 239833..135421 iter 12: time=349.55 rNorms/orig: (0.06,0.06) res2s: 240888..135942 iter 13: time=353.27 rNorms/orig: (0.05,0.05) res2s: 241519..136248 iter 14: time=353.69 rNorms/orig: (0.03,0.04) res2s: 241951..136435 iter 15: time=352.96 rNorms/orig: (0.03,0.03) res2s: 242188..136558 iter 16: time=352.65 rNorms/orig: (0.02,0.02) res2s: 242344..136626 iter 17: time=356.09 rNorms/orig: (0.02,0.02) res2s: 242434..136667 iter 18: time=351.71 rNorms/orig: (0.01,0.01) res2s: 242495..136691 iter 19: time=349.97 rNorms/orig: (0.008,0.009) res2s: 242529..136708 iter 20: time=349.15 rNorms/orig: (0.007,0.007) res2s: 242549..136717 iter 21: time=342.61 rNorms/orig: (0.005,0.005) res2s: 242560..136722 iter 22: time=344.19 rNorms/orig: (0.004,0.004) res2s: 242567..136726 Converged at iter 22: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.5%, memory/overhead = 49.5% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.213619 Estimating MC scaling f_REML at log(delta) = 0.784909, h2 = 0.312029... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=331.42 rNorms/orig: (0.7,0.7) res2s: 375490..99231.7 iter 2: time=349.06 rNorms/orig: (0.6,0.7) res2s: 462151..132298 iter 3: time=352.96 rNorms/orig: (0.4,0.4) res2s: 554751..159207 iter 4: time=348.60 rNorms/orig: (0.2,0.3) res2s: 591913..171340 iter 5: time=355.80 rNorms/orig: (0.2,0.2) res2s: 611560..178516 iter 6: time=352.74 rNorms/orig: (0.1,0.1) res2s: 621793..182247 iter 7: time=339.64 rNorms/orig: (0.07,0.08) res2s: 627287..184109 iter 8: time=347.92 rNorms/orig: (0.05,0.05) res2s: 629876..185118 iter 9: time=349.54 rNorms/orig: (0.03,0.03) res2s: 631202..185535 iter 10: time=343.25 rNorms/orig: (0.02,0.02) res2s: 631908..185741 iter 11: time=349.47 rNorms/orig: (0.01,0.02) res2s: 632239..185841 iter 12: time=345.88 rNorms/orig: (0.009,0.01) res2s: 632400..185891 iter 13: time=345.20 rNorms/orig: (0.006,0.007) res2s: 632474..185914 iter 14: time=353.47 rNorms/orig: (0.004,0.004) res2s: 632513..185924 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.9%, memory/overhead = 49.1% MCscaling: logDelta = 0.78, h2 = 0.312, f = 0.00168962 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.312 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.295612, logDelta = 0.784909, f = 0.00168962 Time for fitting variance components = 17830.9 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=1000.93 rNorms/orig: (0.5,1) res2s: 99311.7..167798 iter 2: time=1002.98 rNorms/orig: (0.4,0.7) res2s: 132816..190486 iter 3: time=981.94 rNorms/orig: (0.2,0.4) res2s: 161010..216493 iter 4: time=1000.23 rNorms/orig: (0.2,0.3) res2s: 174300..223293 iter 5: time=1003.44 rNorms/orig: (0.1,0.2) res2s: 182195..228703 iter 6: time=980.32 rNorms/orig: (0.07,0.1) res2s: 186330..231036 iter 7: time=978.30 rNorms/orig: (0.05,0.09) res2s: 188601..232496 iter 8: time=967.85 rNorms/orig: (0.03,0.06) res2s: 189776..233156 iter 9: time=958.74 rNorms/orig: (0.02,0.04) res2s: 190296..233438 iter 10: time=959.59 rNorms/orig: (0.02,0.03) res2s: 190567..233603 iter 11: time=958.02 rNorms/orig: (0.01,0.02) res2s: 190701..233682 iter 12: time=957.26 rNorms/orig: (0.007,0.01) res2s: 190770..233714 iter 13: time=958.95 rNorms/orig: (0.004,0.009) res2s: 190803..233731 iter 14: time=957.26 rNorms/orig: (0.003,0.006) res2s: 190818..233739 iter 15: time=958.11 rNorms/orig: (0.002,0.004) res2s: 190826..233743 iter 16: time=955.81 rNorms/orig: (0.001,0.003) res2s: 190830..233745 iter 17: time=993.60 rNorms/orig: (0.0006,0.002) res2s: 190831..233746 iter 18: time=1042.86 rNorms/orig: (0.0004,0.001) res2s: 190832..233746 iter 19: time=1087.56 rNorms/orig: (0.0002,0.0008) res2s: 190833..233746 iter 20: time=1129.31 rNorms/orig: (0.0001,0.0005) res2s: 190833..233746 iter 21: time=1194.34 rNorms/orig: (9e-05,0.0004) res2s: 190833..233746 Converged at iter 21: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 81.1%, memory/overhead = 18.9% AvgPro: 2.162 AvgRetro: 2.133 Calibration: 1.013 (0.001) (30 SNPs) Ratio of medians: 1.013 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 21546.3 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 20.0205 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 > 371.9) # of SNPs remaining after outlier window removal: 578079/583483 Intercept of LD Score regression for ref stats: 1.164 (0.013) Estimated attenuation: 0.160 (0.014) Intercept of LD Score regression for cur stats: 1.165 (0.012) Calibration factor (ref/cur) to multiply by: 0.999 (0.003) LINREG intercept inflation = 1.00081 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 = 297559 Singular values of covariate matrix: S[0] = 1.83693e+06 S[1] = 3929.04 S[2] = 384.18 S[3] = 259.273 S[4] = 163.598 S[5] = 158.116 S[6] = 148.76 S[7] = 145.515 S[8] = 140.94 S[9] = 137.421 S[10] = 133.761 S[11] = 126.615 S[12] = 120.598 S[13] = 117.501 S[14] = 115.47 S[15] = 110.411 S[16] = 106.622 S[17] = 103.487 S[18] = 101.06 S[19] = 94.4498 S[20] = 90.6021 S[21] = 83.5205 S[22] = 36.7414 S[23] = 19.5997 S[24] = 15.8897 S[25] = 0.807293 S[26] = 0.806866 S[27] = 0.805938 S[28] = 0.80526 S[29] = 0.805167 S[30] = 0.804861 S[31] = 0.804031 S[32] = 0.803541 S[33] = 0.80335 S[34] = 0.803005 S[35] = 0.802722 S[36] = 0.802315 S[37] = 0.802194 S[38] = 0.801809 S[39] = 0.80163 S[40] = 0.801322 S[41] = 0.79937 S[42] = 0.79912 S[43] = 0.77701 S[44] = 0.720309 S[45] = 1.27522e-11 S[46] = 5.93919e-12 S[47] = 3.76877e-13 S[48] = 0 Total covariate vectors: C = 49 Total independent covariate vectors: Cindep = 45 === Initializing Bolt object: projecting and normalizing SNPs === Number of chroms with >= 1 good SNP: 23 Average norm of projected SNPs: 295811.863681 Dimension of all-1s proj space (Nused-1): 297558 Beginning variational Bayes iter 1: time=1271.77 for 18 active reps iter 2: time=648.47 for 18 active reps approxLL diffs: (9079.62,11091.09) iter 3: time=583.69 for 18 active reps approxLL diffs: (814.51,1709.46) iter 4: time=560.95 for 18 active reps approxLL diffs: (142.25,624.59) iter 5: time=557.55 for 18 active reps approxLL diffs: (37.87,325.01) iter 6: time=538.01 for 18 active reps approxLL diffs: (13.38,179.85) iter 7: time=547.23 for 18 active reps approxLL diffs: (6.04,114.16) iter 8: time=552.49 for 18 active reps approxLL diffs: (3.24,83.14) iter 9: time=562.69 for 18 active reps approxLL diffs: (1.98,61.74) iter 10: time=580.12 for 18 active reps approxLL diffs: (1.33,48.26) iter 11: time=569.02 for 18 active reps approxLL diffs: (0.92,35.97) iter 12: time=557.74 for 18 active reps approxLL diffs: (0.65,39.43) iter 13: time=552.77 for 18 active reps approxLL diffs: (0.49,19.09) iter 14: time=563.24 for 18 active reps approxLL diffs: (0.37,13.36) iter 15: time=568.19 for 18 active reps approxLL diffs: (0.29,10.54) iter 16: time=574.74 for 18 active reps approxLL diffs: (0.22,10.78) iter 17: time=576.33 for 18 active reps approxLL diffs: (0.17,15.93) iter 18: time=563.81 for 18 active reps approxLL diffs: (0.13,10.14) iter 19: time=542.21 for 18 active reps approxLL diffs: (0.10,7.61) iter 20: time=516.24 for 18 active reps approxLL diffs: (0.09,4.36) iter 21: time=520.44 for 18 active reps approxLL diffs: (0.07,4.26) iter 22: time=528.06 for 18 active reps approxLL diffs: (0.06,5.43) iter 23: time=536.85 for 18 active reps approxLL diffs: (0.05,8.42) iter 24: time=540.19 for 18 active reps approxLL diffs: (0.04,10.05) iter 25: time=567.53 for 18 active reps approxLL diffs: (0.03,2.58) iter 26: time=585.20 for 18 active reps approxLL diffs: (0.02,3.67) iter 27: time=593.58 for 18 active reps approxLL diffs: (0.02,4.56) iter 28: time=597.47 for 18 active reps approxLL diffs: (0.02,3.46) iter 29: time=604.13 for 18 active reps approxLL diffs: (0.01,2.58) iter 30: time=607.78 for 18 active reps approxLL diffs: (0.01,2.50) iter 31: time=552.18 for 18 active reps approxLL diffs: (0.01,1.41) iter 32: time=1026.30 for 16 active reps approxLL diffs: (0.01,1.30) iter 33: time=591.47 for 15 active reps approxLL diffs: (0.01,1.05) iter 34: time=551.21 for 13 active reps approxLL diffs: (0.01,1.00) iter 35: time=555.42 for 13 active reps approxLL diffs: (0.01,1.49) iter 36: time=555.36 for 12 active reps approxLL diffs: (0.01,2.00) iter 37: time=506.75 for 11 active reps approxLL diffs: (0.03,1.69) iter 38: time=509.30 for 11 active reps approxLL diffs: (0.03,1.44) iter 39: time=546.37 for 11 active reps approxLL diffs: (0.02,0.92) iter 40: time=803.33 for 11 active reps approxLL diffs: (0.02,0.45) iter 41: time=524.62 for 11 active reps approxLL diffs: (0.02,0.38) iter 42: time=516.35 for 11 active reps approxLL diffs: (0.01,0.47) iter 43: time=511.40 for 11 active reps approxLL diffs: (0.01,0.26) iter 44: time=483.55 for 10 active reps approxLL diffs: (0.01,0.41) iter 45: time=478.46 for 10 active reps approxLL diffs: (0.01,1.36) iter 46: time=479.84 for 9 active reps approxLL diffs: (0.01,0.33) iter 47: time=472.74 for 9 active reps approxLL diffs: (0.01,1.22) iter 48: time=497.31 for 9 active reps approxLL diffs: (0.02,2.09) iter 49: time=474.02 for 9 active reps approxLL diffs: (0.02,0.53) iter 50: time=522.70 for 9 active reps approxLL diffs: (0.02,0.89) iter 51: time=608.56 for 9 active reps approxLL diffs: (0.02,3.84) iter 52: time=705.39 for 9 active reps approxLL diffs: (0.01,2.42) iter 53: time=587.08 for 9 active reps approxLL diffs: (0.01,1.85) iter 54: time=447.13 for 8 active reps approxLL diffs: (0.02,2.50) iter 55: time=564.32 for 8 active reps approxLL diffs: (0.01,3.97) iter 56: time=385.52 for 7 active reps approxLL diffs: (0.01,0.45) iter 57: time=356.08 for 6 active reps approxLL diffs: (0.05,0.51) iter 58: time=352.83 for 6 active reps approxLL diffs: (0.03,0.26) iter 59: time=357.83 for 6 active reps approxLL diffs: (0.01,0.16) iter 60: time=344.76 for 5 active reps approxLL diffs: (0.01,0.12) iter 61: time=348.69 for 5 active reps approxLL diffs: (0.01,0.08) iter 62: time=321.81 for 4 active reps approxLL diffs: (0.01,0.15) iter 63: time=591.59 for 2 active reps approxLL diffs: (0.02,0.45) iter 64: time=597.44 for 2 active reps approxLL diffs: (0.03,0.90) iter 65: time=598.20 for 2 active reps approxLL diffs: (0.04,0.46) iter 66: time=600.81 for 2 active reps approxLL diffs: (0.05,0.12) iter 67: time=601.96 for 2 active reps approxLL diffs: (0.04,0.04) iter 68: time=602.84 for 2 active reps approxLL diffs: (0.02,0.02) iter 69: time=605.76 for 2 active reps approxLL diffs: (0.01,0.02) iter 70: time=373.44 for 1 active reps approxLL diffs: (0.02,0.02) iter 71: time=343.17 for 1 active reps approxLL diffs: (0.01,0.01) iter 72: time=334.39 for 1 active reps approxLL diffs: (0.01,0.01) iter 73: time=331.32 for 1 active reps approxLL diffs: (0.01,0.01) iter 74: time=306.63 for 1 active reps approxLL diffs: (0.01,0.01) iter 75: time=307.01 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 = 73.0%, memory/overhead = 27.0% Computing predictions on left-out cross-validation fold Time for computing predictions = 7955.43 sec Average PVEs obtained by param pairs tested (high to low): f2=0.5, p=0.02: 0.147959 f2=0.3, p=0.05: 0.147430 f2=0.5, p=0.01: 0.146370 ... f2=0.5, p=0.5: 0.114753 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.95454 Absolute prediction MSE using standard LMM: 0.845003 Absolute prediction MSE, fold-best f2=0.5, p=0.02: 0.813307 Absolute pred MSE using f2=0.5, p=0.5: 0.845003 Absolute pred MSE using f2=0.5, p=0.2: 0.835557 Absolute pred MSE using f2=0.5, p=0.1: 0.825428 Absolute pred MSE using f2=0.5, p=0.05: 0.816745 Absolute pred MSE using f2=0.5, p=0.02: 0.813307 Absolute pred MSE using f2=0.5, p=0.01: 0.814823 Absolute pred MSE using f2=0.3, p=0.5: 0.841931 Absolute pred MSE using f2=0.3, p=0.2: 0.828169 Absolute pred MSE using f2=0.3, p=0.1: 0.818315 Absolute pred MSE using f2=0.3, p=0.05: 0.813812 Absolute pred MSE using f2=0.3, p=0.02: 0.816658 Absolute pred MSE using f2=0.3, p=0.01: 0.819888 Absolute pred MSE using f2=0.1, p=0.5: 0.836780 Absolute pred MSE using f2=0.1, p=0.2: 0.822199 Absolute pred MSE using f2=0.1, p=0.1: 0.815556 Absolute pred MSE using f2=0.1, p=0.05: 0.817800 Absolute pred MSE using f2=0.1, p=0.02: 0.827504 Absolute pred MSE using f2=0.1, p=0.01: 0.831889 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.115 Relative improvement in prediction MSE using non-inf model: 0.038 Optimal mixture parameters according to CV: f2 = 0.5, p = 0.02 Time for estimating mixture parameters = 55274.8 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=1300.67 for 23 active reps iter 2: time=682.20 for 23 active reps approxLL diffs: (13128.08,14015.24) iter 3: time=697.11 for 23 active reps approxLL diffs: (2061.27,2232.83) iter 4: time=665.98 for 23 active reps approxLL diffs: (640.02,736.95) iter 5: time=649.26 for 23 active reps approxLL diffs: (275.12,318.44) iter 6: time=648.78 for 23 active reps approxLL diffs: (149.73,181.83) iter 7: time=671.57 for 23 active reps approxLL diffs: (98.50,124.28) iter 8: time=683.76 for 23 active reps approxLL diffs: (69.51,92.68) iter 9: time=673.99 for 23 active reps approxLL diffs: (49.59,60.71) iter 10: time=681.47 for 23 active reps approxLL diffs: (29.05,39.35) iter 11: time=684.71 for 23 active reps approxLL diffs: (23.07,33.81) iter 12: time=673.16 for 23 active reps approxLL diffs: (13.94,27.35) iter 13: time=675.84 for 23 active reps approxLL diffs: (9.01,20.58) iter 14: time=676.93 for 23 active reps approxLL diffs: (8.00,15.26) iter 15: time=683.50 for 23 active reps approxLL diffs: (6.63,12.95) iter 16: time=668.08 for 23 active reps approxLL diffs: (5.40,10.77) iter 17: time=668.35 for 23 active reps approxLL diffs: (4.19,8.65) iter 18: time=658.86 for 23 active reps approxLL diffs: (2.87,9.57) iter 19: time=678.83 for 23 active reps approxLL diffs: (1.83,7.84) iter 20: time=686.48 for 23 active reps approxLL diffs: (1.53,7.54) iter 21: time=688.12 for 23 active reps approxLL diffs: (0.80,7.31) iter 22: time=709.81 for 23 active reps approxLL diffs: (0.54,5.91) iter 23: time=696.81 for 23 active reps approxLL diffs: (0.56,5.50) iter 24: time=700.34 for 23 active reps approxLL diffs: (0.47,5.70) iter 25: time=704.27 for 23 active reps approxLL diffs: (0.35,4.71) iter 26: time=681.69 for 23 active reps approxLL diffs: (0.22,4.36) iter 27: time=671.06 for 23 active reps approxLL diffs: (0.16,2.29) iter 28: time=669.74 for 23 active reps approxLL diffs: (0.11,2.78) iter 29: time=665.68 for 23 active reps approxLL diffs: (0.08,1.09) iter 30: time=662.96 for 23 active reps approxLL diffs: (0.08,1.12) iter 31: time=668.10 for 23 active reps approxLL diffs: (0.08,1.82) iter 32: time=665.50 for 23 active reps approxLL diffs: (0.04,2.12) iter 33: time=671.12 for 23 active reps approxLL diffs: (0.02,1.27) iter 34: time=660.52 for 23 active reps approxLL diffs: (0.02,1.01) iter 35: time=676.33 for 23 active reps approxLL diffs: (0.02,0.91) iter 36: time=665.85 for 23 active reps approxLL diffs: (0.03,0.71) iter 37: time=661.90 for 23 active reps approxLL diffs: (0.03,0.66) iter 38: time=670.29 for 23 active reps approxLL diffs: (0.02,0.62) iter 39: time=660.19 for 23 active reps approxLL diffs: (0.01,0.51) iter 40: time=660.47 for 23 active reps approxLL diffs: (0.01,0.75) iter 41: time=642.25 for 22 active reps approxLL diffs: (0.01,0.66) iter 42: time=639.38 for 22 active reps approxLL diffs: (0.01,0.54) iter 43: time=643.71 for 21 active reps approxLL diffs: (0.02,0.79) iter 44: time=634.72 for 21 active reps approxLL diffs: (0.01,0.60) iter 45: time=625.94 for 21 active reps approxLL diffs: (0.01,0.55) iter 46: time=625.77 for 21 active reps approxLL diffs: (0.01,0.35) iter 47: time=626.93 for 21 active reps approxLL diffs: (0.01,0.47) iter 48: time=532.16 for 18 active reps approxLL diffs: (0.00,0.44) iter 49: time=509.20 for 16 active reps approxLL diffs: (0.01,1.26) iter 50: time=623.20 for 15 active reps approxLL diffs: (0.01,0.88) iter 51: time=568.58 for 13 active reps approxLL diffs: (0.01,0.28) iter 52: time=539.29 for 12 active reps approxLL diffs: (0.00,0.16) iter 53: time=511.56 for 10 active reps approxLL diffs: (0.01,0.26) iter 54: time=505.16 for 10 active reps approxLL diffs: (0.00,0.23) iter 55: time=494.85 for 9 active reps approxLL diffs: (0.01,0.14) iter 56: time=501.27 for 9 active reps approxLL diffs: (0.00,0.10) iter 57: time=454.85 for 8 active reps approxLL diffs: (0.01,0.21) iter 58: time=372.21 for 6 active reps approxLL diffs: (0.01,0.31) iter 59: time=335.12 for 4 active reps approxLL diffs: (0.01,0.23) iter 60: time=815.21 for 3 active reps approxLL diffs: (0.01,0.08) iter 61: time=515.33 for 2 active reps approxLL diffs: (0.02,0.02) iter 62: time=517.59 for 2 active reps approxLL diffs: (0.01,0.01) iter 63: time=254.54 for 1 active reps approxLL diffs: (0.01,0.01) iter 64: time=264.13 for 1 active reps approxLL diffs: (0.02,0.02) iter 65: time=248.02 for 1 active reps approxLL diffs: (0.03,0.03) iter 66: time=247.24 for 1 active reps approxLL diffs: (0.05,0.05) iter 67: time=245.51 for 1 active reps approxLL diffs: (0.06,0.06) iter 68: time=244.03 for 1 active reps approxLL diffs: (0.06,0.06) iter 69: time=246.00 for 1 active reps approxLL diffs: (0.05,0.05) iter 70: time=243.66 for 1 active reps approxLL diffs: (0.03,0.03) iter 71: time=242.64 for 1 active reps approxLL diffs: (0.02,0.02) iter 72: time=248.90 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 72: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 79.5%, memory/overhead = 20.5% 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 > 371.9) # of SNPs remaining after outlier window removal: 578079/583483 Intercept of LD Score regression for ref stats: 1.164 (0.013) Estimated attenuation: 0.160 (0.014) Intercept of LD Score regression for cur stats: 1.156 (0.013) Calibration factor (ref/cur) to multiply by: 1.007 (0.001) Time for computing Bayesian mixed model assoc stats = 42861.1 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=768.51 for 1 active reps iter 2: time=255.49 for 1 active reps approxLL diffs: (14072.16,14072.16) iter 3: time=258.29 for 1 active reps approxLL diffs: (2230.50,2230.50) iter 4: time=259.72 for 1 active reps approxLL diffs: (728.45,728.45) iter 5: time=253.92 for 1 active reps approxLL diffs: (313.94,313.94) iter 6: time=258.75 for 1 active reps approxLL diffs: (171.24,171.24) iter 7: time=259.03 for 1 active reps approxLL diffs: (116.81,116.81) iter 8: time=254.24 for 1 active reps approxLL diffs: (83.99,83.99) iter 9: time=258.79 for 1 active reps approxLL diffs: (56.81,56.81) iter 10: time=251.16 for 1 active reps approxLL diffs: (39.25,39.25) iter 11: time=250.03 for 1 active reps approxLL diffs: (27.70,27.70) iter 12: time=246.60 for 1 active reps approxLL diffs: (19.45,19.45) iter 13: time=248.21 for 1 active reps approxLL diffs: (17.44,17.44) iter 14: time=246.05 for 1 active reps approxLL diffs: (14.08,14.08) iter 15: time=244.68 for 1 active reps approxLL diffs: (10.60,10.60) iter 16: time=250.18 for 1 active reps approxLL diffs: (7.86,7.86) iter 17: time=254.78 for 1 active reps approxLL diffs: (6.86,6.86) iter 18: time=239.99 for 1 active reps approxLL diffs: (7.30,7.30) iter 19: time=250.92 for 1 active reps approxLL diffs: (7.04,7.04) iter 20: time=247.11 for 1 active reps approxLL diffs: (3.76,3.76) iter 21: time=251.45 for 1 active reps approxLL diffs: (2.25,2.25) iter 22: time=250.21 for 1 active reps approxLL diffs: (1.52,1.52) iter 23: time=246.22 for 1 active reps approxLL diffs: (0.87,0.87) iter 24: time=248.36 for 1 active reps approxLL diffs: (0.51,0.51) iter 25: time=239.78 for 1 active reps approxLL diffs: (0.40,0.40) iter 26: time=243.99 for 1 active reps approxLL diffs: (0.28,0.28) iter 27: time=241.58 for 1 active reps approxLL diffs: (0.19,0.19) iter 28: time=239.38 for 1 active reps approxLL diffs: (0.17,0.17) iter 29: time=232.47 for 1 active reps approxLL diffs: (0.19,0.19) iter 30: time=224.42 for 1 active reps approxLL diffs: (0.24,0.24) iter 31: time=224.62 for 1 active reps approxLL diffs: (0.24,0.24) iter 32: time=235.73 for 1 active reps approxLL diffs: (0.19,0.19) iter 33: time=224.25 for 1 active reps approxLL diffs: (0.15,0.15) iter 34: time=227.67 for 1 active reps approxLL diffs: (0.16,0.16) iter 35: time=230.95 for 1 active reps approxLL diffs: (0.23,0.23) iter 36: time=225.99 for 1 active reps approxLL diffs: (0.30,0.30) iter 37: time=227.89 for 1 active reps approxLL diffs: (0.60,0.60) iter 38: time=226.59 for 1 active reps approxLL diffs: (1.14,1.14) iter 39: time=228.28 for 1 active reps approxLL diffs: (0.51,0.51) iter 40: time=238.79 for 1 active reps approxLL diffs: (0.10,0.10) iter 41: time=227.72 for 1 active reps approxLL diffs: (0.06,0.06) iter 42: time=229.31 for 1 active reps approxLL diffs: (0.05,0.05) iter 43: time=220.05 for 1 active reps approxLL diffs: (0.04,0.04) iter 44: time=222.08 for 1 active reps approxLL diffs: (0.03,0.03) iter 45: time=227.91 for 1 active reps approxLL diffs: (0.05,0.05) iter 46: time=231.97 for 1 active reps approxLL diffs: (0.10,0.10) iter 47: time=232.51 for 1 active reps approxLL diffs: (0.20,0.20) iter 48: time=252.29 for 1 active reps approxLL diffs: (0.30,0.30) iter 49: time=233.81 for 1 active reps approxLL diffs: (0.21,0.21) iter 50: time=233.94 for 1 active reps approxLL diffs: (0.08,0.08) iter 51: time=228.70 for 1 active reps approxLL diffs: (0.03,0.03) iter 52: time=227.83 for 1 active reps approxLL diffs: (0.01,0.01) iter 53: time=232.97 for 1 active reps approxLL diffs: (0.02,0.02) iter 54: time=232.76 for 1 active reps approxLL diffs: (0.02,0.02) iter 55: time=245.57 for 1 active reps approxLL diffs: (0.04,0.04) iter 56: time=233.04 for 1 active reps approxLL diffs: (0.07,0.07) iter 57: time=235.30 for 1 active reps approxLL diffs: (0.11,0.11) iter 58: time=235.78 for 1 active reps approxLL diffs: (0.16,0.16) iter 59: time=230.96 for 1 active reps approxLL diffs: (0.14,0.14) iter 60: time=229.64 for 1 active reps approxLL diffs: (0.06,0.06) iter 61: time=234.03 for 1 active reps approxLL diffs: (0.02,0.02) iter 62: time=234.70 for 1 active reps approxLL diffs: (0.00,0.00) Converged at iter 62: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 56.8%, memory/overhead = 43.2% Time for computing and writing betas = 15396.4 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.00735 (709958 good SNPs) lambdaGC: 1.48561 Mean BOLT_LMM_INF: 2.11093 (709958 good SNPs) lambdaGC: 1.50981 Mean BOLT_LMM: 2.14522 (709958 good SNPs) lambdaGC: 1.52097 Note that LINREG may be confounded by a factor of 1.00081 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 9158.73 sec Total elapsed time for analysis = 170794 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=lung_FEV1FVCzSMOKE --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.lung_FEV1FVCzSMOKE.predbetas.txt.gz --statsFile=bolt_460K_selfRepWhite.lung_FEV1FVCzSMOKE.stats.gz --verboseStats" User time (seconds): 1218774.08 System time (seconds): 49159.76 Percent of CPU this job got: 742% Elapsed (wall clock) time (h:mm:ss or m:ss): 47:27:58 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): 86913920 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 888060 Minor (reclaiming a frame) page faults: 6548466849 Voluntary context switches: 4094618 Involuntary context switches: 65446908 Swaps: 0 File system inputs: 441728056 File system outputs: 96760 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0