+-----------------------------+ | ___ | | 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=body_BMIz \ --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.body_BMIz.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.body_BMIz.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) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt WARNING: 342 SNP(s) not found in data set 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 = 3313.1 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: 457824 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 = 457824 Singular values of covariate matrix: S[0] = 2.30402e+06 S[1] = 4835.83 S[2] = 476.592 S[3] = 298.077 S[4] = 203.082 S[5] = 197.204 S[6] = 185.62 S[7] = 177.812 S[8] = 173.148 S[9] = 167.804 S[10] = 164.684 S[11] = 155.873 S[12] = 147.807 S[13] = 145.573 S[14] = 143.452 S[15] = 138.574 S[16] = 134.758 S[17] = 131.597 S[18] = 128.552 S[19] = 117.323 S[20] = 113.185 S[21] = 101.211 S[22] = 45.8211 S[23] = 24.9707 S[24] = 21.1188 S[25] = 19.5364 S[26] = 0.997756 S[27] = 0.997724 S[28] = 0.997703 S[29] = 0.997621 S[30] = 0.99757 S[31] = 0.997524 S[32] = 0.997493 S[33] = 0.997481 S[34] = 0.997396 S[35] = 0.997343 S[36] = 0.997319 S[37] = 0.997296 S[38] = 0.997213 S[39] = 0.997181 S[40] = 0.997123 S[41] = 0.997023 S[42] = 0.996943 S[43] = 0.996722 S[44] = 0.980689 S[45] = 0.900884 S[46] = 1.75876e-11 S[47] = 8.5304e-12 S[48] = 5.38583e-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: 455147.993479 Dimension of all-1s proj space (Nused-1): 457823 Time for covariate data setup + Bolt initialization = 4342.95 sec Phenotype 1: N = 457824 mean = -0.00739844 std = 0.995925 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 431.136 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 457824) 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=377.04 rNorms/orig: (0.6,0.6) res2s: 742102..151135 iter 2: time=358.65 rNorms/orig: (0.6,0.6) res2s: 879791..199962 iter 3: time=349.80 rNorms/orig: (0.3,0.3) res2s: 1.01501e+06..230800 iter 4: time=346.83 rNorms/orig: (0.2,0.2) res2s: 1.06931e+06..244711 iter 5: time=359.72 rNorms/orig: (0.1,0.2) res2s: 1.09574e+06..252144 iter 6: time=349.15 rNorms/orig: (0.09,0.1) res2s: 1.11045e+06..255848 iter 7: time=342.37 rNorms/orig: (0.06,0.07) res2s: 1.11782e+06..257580 iter 8: time=345.85 rNorms/orig: (0.04,0.04) res2s: 1.12084e+06..258501 iter 9: time=341.52 rNorms/orig: (0.03,0.03) res2s: 1.12231e+06..258927 iter 10: time=341.70 rNorms/orig: (0.02,0.02) res2s: 1.12307e+06..259119 iter 11: time=341.70 rNorms/orig: (0.01,0.01) res2s: 1.12338e+06..259215 iter 12: time=346.28 rNorms/orig: (0.007,0.008) res2s: 1.12354e+06..259257 iter 13: time=345.09 rNorms/orig: (0.005,0.005) res2s: 1.1236e+06..259275 iter 14: time=340.73 rNorms/orig: (0.003,0.003) res2s: 1.12363e+06..259285 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 52.4%, memory/overhead = 47.6% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0965211 Estimating MC scaling f_REML at log(delta) = -0.00576172, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=335.17 rNorms/orig: (1,1) res2s: 78350.9..44657.4 iter 2: time=338.21 rNorms/orig: (1,1) res2s: 110825..75041 iter 3: time=340.25 rNorms/orig: (0.8,0.9) res2s: 167488..106171 iter 4: time=338.91 rNorms/orig: (0.6,0.7) res2s: 207682..127412 iter 5: time=340.82 rNorms/orig: (0.5,0.6) res2s: 235241..143721 iter 6: time=340.99 rNorms/orig: (0.4,0.4) res2s: 258180..155365 iter 7: time=338.28 rNorms/orig: (0.3,0.3) res2s: 275426..163037 iter 8: time=339.66 rNorms/orig: (0.3,0.3) res2s: 285479..168790 iter 9: time=337.76 rNorms/orig: (0.2,0.2) res2s: 292758..172597 iter 10: time=341.57 rNorms/orig: (0.1,0.2) res2s: 298100..175029 iter 11: time=343.59 rNorms/orig: (0.1,0.1) res2s: 301182..176749 iter 12: time=339.72 rNorms/orig: (0.09,0.1) res2s: 303465..177857 iter 13: time=337.45 rNorms/orig: (0.07,0.08) res2s: 304777..178511 iter 14: time=341.05 rNorms/orig: (0.06,0.06) res2s: 305757..179002 iter 15: time=344.18 rNorms/orig: (0.04,0.05) res2s: 306301..179272 iter 16: time=350.29 rNorms/orig: (0.03,0.04) res2s: 306685..179487 iter 17: time=337.91 rNorms/orig: (0.03,0.03) res2s: 306930..179600 iter 18: time=345.19 rNorms/orig: (0.02,0.02) res2s: 307090..179681 iter 19: time=343.95 rNorms/orig: (0.01,0.02) res2s: 307192..179732 iter 20: time=339.84 rNorms/orig: (0.01,0.01) res2s: 307247..179763 iter 21: time=337.09 rNorms/orig: (0.01,0.01) res2s: 307288..179781 iter 22: time=341.70 rNorms/orig: (0.007,0.008) res2s: 307313..179793 iter 23: time=344.69 rNorms/orig: (0.006,0.006) res2s: 307328..179801 iter 24: time=346.77 rNorms/orig: (0.004,0.005) res2s: 307338..179805 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 52.6%, memory/overhead = 47.4% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.238324 Estimating MC scaling f_REML at log(delta) = 0.776169, h2 = 0.313904... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=354.62 rNorms/orig: (0.8,0.8) res2s: 418856..113016 iter 2: time=354.99 rNorms/orig: (0.7,0.8) res2s: 523767..159988 iter 3: time=345.66 rNorms/orig: (0.5,0.5) res2s: 647734..194507 iter 4: time=345.13 rNorms/orig: (0.3,0.3) res2s: 706469..212214 iter 5: time=350.22 rNorms/orig: (0.2,0.2) res2s: 738005..222840 iter 6: time=356.47 rNorms/orig: (0.2,0.2) res2s: 757966..228800 iter 7: time=375.70 rNorms/orig: (0.1,0.1) res2s: 769348..231927 iter 8: time=373.79 rNorms/orig: (0.08,0.08) res2s: 774580..233797 iter 9: time=372.38 rNorms/orig: (0.05,0.06) res2s: 777503..234774 iter 10: time=373.11 rNorms/orig: (0.04,0.04) res2s: 779189..235270 iter 11: time=369.17 rNorms/orig: (0.02,0.03) res2s: 779971..235549 iter 12: time=371.07 rNorms/orig: (0.02,0.02) res2s: 780419..235691 iter 13: time=364.92 rNorms/orig: (0.01,0.01) res2s: 780625..235758 iter 14: time=368.12 rNorms/orig: (0.008,0.009) res2s: 780746..235797 iter 15: time=371.76 rNorms/orig: (0.006,0.006) res2s: 780800..235815 iter 16: time=370.55 rNorms/orig: (0.004,0.004) res2s: 780829..235826 Converged at iter 16: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.5%, memory/overhead = 48.5% MCscaling: logDelta = 0.78, h2 = 0.314, f = 0.00186333 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.314 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.303218, logDelta = 0.776169, f = 0.00186333 Time for fitting variance components = 20031.4 sec === Computing mixed model assoc stats (inf. model) === Selected 30 SNPs for computation of prospective stat Tried 31; threw out 1 with GRAMMAR chisq > 5 Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Batch-solving 53 systems of equations using conjugate gradient iteration iter 1: time=1206.50 rNorms/orig: (0.5,1) res2s: 112946..208907 iter 2: time=1036.94 rNorms/orig: (0.5,0.8) res2s: 161072..241063 iter 3: time=1045.15 rNorms/orig: (0.3,0.5) res2s: 196642..268573 iter 4: time=1046.09 rNorms/orig: (0.2,0.4) res2s: 215839..280877 iter 5: time=1039.92 rNorms/orig: (0.2,0.2) res2s: 227519..288501 iter 6: time=1039.22 rNorms/orig: (0.1,0.2) res2s: 233926..292735 iter 7: time=1038.41 rNorms/orig: (0.07,0.1) res2s: 237628..294904 iter 8: time=1008.62 rNorms/orig: (0.05,0.09) res2s: 239787..296313 iter 9: time=1008.80 rNorms/orig: (0.03,0.06) res2s: 240935..296989 iter 10: time=1011.95 rNorms/orig: (0.02,0.04) res2s: 241562..297290 iter 11: time=1007.84 rNorms/orig: (0.02,0.03) res2s: 241916..297484 iter 12: time=1008.68 rNorms/orig: (0.01,0.02) res2s: 242097..297578 iter 13: time=1010.30 rNorms/orig: (0.008,0.02) res2s: 242190..297624 iter 14: time=1013.49 rNorms/orig: (0.005,0.01) res2s: 242243..297649 iter 15: time=1011.00 rNorms/orig: (0.003,0.007) res2s: 242269..297663 iter 16: time=1014.16 rNorms/orig: (0.002,0.005) res2s: 242285..297669 iter 17: time=1012.51 rNorms/orig: (0.001,0.004) res2s: 242292..297673 iter 18: time=1014.85 rNorms/orig: (0.0009,0.003) res2s: 242296..297674 iter 19: time=1010.86 rNorms/orig: (0.0006,0.002) res2s: 242298..297675 iter 20: time=1015.85 rNorms/orig: (0.0004,0.001) res2s: 242299..297676 iter 21: time=1010.38 rNorms/orig: (0.0002,0.0009) res2s: 242299..297676 iter 22: time=1009.03 rNorms/orig: (0.0001,0.0006) res2s: 242299..297676 iter 23: time=1008.40 rNorms/orig: (0.0001,0.0004) res2s: 242300..297676 Converged at iter 23: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 80.5%, memory/overhead = 19.5% AvgPro: 3.620 AvgRetro: 3.566 Calibration: 1.015 (0.002) (30 SNPs) Ratio of medians: 1.026 Median of ratios: 1.013 Time for computing infinitesimal model assoc stats = 24044.2 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 20.135 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 > 457.8) # of SNPs remaining after outlier window removal: 582462/583483 Intercept of LD Score regression for ref stats: 1.184 (0.012) Estimated attenuation: 0.132 (0.010) Intercept of LD Score regression for cur stats: 1.190 (0.012) Calibration factor (ref/cur) to multiply by: 0.994 (0.003) LINREG intercept inflation = 1.00564 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 = 366259 Singular values of covariate matrix: S[0] = 2.06123e+06 S[1] = 4321.43 S[2] = 426.308 S[3] = 266.624 S[4] = 181.812 S[5] = 176.494 S[6] = 165.983 S[7] = 159.002 S[8] = 154.789 S[9] = 150.092 S[10] = 147.163 S[11] = 139.412 S[12] = 132.31 S[13] = 130.2 S[14] = 128.367 S[15] = 123.99 S[16] = 120.502 S[17] = 117.558 S[18] = 114.921 S[19] = 104.86 S[20] = 101.288 S[21] = 90.4292 S[22] = 40.9142 S[23] = 22.4796 S[24] = 19.0272 S[25] = 17.4616 S[26] = 0.894968 S[27] = 0.894517 S[28] = 0.894378 S[29] = 0.893859 S[30] = 0.89364 S[31] = 0.89324 S[32] = 0.893129 S[33] = 0.892702 S[34] = 0.892389 S[35] = 0.8923 S[36] = 0.891911 S[37] = 0.891587 S[38] = 0.891473 S[39] = 0.890898 S[40] = 0.890735 S[41] = 0.890447 S[42] = 0.890118 S[43] = 0.889765 S[44] = 0.878094 S[45] = 0.805434 S[46] = 1.35598e-11 S[47] = 4.6161e-12 S[48] = 5.0591e-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: 364108.287492 Dimension of all-1s proj space (Nused-1): 366258 Beginning variational Bayes iter 1: time=957.09 for 18 active reps iter 2: time=512.69 for 18 active reps approxLL diffs: (9355.69,13721.88) iter 3: time=499.54 for 18 active reps approxLL diffs: (1227.09,1768.30) iter 4: time=500.95 for 18 active reps approxLL diffs: (217.96,608.67) iter 5: time=503.74 for 18 active reps approxLL diffs: (56.90,300.57) iter 6: time=514.65 for 18 active reps approxLL diffs: (19.83,167.77) iter 7: time=510.90 for 18 active reps approxLL diffs: (8.66,114.05) iter 8: time=481.22 for 18 active reps approxLL diffs: (4.43,79.39) iter 9: time=458.87 for 18 active reps approxLL diffs: (2.54,55.75) iter 10: time=479.48 for 18 active reps approxLL diffs: (1.62,39.19) iter 11: time=471.36 for 18 active reps approxLL diffs: (1.12,27.43) iter 12: time=459.21 for 18 active reps approxLL diffs: (0.82,22.80) iter 13: time=469.16 for 18 active reps approxLL diffs: (0.57,19.19) iter 14: time=466.27 for 18 active reps approxLL diffs: (0.41,19.74) iter 15: time=466.32 for 18 active reps approxLL diffs: (0.30,12.58) iter 16: time=466.10 for 18 active reps approxLL diffs: (0.23,13.36) iter 17: time=461.85 for 18 active reps approxLL diffs: (0.18,8.10) iter 18: time=462.92 for 18 active reps approxLL diffs: (0.14,4.78) iter 19: time=462.33 for 18 active reps approxLL diffs: (0.11,3.69) iter 20: time=462.10 for 18 active reps approxLL diffs: (0.09,2.61) iter 21: time=459.77 for 18 active reps approxLL diffs: (0.07,3.62) iter 22: time=463.44 for 18 active reps approxLL diffs: (0.06,4.00) iter 23: time=460.83 for 18 active reps approxLL diffs: (0.04,3.25) iter 24: time=467.46 for 18 active reps approxLL diffs: (0.04,2.32) iter 25: time=473.87 for 18 active reps approxLL diffs: (0.03,3.23) iter 26: time=470.46 for 18 active reps approxLL diffs: (0.03,2.55) iter 27: time=489.61 for 18 active reps approxLL diffs: (0.02,2.55) iter 28: time=491.61 for 18 active reps approxLL diffs: (0.02,3.54) iter 29: time=495.67 for 18 active reps approxLL diffs: (0.01,2.22) iter 30: time=494.22 for 18 active reps approxLL diffs: (0.01,2.36) iter 31: time=492.89 for 18 active reps approxLL diffs: (0.01,4.69) iter 32: time=478.21 for 17 active reps approxLL diffs: (0.01,3.83) iter 33: time=502.74 for 14 active reps approxLL diffs: (0.01,2.98) iter 34: time=494.62 for 13 active reps approxLL diffs: (0.01,2.67) iter 35: time=469.64 for 12 active reps approxLL diffs: (0.01,2.17) iter 36: time=466.13 for 11 active reps approxLL diffs: (0.03,2.27) iter 37: time=440.97 for 11 active reps approxLL diffs: (0.03,1.97) iter 38: time=447.05 for 11 active reps approxLL diffs: (0.02,1.61) iter 39: time=440.33 for 11 active reps approxLL diffs: (0.02,0.88) iter 40: time=445.23 for 11 active reps approxLL diffs: (0.02,0.61) iter 41: time=452.64 for 11 active reps approxLL diffs: (0.01,0.35) iter 42: time=456.74 for 11 active reps approxLL diffs: (0.01,0.37) iter 43: time=445.13 for 11 active reps approxLL diffs: (0.01,0.63) iter 44: time=454.06 for 11 active reps approxLL diffs: (0.01,1.73) iter 45: time=448.91 for 11 active reps approxLL diffs: (0.01,3.11) iter 46: time=408.51 for 10 active reps approxLL diffs: (0.01,0.85) iter 47: time=405.14 for 9 active reps approxLL diffs: (0.01,0.38) iter 48: time=403.95 for 9 active reps approxLL diffs: (0.01,0.25) iter 49: time=403.57 for 9 active reps approxLL diffs: (0.01,0.41) iter 50: time=403.64 for 9 active reps approxLL diffs: (0.01,0.49) iter 51: time=403.33 for 9 active reps approxLL diffs: (0.02,0.30) iter 52: time=406.44 for 9 active reps approxLL diffs: (0.02,0.14) iter 53: time=401.97 for 9 active reps approxLL diffs: (0.01,0.26) iter 54: time=379.38 for 8 active reps approxLL diffs: (0.01,0.38) iter 55: time=382.28 for 8 active reps approxLL diffs: (0.01,0.27) iter 56: time=377.30 for 8 active reps approxLL diffs: (0.01,0.14) iter 57: time=321.20 for 7 active reps approxLL diffs: (0.01,0.14) iter 58: time=296.58 for 6 active reps approxLL diffs: (0.01,0.26) iter 59: time=293.72 for 6 active reps approxLL diffs: (0.01,0.57) iter 60: time=284.79 for 5 active reps approxLL diffs: (0.01,0.47) iter 61: time=270.60 for 4 active reps approxLL diffs: (0.05,0.32) iter 62: time=267.67 for 4 active reps approxLL diffs: (0.04,0.32) iter 63: time=267.43 for 4 active reps approxLL diffs: (0.05,0.35) iter 64: time=267.84 for 4 active reps approxLL diffs: (0.09,0.34) iter 65: time=269.87 for 4 active reps approxLL diffs: (0.03,0.49) iter 66: time=270.09 for 4 active reps approxLL diffs: (0.02,0.38) iter 67: time=267.04 for 4 active reps approxLL diffs: (0.02,0.28) iter 68: time=263.50 for 4 active reps approxLL diffs: (0.01,0.23) iter 69: time=262.05 for 4 active reps approxLL diffs: (0.01,0.24) iter 70: time=448.12 for 2 active reps approxLL diffs: (0.09,0.31) iter 71: time=441.74 for 2 active reps approxLL diffs: (0.24,0.34) iter 72: time=446.69 for 2 active reps approxLL diffs: (0.21,0.90) iter 73: time=441.82 for 2 active reps approxLL diffs: (0.10,0.51) iter 74: time=443.70 for 2 active reps approxLL diffs: (0.06,0.14) iter 75: time=439.09 for 2 active reps approxLL diffs: (0.05,0.10) iter 76: time=433.69 for 2 active reps approxLL diffs: (0.04,0.09) iter 77: time=429.05 for 2 active reps approxLL diffs: (0.04,0.09) iter 78: time=430.10 for 2 active reps approxLL diffs: (0.03,0.08) iter 79: time=432.63 for 2 active reps approxLL diffs: (0.03,0.05) iter 80: time=433.92 for 2 active reps approxLL diffs: (0.03,0.03) iter 81: time=435.20 for 2 active reps approxLL diffs: (0.02,0.03) iter 82: time=438.49 for 2 active reps approxLL diffs: (0.02,0.03) iter 83: time=439.93 for 2 active reps approxLL diffs: (0.02,0.02) iter 84: time=444.45 for 2 active reps approxLL diffs: (0.02,0.03) iter 85: time=445.00 for 2 active reps approxLL diffs: (0.01,0.05) iter 86: time=440.16 for 2 active reps approxLL diffs: (0.01,0.07) iter 87: time=440.46 for 2 active reps approxLL diffs: (0.01,0.10) iter 88: time=439.43 for 2 active reps approxLL diffs: (0.01,0.10) iter 89: time=436.31 for 2 active reps approxLL diffs: (0.02,0.08) iter 90: time=443.43 for 2 active reps approxLL diffs: (0.02,0.07) iter 91: time=444.71 for 2 active reps approxLL diffs: (0.03,0.06) iter 92: time=442.22 for 2 active reps approxLL diffs: (0.04,0.04) iter 93: time=445.43 for 2 active reps approxLL diffs: (0.02,0.05) iter 94: time=444.69 for 2 active reps approxLL diffs: (0.01,0.07) iter 95: time=192.81 for 1 active reps approxLL diffs: (0.08,0.08) iter 96: time=186.79 for 1 active reps approxLL diffs: (0.09,0.09) iter 97: time=187.13 for 1 active reps approxLL diffs: (0.10,0.10) iter 98: time=187.13 for 1 active reps approxLL diffs: (0.10,0.10) iter 99: time=182.91 for 1 active reps approxLL diffs: (0.09,0.09) iter 100: time=180.70 for 1 active reps approxLL diffs: (0.07,0.07) iter 101: time=180.42 for 1 active reps approxLL diffs: (0.04,0.04) iter 102: time=177.94 for 1 active reps approxLL diffs: (0.03,0.03) iter 103: time=175.30 for 1 active reps approxLL diffs: (0.01,0.01) iter 104: time=176.63 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 104: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 78.1%, memory/overhead = 21.9% Computing predictions on left-out cross-validation fold Time for computing predictions = 5433.53 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.1: 0.141735 f2=0.5, p=0.05: 0.141561 f2=0.1, p=0.2: 0.141035 ... f2=0.1, p=0.01: 0.105261 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.982989 Absolute prediction MSE using standard LMM: 0.854881 Absolute prediction MSE, fold-best f2=0.3, p=0.1: 0.843664 Absolute pred MSE using f2=0.5, p=0.5: 0.854881 Absolute pred MSE using f2=0.5, p=0.2: 0.850509 Absolute pred MSE using f2=0.5, p=0.1: 0.846083 Absolute pred MSE using f2=0.5, p=0.05: 0.843836 Absolute pred MSE using f2=0.5, p=0.02: 0.847448 Absolute pred MSE using f2=0.5, p=0.01: 0.850809 Absolute pred MSE using f2=0.3, p=0.5: 0.853385 Absolute pred MSE using f2=0.3, p=0.2: 0.846721 Absolute pred MSE using f2=0.3, p=0.1: 0.843664 Absolute pred MSE using f2=0.3, p=0.05: 0.845963 Absolute pred MSE using f2=0.3, p=0.02: 0.855008 Absolute pred MSE using f2=0.3, p=0.01: 0.860067 Absolute pred MSE using f2=0.1, p=0.5: 0.850475 Absolute pred MSE using f2=0.1, p=0.2: 0.844353 Absolute pred MSE using f2=0.1, p=0.1: 0.845838 Absolute pred MSE using f2=0.1, p=0.05: 0.856652 Absolute pred MSE using f2=0.1, p=0.02: 0.872930 Absolute pred MSE using f2=0.1, p=0.01: 0.879518 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.130 Relative improvement in prediction MSE using non-inf model: 0.013 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.1 Time for estimating mixture parameters = 52521 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=1032.38 for 23 active reps iter 2: time=576.65 for 23 active reps approxLL diffs: (16743.71,17747.84) iter 3: time=568.80 for 23 active reps approxLL diffs: (2325.62,2424.26) iter 4: time=567.02 for 23 active reps approxLL diffs: (549.49,593.92) iter 5: time=565.05 for 23 active reps approxLL diffs: (185.43,207.75) iter 6: time=552.99 for 23 active reps approxLL diffs: (75.85,87.98) iter 7: time=546.02 for 23 active reps approxLL diffs: (36.64,43.75) iter 8: time=545.73 for 23 active reps approxLL diffs: (20.69,25.82) iter 9: time=546.80 for 23 active reps approxLL diffs: (13.74,16.84) iter 10: time=553.21 for 23 active reps approxLL diffs: (9.98,12.01) iter 11: time=562.56 for 23 active reps approxLL diffs: (7.42,9.19) iter 12: time=555.92 for 23 active reps approxLL diffs: (5.39,6.95) iter 13: time=554.41 for 23 active reps approxLL diffs: (3.63,5.43) iter 14: time=566.57 for 23 active reps approxLL diffs: (2.63,4.59) iter 15: time=566.48 for 23 active reps approxLL diffs: (2.12,3.64) iter 16: time=568.13 for 23 active reps approxLL diffs: (1.72,2.46) iter 17: time=566.73 for 23 active reps approxLL diffs: (1.39,2.10) iter 18: time=564.41 for 23 active reps approxLL diffs: (1.01,1.95) iter 19: time=565.47 for 23 active reps approxLL diffs: (0.74,1.68) iter 20: time=562.37 for 23 active reps approxLL diffs: (0.65,1.59) iter 21: time=565.11 for 23 active reps approxLL diffs: (0.64,1.24) iter 22: time=565.21 for 23 active reps approxLL diffs: (0.53,1.15) iter 23: time=564.48 for 23 active reps approxLL diffs: (0.47,0.99) iter 24: time=560.86 for 23 active reps approxLL diffs: (0.42,0.87) iter 25: time=569.59 for 23 active reps approxLL diffs: (0.35,0.73) iter 26: time=567.59 for 23 active reps approxLL diffs: (0.27,0.60) iter 27: time=568.17 for 23 active reps approxLL diffs: (0.20,0.57) iter 28: time=569.39 for 23 active reps approxLL diffs: (0.16,0.67) iter 29: time=566.57 for 23 active reps approxLL diffs: (0.13,0.54) iter 30: time=568.13 for 23 active reps approxLL diffs: (0.12,0.56) iter 31: time=569.42 for 23 active reps approxLL diffs: (0.11,0.46) iter 32: time=565.43 for 23 active reps approxLL diffs: (0.06,0.34) iter 33: time=567.33 for 23 active reps approxLL diffs: (0.04,0.22) iter 34: time=568.59 for 23 active reps approxLL diffs: (0.04,0.24) iter 35: time=556.76 for 23 active reps approxLL diffs: (0.04,0.27) iter 36: time=558.71 for 23 active reps approxLL diffs: (0.03,0.23) iter 37: time=562.17 for 23 active reps approxLL diffs: (0.02,0.16) iter 38: time=557.42 for 23 active reps approxLL diffs: (0.02,0.19) iter 39: time=557.91 for 23 active reps approxLL diffs: (0.01,0.23) iter 40: time=525.30 for 22 active reps approxLL diffs: (0.01,0.23) iter 41: time=522.93 for 22 active reps approxLL diffs: (0.01,0.27) iter 42: time=515.90 for 22 active reps approxLL diffs: (0.01,0.18) iter 43: time=520.83 for 21 active reps approxLL diffs: (0.01,0.19) iter 44: time=511.38 for 21 active reps approxLL diffs: (0.01,0.23) iter 45: time=514.04 for 21 active reps approxLL diffs: (0.01,0.26) iter 46: time=485.08 for 20 active reps approxLL diffs: (0.01,0.23) iter 47: time=480.66 for 19 active reps approxLL diffs: (0.01,0.19) iter 48: time=441.89 for 18 active reps approxLL diffs: (0.01,0.20) iter 49: time=437.52 for 18 active reps approxLL diffs: (0.01,0.18) iter 50: time=425.20 for 17 active reps approxLL diffs: (0.01,0.16) iter 51: time=429.48 for 17 active reps approxLL diffs: (0.01,0.13) iter 52: time=415.07 for 16 active reps approxLL diffs: (0.01,0.07) iter 53: time=439.75 for 12 active reps approxLL diffs: (0.01,0.06) iter 54: time=410.14 for 10 active reps approxLL diffs: (0.01,0.07) iter 55: time=399.43 for 9 active reps approxLL diffs: (0.01,0.05) iter 56: time=402.26 for 9 active reps approxLL diffs: (0.01,0.05) iter 57: time=282.58 for 5 active reps approxLL diffs: (0.01,0.06) iter 58: time=264.58 for 4 active reps approxLL diffs: (0.01,0.06) iter 59: time=694.32 for 3 active reps approxLL diffs: (0.03,0.07) iter 60: time=695.30 for 3 active reps approxLL diffs: (0.03,0.07) iter 61: time=694.23 for 3 active reps approxLL diffs: (0.02,0.05) iter 62: time=690.04 for 3 active reps approxLL diffs: (0.02,0.06) iter 63: time=689.96 for 3 active reps approxLL diffs: (0.02,0.08) iter 64: time=692.09 for 3 active reps approxLL diffs: (0.01,0.08) iter 65: time=690.37 for 3 active reps approxLL diffs: (0.01,0.06) iter 66: time=428.85 for 2 active reps approxLL diffs: (0.02,0.03) iter 67: time=429.29 for 2 active reps approxLL diffs: (0.01,0.02) iter 68: time=428.95 for 2 active reps approxLL diffs: (0.01,0.01) Converged at iter 68: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 85.2%, memory/overhead = 14.8% 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 > 457.8) # of SNPs remaining after outlier window removal: 582462/583483 Intercept of LD Score regression for ref stats: 1.184 (0.012) Estimated attenuation: 0.132 (0.010) Intercept of LD Score regression for cur stats: 1.172 (0.012) Calibration factor (ref/cur) to multiply by: 1.010 (0.001) Time for computing Bayesian mixed model assoc stats = 37356.3 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=642.21 for 1 active reps iter 2: time=189.19 for 1 active reps approxLL diffs: (17830.04,17830.04) iter 3: time=199.66 for 1 active reps approxLL diffs: (2414.88,2414.88) iter 4: time=204.40 for 1 active reps approxLL diffs: (583.23,583.23) iter 5: time=204.82 for 1 active reps approxLL diffs: (199.46,199.46) iter 6: time=205.69 for 1 active reps approxLL diffs: (83.11,83.11) iter 7: time=202.60 for 1 active reps approxLL diffs: (41.61,41.61) iter 8: time=199.33 for 1 active reps approxLL diffs: (24.10,24.10) iter 9: time=202.44 for 1 active reps approxLL diffs: (16.10,16.10) iter 10: time=204.33 for 1 active reps approxLL diffs: (11.30,11.30) iter 11: time=206.44 for 1 active reps approxLL diffs: (8.12,8.12) iter 12: time=209.72 for 1 active reps approxLL diffs: (6.17,6.17) iter 13: time=208.88 for 1 active reps approxLL diffs: (4.51,4.51) iter 14: time=210.36 for 1 active reps approxLL diffs: (3.26,3.26) iter 15: time=209.09 for 1 active reps approxLL diffs: (2.53,2.53) iter 16: time=215.18 for 1 active reps approxLL diffs: (2.26,2.26) iter 17: time=215.78 for 1 active reps approxLL diffs: (2.03,2.03) iter 18: time=217.21 for 1 active reps approxLL diffs: (1.67,1.67) iter 19: time=221.63 for 1 active reps approxLL diffs: (1.22,1.22) iter 20: time=217.67 for 1 active reps approxLL diffs: (0.84,0.84) iter 21: time=219.76 for 1 active reps approxLL diffs: (0.64,0.64) iter 22: time=215.11 for 1 active reps approxLL diffs: (0.58,0.58) iter 23: time=215.55 for 1 active reps approxLL diffs: (0.60,0.60) iter 24: time=217.87 for 1 active reps approxLL diffs: (0.65,0.65) iter 25: time=216.57 for 1 active reps approxLL diffs: (0.67,0.67) iter 26: time=219.57 for 1 active reps approxLL diffs: (0.56,0.56) iter 27: time=213.41 for 1 active reps approxLL diffs: (0.39,0.39) iter 28: time=214.99 for 1 active reps approxLL diffs: (0.28,0.28) iter 29: time=218.32 for 1 active reps approxLL diffs: (0.21,0.21) iter 30: time=214.69 for 1 active reps approxLL diffs: (0.17,0.17) iter 31: time=219.10 for 1 active reps approxLL diffs: (0.15,0.15) iter 32: time=215.94 for 1 active reps approxLL diffs: (0.14,0.14) iter 33: time=214.39 for 1 active reps approxLL diffs: (0.14,0.14) iter 34: time=214.86 for 1 active reps approxLL diffs: (0.12,0.12) iter 35: time=214.48 for 1 active reps approxLL diffs: (0.09,0.09) iter 36: time=206.73 for 1 active reps approxLL diffs: (0.05,0.05) iter 37: time=204.25 for 1 active reps approxLL diffs: (0.03,0.03) iter 38: time=210.10 for 1 active reps approxLL diffs: (0.02,0.02) iter 39: time=205.14 for 1 active reps approxLL diffs: (0.01,0.01) iter 40: time=204.12 for 1 active reps approxLL diffs: (0.01,0.01) iter 41: time=205.13 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 41: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 59.4%, memory/overhead = 40.6% Time for computing and writing betas = 9070.53 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.18337 (709970 good SNPs) lambdaGC: 1.71557 Mean BOLT_LMM_INF: 2.28249 (709970 good SNPs) lambdaGC: 1.74238 Mean BOLT_LMM: 2.29891 (709970 good SNPs) lambdaGC: 1.74929 Note that LINREG may be confounded by a factor of 1.00564 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 7036.14 sec Total elapsed time for analysis = 158167 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=body_BMIz --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.body_BMIz.predbetas.txt.gz --statsFile=bolt_460K_selfRepWhite.body_BMIz.stats.gz --verboseStats" User time (seconds): 1148719.46 System time (seconds): 45570.81 Percent of CPU this job got: 754% Elapsed (wall clock) time (h:mm:ss or m:ss): 43:56:30 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): 86921048 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 68 Minor (reclaiming a frame) page faults: 6531288023 Voluntary context switches: 6103962 Involuntary context switches: 8477912 Swaps: 0 File system inputs: 385489320 File system outputs: 92920 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0