+-----------------------------+ | ___ | | BOLT-LMM, v2.3.1 /_ / | | December 19, 2017 /_/ | | Po-Ru Loh // | | / | +-----------------------------+ Copyright (C) 2014-2017 Harvard University. Distributed under the GNU GPLv3 open source license. Compiled with USE_SSE: fast aligned memory access Compiled with USE_MKL: Intel Math Kernel Library linear algebra Boost version: 1_58 Command line options: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt \ --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed \ --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim \ --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam \ --allowX \ --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt \ --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab \ --phenoCol=blood_RBC_DISTRIB_WIDTH \ --covarFile=/n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz \ --covarCol=cov_ASSESS_CENTER \ --covarCol=cov_GENO_ARRAY \ --covarCol=cov_SEX \ --covarMaxLevels=30 \ --qCovarCol=cov_AGE \ --qCovarCol=cov_AGE_SQ \ --qCovarCol=PC{1:20} \ --LDscoresFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/LDSCORE.1000G_EUR.tab.gz \ --geneticMapFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz \ --lmmForceNonInf \ --numThreads=8 \ --predBetasFile=bolt_460K_selfRepWhite.blood_RBC_DISTRIB_WIDTH.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_RBC_DISTRIB_WIDTH.stats.gz \ --verboseStats Setting number of threads to 8 fam: /n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam bim(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim bed(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed === Reading genotype data === Total indivs in PLINK data: Nbed = 488377 Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt Removed 976 individual(s) Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt WARNING: Unable to find individual to remove: FID IID WARNING: Unable to find individual to remove: 6023494 6023494 WARNING: Unable to find individual to remove: 6022857 6022857 WARNING: Unable to find individual to remove: 6020026 6020026 WARNING: Unable to find individual to remove: 6017119 6017119 Removed 28074 individual(s) WARNING: 1684 individual(s) not found in data set Total indivs stored in memory: N = 459327 Reading bim file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim Read 63487 snps Reading bim file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim Read 61966 snps Reading bim file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim Read 52300 snps Reading bim file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim Read 47443 snps Reading bim file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim Read 46314 snps Reading bim file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim Read 53695 snps Reading bim file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim Read 42722 snps Reading bim file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim Read 38591 snps Reading bim file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim Read 34310 snps Reading bim file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim Read 38308 snps Reading bim file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim Read 40824 snps Reading bim file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim Read 37302 snps Reading bim file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim Read 26806 snps Reading bim file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim Read 25509 snps Reading bim file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim Read 24467 snps Reading bim file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim Read 28960 snps Reading bim file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim Read 28835 snps Reading bim file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim Read 21962 snps Reading bim file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim Read 26186 snps Reading bim file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim Read 19959 snps Reading bim file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim Read 11342 snps Reading bim file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim Read 12968 snps Reading bim file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim Read 20214 snps Total snps in PLINK data: Mbed = 804470 Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt Excluded 73451 SNP(s) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89009265 WARNING: Unable to find SNP to exclude: Affx-89005343 WARNING: Unable to find SNP to exclude: Affx-89020964 WARNING: Unable to find SNP to exclude: Affx-89013736 Excluded 16710 SNP(s) WARNING: 342 SNP(s) not found in data set Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89022776 WARNING: Unable to find SNP to exclude: Affx-89017694 WARNING: Unable to find SNP to exclude: Affx-89018603 WARNING: Unable to find SNP to exclude: Affx-79443721 Excluded 8428 SNP(s) WARNING: 112 SNP(s) not found in data set Breakdown of SNP pre-filtering results: 705881 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 98589 excluded SNPs Filling in genetic map coordinates using reference file: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz Allocating 705881 x 459328/4 bytes to store genotypes Reading genotypes and performing QC filtering on snps and indivs... Reading bed file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed Expecting 7751445265 (+3) bytes for 488377 indivs, 63487 snps Reading bed file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed Expecting 7565738770 (+3) bytes for 488377 indivs, 61966 snps Reading bed file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed Expecting 6385568500 (+3) bytes for 488377 indivs, 52300 snps Reading bed file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed Expecting 5792553085 (+3) bytes for 488377 indivs, 47443 snps Reading bed file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed Expecting 5654707830 (+3) bytes for 488377 indivs, 46314 snps Reading bed file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed Expecting 6555891025 (+3) bytes for 488377 indivs, 53695 snps Reading bed file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed Expecting 5216142590 (+3) bytes for 488377 indivs, 42722 snps Reading bed file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed Expecting 4711768145 (+3) bytes for 488377 indivs, 38591 snps Reading bed file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed Expecting 4189079450 (+3) bytes for 488377 indivs, 34310 snps Reading bed file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed Expecting 4677215260 (+3) bytes for 488377 indivs, 38308 snps Reading bed file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed Expecting 4984406280 (+3) bytes for 488377 indivs, 40824 snps Reading bed file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed Expecting 4554387690 (+3) bytes for 488377 indivs, 37302 snps Reading bed file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed Expecting 3272878570 (+3) bytes for 488377 indivs, 26806 snps Reading bed file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed Expecting 3114521355 (+3) bytes for 488377 indivs, 25509 snps Reading bed file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed Expecting 2987298365 (+3) bytes for 488377 indivs, 24467 snps Reading bed file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed Expecting 3535871200 (+3) bytes for 488377 indivs, 28960 snps Reading bed file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed Expecting 3520609325 (+3) bytes for 488377 indivs, 28835 snps Reading bed file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed Expecting 2681450390 (+3) bytes for 488377 indivs, 21962 snps Reading bed file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed Expecting 3197179670 (+3) bytes for 488377 indivs, 26186 snps Reading bed file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed Expecting 2436894105 (+3) bytes for 488377 indivs, 19959 snps Reading bed file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed Expecting 1384801490 (+3) bytes for 488377 indivs, 11342 snps Reading bed file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed Expecting 1583327960 (+3) bytes for 488377 indivs, 12968 snps Reading bed file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed Expecting 2468028330 (+3) bytes for 488377 indivs, 20214 snps Total indivs after QC: 459327 Total post-QC SNPs: M = 705881 Variance component 1: 705881 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 2875.81 sec === Reading phenotype and covariate data === WARNING: Ignoring indiv not in genotype data: FID=6018780, IID=6018780 WARNING: Ignoring indiv not in genotype data: FID=6012488, IID=6012488 WARNING: Ignoring indiv not in genotype data: FID=5998913, IID=5998913 WARNING: Ignoring indiv not in genotype data: FID=5989416, IID=5989416 WARNING: Ignoring indiv not in genotype data: FID=5985954, IID=5985954 Read data for 460238 indivs (ignored 914 without genotypes) from: /n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz WARNING: Ignoring indiv not in genotype data: FID=1000129, IID=1000129 WARNING: Ignoring indiv not in genotype data: FID=1000170, IID=1000170 WARNING: Ignoring indiv not in genotype data: FID=1000224, IID=1000224 WARNING: Ignoring indiv not in genotype data: FID=1000362, IID=1000362 WARNING: Ignoring indiv not in genotype data: FID=1000379, IID=1000379 Read data for 502655 indivs (ignored 43331 without genotypes) from: /n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab Number of indivs with no missing phenotype(s) to use: 442700 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 = 442700 Singular values of covariate matrix: S[0] = 2.26671e+06 S[1] = 4750.04 S[2] = 468.862 S[3] = 292.809 S[4] = 199.485 S[5] = 194.001 S[6] = 183.178 S[7] = 174.724 S[8] = 170.441 S[9] = 165.144 S[10] = 162.417 S[11] = 153.429 S[12] = 145.446 S[13] = 143.366 S[14] = 140.782 S[15] = 135.525 S[16] = 132.141 S[17] = 129.926 S[18] = 125.845 S[19] = 116.015 S[20] = 111.816 S[21] = 99.5875 S[22] = 44.7676 S[23] = 23.9375 S[24] = 19.269 S[25] = 0.981938 S[26] = 0.981754 S[27] = 0.981616 S[28] = 0.981406 S[29] = 0.981281 S[30] = 0.981181 S[31] = 0.981155 S[32] = 0.98097 S[33] = 0.980876 S[34] = 0.980713 S[35] = 0.980516 S[36] = 0.980474 S[37] = 0.98032 S[38] = 0.980192 S[39] = 0.980028 S[40] = 0.979937 S[41] = 0.979701 S[42] = 0.979543 S[43] = 0.962265 S[44] = 0.885907 S[45] = 5.04613e-12 S[46] = 5.77284e-13 S[47] = 4.96444e-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: 440121.263670 Dimension of all-1s proj space (Nused-1): 442699 Time for covariate data setup + Bolt initialization = 5330.26 sec Phenotype 1: N = 442700 mean = -0.0176807 std = 0.988721 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 419.639 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 442700) 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=338.35 rNorms/orig: (0.6,0.7) res2s: 720909..142940 iter 2: time=305.78 rNorms/orig: (0.5,0.6) res2s: 857126..185100 iter 3: time=301.00 rNorms/orig: (0.3,0.4) res2s: 987922..222910 iter 4: time=289.80 rNorms/orig: (0.2,0.2) res2s: 1.03595e+06..237949 iter 5: time=302.53 rNorms/orig: (0.1,0.2) res2s: 1.06208e+06..244890 iter 6: time=296.59 rNorms/orig: (0.09,0.1) res2s: 1.07412e+06..248456 iter 7: time=289.71 rNorms/orig: (0.06,0.06) res2s: 1.08003e+06..250170 iter 8: time=293.89 rNorms/orig: (0.04,0.04) res2s: 1.08285e+06..251055 iter 9: time=293.43 rNorms/orig: (0.02,0.03) res2s: 1.08419e+06..251404 iter 10: time=292.81 rNorms/orig: (0.02,0.02) res2s: 1.08487e+06..251573 iter 11: time=282.34 rNorms/orig: (0.01,0.01) res2s: 1.08517e+06..251656 iter 12: time=278.76 rNorms/orig: (0.006,0.007) res2s: 1.08531e+06..251693 iter 13: time=284.93 rNorms/orig: (0.004,0.005) res2s: 1.08536e+06..251709 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.8%, memory/overhead = 48.2% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0582007 Estimating MC scaling f_REML at log(delta) = -0.0057404, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=286.81 rNorms/orig: (1,1) res2s: 76459.1..41893.7 iter 2: time=282.99 rNorms/orig: (1,1) res2s: 110302..66578.8 iter 3: time=283.24 rNorms/orig: (0.8,0.9) res2s: 165720..102410 iter 4: time=282.98 rNorms/orig: (0.6,0.7) res2s: 203384..125178 iter 5: time=280.27 rNorms/orig: (0.5,0.6) res2s: 231831..140468 iter 6: time=279.98 rNorms/orig: (0.4,0.4) res2s: 251889..151694 iter 7: time=281.80 rNorms/orig: (0.3,0.3) res2s: 266277..159376 iter 8: time=280.64 rNorms/orig: (0.2,0.3) res2s: 276034..165115 iter 9: time=281.95 rNorms/orig: (0.2,0.2) res2s: 282759..168393 iter 10: time=282.19 rNorms/orig: (0.1,0.2) res2s: 287602..170614 iter 11: time=282.23 rNorms/orig: (0.1,0.1) res2s: 290758..172159 iter 12: time=281.74 rNorms/orig: (0.09,0.09) res2s: 292835..173165 iter 13: time=281.53 rNorms/orig: (0.07,0.07) res2s: 293988..173769 iter 14: time=280.98 rNorms/orig: (0.05,0.06) res2s: 294817..174208 iter 15: time=283.18 rNorms/orig: (0.04,0.04) res2s: 295338..174485 iter 16: time=283.72 rNorms/orig: (0.03,0.04) res2s: 295680..174654 iter 17: time=284.98 rNorms/orig: (0.02,0.03) res2s: 295866..174768 iter 18: time=283.31 rNorms/orig: (0.02,0.02) res2s: 296008..174842 iter 19: time=281.53 rNorms/orig: (0.01,0.01) res2s: 296099..174887 iter 20: time=285.57 rNorms/orig: (0.01,0.01) res2s: 296153..174914 iter 21: time=286.50 rNorms/orig: (0.009,0.009) res2s: 296183..174930 iter 22: time=281.34 rNorms/orig: (0.007,0.007) res2s: 296204..174942 iter 23: time=283.90 rNorms/orig: (0.005,0.006) res2s: 296217..174948 iter 24: time=281.67 rNorms/orig: (0.004,0.004) res2s: 296225..174952 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 = -0.266608 Estimating MC scaling f_REML at log(delta) = 0.896018, h2 = 0.288689... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=284.90 rNorms/orig: (0.7,0.8) res2s: 509686..119835 iter 2: time=284.24 rNorms/orig: (0.6,0.7) res2s: 627372..160877 iter 3: time=286.52 rNorms/orig: (0.4,0.4) res2s: 753223..201316 iter 4: time=285.48 rNorms/orig: (0.2,0.3) res2s: 805008..218946 iter 5: time=281.53 rNorms/orig: (0.2,0.2) res2s: 834859..227728 iter 6: time=282.63 rNorms/orig: (0.1,0.1) res2s: 849864..232594 iter 7: time=283.37 rNorms/orig: (0.08,0.09) res2s: 857837..235116 iter 8: time=286.91 rNorms/orig: (0.06,0.06) res2s: 861916..236528 iter 9: time=285.39 rNorms/orig: (0.04,0.04) res2s: 864028..237133 iter 10: time=286.42 rNorms/orig: (0.03,0.03) res2s: 865173..237447 iter 11: time=282.26 rNorms/orig: (0.02,0.02) res2s: 865733..237614 iter 12: time=278.27 rNorms/orig: (0.01,0.01) res2s: 866006..237695 iter 13: time=281.83 rNorms/orig: (0.008,0.009) res2s: 866117..237733 iter 14: time=280.41 rNorms/orig: (0.005,0.006) res2s: 866179..237754 iter 15: time=280.63 rNorms/orig: (0.003,0.004) res2s: 866208..237764 Converged at iter 15: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 52.7%, memory/overhead = 47.3% MCscaling: logDelta = 0.90, h2 = 0.289, f = 0.000647531 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.289 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.275713, logDelta = 0.896018, f = 0.000647531 Time for fitting variance components = 15472.1 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=614.84 rNorms/orig: (0.5,0.9) res2s: 119806..218591 iter 2: time=605.19 rNorms/orig: (0.5,0.8) res2s: 161537..257143 iter 3: time=605.31 rNorms/orig: (0.3,0.5) res2s: 203537..276201 iter 4: time=606.56 rNorms/orig: (0.2,0.3) res2s: 222791..284526 iter 5: time=606.96 rNorms/orig: (0.1,0.2) res2s: 232364..289875 iter 6: time=606.47 rNorms/orig: (0.07,0.1) res2s: 237851..292474 iter 7: time=606.86 rNorms/orig: (0.06,0.1) res2s: 240814..293897 iter 8: time=606.84 rNorms/orig: (0.04,0.06) res2s: 242513..294670 iter 9: time=607.34 rNorms/orig: (0.02,0.05) res2s: 243234..295031 iter 10: time=606.73 rNorms/orig: (0.02,0.03) res2s: 243645..295222 iter 11: time=606.80 rNorms/orig: (0.01,0.02) res2s: 243865..295317 iter 12: time=607.56 rNorms/orig: (0.008,0.02) res2s: 243970..295365 iter 13: time=606.30 rNorms/orig: (0.005,0.01) res2s: 244022..295387 iter 14: time=607.29 rNorms/orig: (0.003,0.007) res2s: 244051..295399 iter 15: time=608.70 rNorms/orig: (0.002,0.005) res2s: 244065..295404 iter 16: time=606.26 rNorms/orig: (0.001,0.004) res2s: 244072..295406 iter 17: time=607.85 rNorms/orig: (0.0007,0.002) res2s: 244076..295408 iter 18: time=607.58 rNorms/orig: (0.0004,0.002) res2s: 244077..295408 iter 19: time=605.38 rNorms/orig: (0.0003,0.001) res2s: 244078..295409 iter 20: time=605.70 rNorms/orig: (0.0002,0.0007) res2s: 244078..295409 iter 21: time=605.81 rNorms/orig: (0.0001,0.0005) res2s: 244079..295409 Converged at iter 21: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 76.6%, memory/overhead = 23.4% AvgPro: 1.719 AvgRetro: 1.697 Calibration: 1.012 (0.002) (30 SNPs) Ratio of medians: 1.013 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 13163.2 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 14.0003 sec === Reading LD Scores for calibration of Bayesian assoc stats === Looking up LD Scores... Looking for column header 'SNP': column number = 1 Looking for column header 'LDSCORE': column number = 5 Found LD Scores for 601289/705881 SNPs Estimating inflation of LINREG chisq stats using MLMe as reference... Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 442.7) # of SNPs remaining after outlier window removal: 569721/579570 Intercept of LD Score regression for ref stats: 1.121 (0.017) Estimated attenuation: 0.125 (0.017) Intercept of LD Score regression for cur stats: 1.120 (0.015) Calibration factor (ref/cur) to multiply by: 1.002 (0.003) LINREG intercept inflation = 0.998451 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 = 354160 Singular values of covariate matrix: S[0] = 2.0273e+06 S[1] = 4247.18 S[2] = 419.329 S[3] = 261.912 S[4] = 178.411 S[5] = 173.492 S[6] = 163.782 S[7] = 156.187 S[8] = 152.484 S[9] = 147.788 S[10] = 145.391 S[11] = 137.304 S[12] = 130.23 S[13] = 128.163 S[14] = 125.886 S[15] = 121.212 S[16] = 118.114 S[17] = 116.241 S[18] = 112.546 S[19] = 103.745 S[20] = 99.9864 S[21] = 89.0384 S[22] = 39.8904 S[23] = 21.3897 S[24] = 17.2311 S[25] = 0.880213 S[26] = 0.879787 S[27] = 0.879251 S[28] = 0.878772 S[29] = 0.87848 S[30] = 0.878103 S[31] = 0.877789 S[32] = 0.877506 S[33] = 0.877386 S[34] = 0.877108 S[35] = 0.87666 S[36] = 0.876415 S[37] = 0.876116 S[38] = 0.875691 S[39] = 0.875481 S[40] = 0.875264 S[41] = 0.874964 S[42] = 0.874327 S[43] = 0.8633 S[44] = 0.792865 S[45] = 6.35959e-12 S[46] = 4.0257e-13 S[47] = 1.73132e-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: 352086.527236 Dimension of all-1s proj space (Nused-1): 354159 Beginning variational Bayes iter 1: time=602.97 for 18 active reps iter 2: time=396.87 for 18 active reps approxLL diffs: (10517.40,14133.25) iter 3: time=394.01 for 18 active reps approxLL diffs: (1044.82,3003.45) iter 4: time=391.64 for 18 active reps approxLL diffs: (196.39,1186.37) iter 5: time=397.24 for 18 active reps approxLL diffs: (55.91,572.80) iter 6: time=394.84 for 18 active reps approxLL diffs: (22.15,291.04) iter 7: time=391.97 for 18 active reps approxLL diffs: (11.13,208.28) iter 8: time=392.98 for 18 active reps approxLL diffs: (6.41,133.03) iter 9: time=397.02 for 18 active reps approxLL diffs: (4.11,111.76) iter 10: time=392.95 for 18 active reps approxLL diffs: (2.84,74.88) iter 11: time=393.65 for 18 active reps approxLL diffs: (2.04,62.18) iter 12: time=393.80 for 18 active reps approxLL diffs: (1.50,46.34) iter 13: time=392.88 for 18 active reps approxLL diffs: (1.14,40.56) iter 14: time=391.86 for 18 active reps approxLL diffs: (0.89,40.69) iter 15: time=392.90 for 18 active reps approxLL diffs: (0.71,24.06) iter 16: time=392.11 for 18 active reps approxLL diffs: (0.59,29.05) iter 17: time=393.43 for 18 active reps approxLL diffs: (0.49,25.73) iter 18: time=398.58 for 18 active reps approxLL diffs: (0.42,17.10) iter 19: time=394.53 for 18 active reps approxLL diffs: (0.35,13.00) iter 20: time=397.93 for 18 active reps approxLL diffs: (0.29,10.76) iter 21: time=398.08 for 18 active reps approxLL diffs: (0.24,6.24) iter 22: time=398.53 for 18 active reps approxLL diffs: (0.20,6.77) iter 23: time=397.83 for 18 active reps approxLL diffs: (0.17,7.53) iter 24: time=406.74 for 18 active reps approxLL diffs: (0.15,6.08) iter 25: time=411.59 for 18 active reps approxLL diffs: (0.13,6.33) iter 26: time=403.60 for 18 active reps approxLL diffs: (0.11,7.99) iter 27: time=399.49 for 18 active reps approxLL diffs: (0.10,4.42) iter 28: time=399.98 for 18 active reps approxLL diffs: (0.09,2.86) iter 29: time=401.94 for 18 active reps approxLL diffs: (0.07,3.90) iter 30: time=399.14 for 18 active reps approxLL diffs: (0.06,4.53) iter 31: time=399.86 for 18 active reps approxLL diffs: (0.05,4.36) iter 32: time=397.27 for 18 active reps approxLL diffs: (0.05,3.60) iter 33: time=397.76 for 18 active reps approxLL diffs: (0.04,2.64) iter 34: time=397.82 for 18 active reps approxLL diffs: (0.04,2.30) iter 35: time=398.80 for 18 active reps approxLL diffs: (0.03,2.52) iter 36: time=397.15 for 18 active reps approxLL diffs: (0.03,1.83) iter 37: time=397.55 for 18 active reps approxLL diffs: (0.02,1.62) iter 38: time=396.49 for 18 active reps approxLL diffs: (0.02,1.92) iter 39: time=398.59 for 18 active reps approxLL diffs: (0.02,1.65) iter 40: time=400.39 for 18 active reps approxLL diffs: (0.02,1.00) iter 41: time=402.60 for 18 active reps approxLL diffs: (0.01,0.86) iter 42: time=403.92 for 18 active reps approxLL diffs: (0.01,0.68) iter 43: time=400.07 for 18 active reps approxLL diffs: (0.01,0.84) iter 44: time=403.39 for 18 active reps approxLL diffs: (0.01,0.62) iter 45: time=400.61 for 18 active reps approxLL diffs: (0.01,0.20) iter 46: time=363.02 for 14 active reps approxLL diffs: (0.01,0.25) iter 47: time=320.25 for 12 active reps approxLL diffs: (0.01,0.49) iter 48: time=321.95 for 12 active reps approxLL diffs: (0.01,0.41) iter 49: time=332.71 for 11 active reps approxLL diffs: (0.01,0.21) iter 50: time=311.39 for 10 active reps approxLL diffs: (0.01,0.20) iter 51: time=311.28 for 10 active reps approxLL diffs: (0.00,0.24) iter 52: time=254.55 for 8 active reps approxLL diffs: (0.02,0.43) iter 53: time=254.66 for 8 active reps approxLL diffs: (0.02,0.49) iter 54: time=256.07 for 8 active reps approxLL diffs: (0.02,0.60) iter 55: time=255.32 for 8 active reps approxLL diffs: (0.01,0.50) iter 56: time=253.42 for 8 active reps approxLL diffs: (0.00,1.21) iter 57: time=273.89 for 7 active reps approxLL diffs: (0.01,0.25) iter 58: time=278.77 for 7 active reps approxLL diffs: (0.01,0.15) iter 59: time=250.66 for 5 active reps approxLL diffs: (0.03,0.23) iter 60: time=234.14 for 5 active reps approxLL diffs: (0.02,0.31) iter 61: time=233.41 for 5 active reps approxLL diffs: (0.01,0.29) iter 62: time=233.51 for 5 active reps approxLL diffs: (0.01,0.15) iter 63: time=215.99 for 4 active reps approxLL diffs: (0.02,0.08) iter 64: time=216.13 for 4 active reps approxLL diffs: (0.02,0.05) iter 65: time=216.48 for 4 active reps approxLL diffs: (0.02,0.08) iter 66: time=221.42 for 4 active reps approxLL diffs: (0.01,0.14) iter 67: time=217.89 for 4 active reps approxLL diffs: (0.01,0.13) iter 68: time=226.04 for 3 active reps approxLL diffs: (0.02,0.07) iter 69: time=225.52 for 3 active reps approxLL diffs: (0.01,0.04) iter 70: time=224.27 for 3 active reps approxLL diffs: (0.01,0.07) iter 71: time=211.22 for 2 active reps approxLL diffs: (0.01,0.15) iter 72: time=206.81 for 2 active reps approxLL diffs: (0.01,0.24) iter 73: time=171.23 for 1 active reps approxLL diffs: (0.17,0.17) iter 74: time=168.54 for 1 active reps approxLL diffs: (0.05,0.05) iter 75: time=169.49 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 = 79.1%, memory/overhead = 20.9% Computing predictions on left-out cross-validation fold Time for computing predictions = 8587.25 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.02: 0.182075 f2=0.3, p=0.01: 0.181789 f2=0.5, p=0.01: 0.180272 ... f2=0.5, p=0.5: 0.117981 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.978422 Absolute prediction MSE using standard LMM: 0.862987 Absolute prediction MSE, fold-best f2=0.3, p=0.02: 0.800275 Absolute pred MSE using f2=0.5, p=0.5: 0.862987 Absolute pred MSE using f2=0.5, p=0.2: 0.842335 Absolute pred MSE using f2=0.5, p=0.1: 0.825072 Absolute pred MSE using f2=0.5, p=0.05: 0.811472 Absolute pred MSE using f2=0.5, p=0.02: 0.803275 Absolute pred MSE using f2=0.5, p=0.01: 0.802040 Absolute pred MSE using f2=0.3, p=0.5: 0.855762 Absolute pred MSE using f2=0.3, p=0.2: 0.830416 Absolute pred MSE using f2=0.3, p=0.1: 0.814096 Absolute pred MSE using f2=0.3, p=0.05: 0.804115 Absolute pred MSE using f2=0.3, p=0.02: 0.800275 Absolute pred MSE using f2=0.3, p=0.01: 0.800556 Absolute pred MSE using f2=0.1, p=0.5: 0.846603 Absolute pred MSE using f2=0.1, p=0.2: 0.820842 Absolute pred MSE using f2=0.1, p=0.1: 0.807516 Absolute pred MSE using f2=0.1, p=0.05: 0.802677 Absolute pred MSE using f2=0.1, p=0.02: 0.803331 Absolute pred MSE using f2=0.1, p=0.01: 0.805284 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.118 Relative improvement in prediction MSE using non-inf model: 0.073 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.02 Time for estimating mixture parameters = 38592.5 sec === Computing Bayesian mixed model assoc stats with mixture prior === Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Beginning variational Bayes iter 1: time=673.78 for 23 active reps iter 2: time=469.06 for 23 active reps approxLL diffs: (15554.10,17640.25) iter 3: time=468.71 for 23 active reps approxLL diffs: (3255.99,3808.91) iter 4: time=470.49 for 23 active reps approxLL diffs: (1223.80,1473.15) iter 5: time=470.72 for 23 active reps approxLL diffs: (634.97,767.24) iter 6: time=469.06 for 23 active reps approxLL diffs: (314.32,405.65) iter 7: time=464.05 for 23 active reps approxLL diffs: (196.73,246.77) iter 8: time=468.28 for 23 active reps approxLL diffs: (127.62,163.32) iter 9: time=465.97 for 23 active reps approxLL diffs: (79.97,112.13) iter 10: time=467.01 for 23 active reps approxLL diffs: (54.59,82.33) iter 11: time=469.92 for 23 active reps approxLL diffs: (40.37,60.78) iter 12: time=468.40 for 23 active reps approxLL diffs: (33.49,49.56) iter 13: time=465.37 for 23 active reps approxLL diffs: (24.66,38.98) iter 14: time=466.86 for 23 active reps approxLL diffs: (15.91,26.49) iter 15: time=465.49 for 23 active reps approxLL diffs: (12.77,24.57) iter 16: time=466.06 for 23 active reps approxLL diffs: (12.55,23.88) iter 17: time=462.95 for 23 active reps approxLL diffs: (9.95,22.56) iter 18: time=465.31 for 23 active reps approxLL diffs: (6.62,17.28) iter 19: time=464.53 for 23 active reps approxLL diffs: (5.69,15.14) iter 20: time=468.39 for 23 active reps approxLL diffs: (4.89,11.83) iter 21: time=462.30 for 23 active reps approxLL diffs: (4.45,13.40) iter 22: time=460.55 for 23 active reps approxLL diffs: (3.63,10.08) iter 23: time=463.35 for 23 active reps approxLL diffs: (2.78,6.73) iter 24: time=470.52 for 23 active reps approxLL diffs: (2.01,6.89) iter 25: time=458.74 for 23 active reps approxLL diffs: (1.65,9.80) iter 26: time=455.64 for 23 active reps approxLL diffs: (1.35,9.40) iter 27: time=456.07 for 23 active reps approxLL diffs: (0.95,5.54) iter 28: time=456.95 for 23 active reps approxLL diffs: (0.37,4.28) iter 29: time=459.35 for 23 active reps approxLL diffs: (0.20,3.52) iter 30: time=459.21 for 23 active reps approxLL diffs: (0.14,3.28) iter 31: time=463.56 for 23 active reps approxLL diffs: (0.10,4.52) iter 32: time=457.87 for 23 active reps approxLL diffs: (0.08,2.29) iter 33: time=460.61 for 23 active reps approxLL diffs: (0.06,4.34) iter 34: time=463.96 for 23 active reps approxLL diffs: (0.06,4.36) iter 35: time=462.59 for 23 active reps approxLL diffs: (0.06,2.55) iter 36: time=458.54 for 23 active reps approxLL diffs: (0.08,2.41) iter 37: time=461.42 for 23 active reps approxLL diffs: (0.08,2.73) iter 38: time=462.89 for 23 active reps approxLL diffs: (0.08,2.32) iter 39: time=467.61 for 23 active reps approxLL diffs: (0.05,3.33) iter 40: time=480.03 for 23 active reps approxLL diffs: (0.04,2.65) iter 41: time=486.69 for 23 active reps approxLL diffs: (0.03,1.46) iter 42: time=491.17 for 23 active reps approxLL diffs: (0.03,1.91) iter 43: time=479.12 for 23 active reps approxLL diffs: (0.01,2.61) iter 44: time=467.01 for 23 active reps approxLL diffs: (0.01,2.26) iter 45: time=429.64 for 21 active reps approxLL diffs: (0.01,2.20) iter 46: time=407.68 for 20 active reps approxLL diffs: (0.01,1.31) iter 47: time=434.26 for 19 active reps approxLL diffs: (0.02,1.57) iter 48: time=437.32 for 19 active reps approxLL diffs: (0.02,1.65) iter 49: time=430.25 for 19 active reps approxLL diffs: (0.01,0.99) iter 50: time=436.05 for 19 active reps approxLL diffs: (0.01,1.81) iter 51: time=430.06 for 19 active reps approxLL diffs: (0.01,3.64) iter 52: time=408.75 for 18 active reps approxLL diffs: (0.01,2.03) iter 53: time=387.70 for 17 active reps approxLL diffs: (0.01,1.77) iter 54: time=361.71 for 16 active reps approxLL diffs: (0.02,0.92) iter 55: time=363.88 for 16 active reps approxLL diffs: (0.01,1.39) iter 56: time=367.09 for 16 active reps approxLL diffs: (0.01,0.54) iter 57: time=382.84 for 16 active reps approxLL diffs: (0.01,1.26) iter 58: time=372.54 for 14 active reps approxLL diffs: (0.01,1.95) iter 59: time=328.84 for 12 active reps approxLL diffs: (0.03,1.45) iter 60: time=359.49 for 12 active reps approxLL diffs: (0.02,1.13) iter 61: time=359.04 for 12 active reps approxLL diffs: (0.01,1.03) iter 62: time=374.00 for 11 active reps approxLL diffs: (0.03,1.99) iter 63: time=360.50 for 11 active reps approxLL diffs: (0.00,2.45) iter 64: time=332.78 for 10 active reps approxLL diffs: (0.09,1.39) iter 65: time=327.88 for 10 active reps approxLL diffs: (0.04,1.02) iter 66: time=340.59 for 10 active reps approxLL diffs: (0.04,0.70) iter 67: time=332.35 for 10 active reps approxLL diffs: (0.03,0.17) iter 68: time=321.86 for 10 active reps approxLL diffs: (0.00,0.55) iter 69: time=304.69 for 9 active reps approxLL diffs: (0.01,1.03) iter 70: time=320.75 for 9 active reps approxLL diffs: (0.00,1.30) iter 71: time=280.21 for 6 active reps approxLL diffs: (0.01,1.13) iter 72: time=281.79 for 6 active reps approxLL diffs: (0.01,0.69) iter 73: time=258.88 for 4 active reps approxLL diffs: (0.04,0.36) iter 74: time=236.90 for 4 active reps approxLL diffs: (0.03,0.35) iter 75: time=232.59 for 4 active reps approxLL diffs: (0.01,0.53) iter 76: time=238.74 for 4 active reps approxLL diffs: (0.01,0.89) iter 77: time=230.46 for 4 active reps approxLL diffs: (0.01,0.58) iter 78: time=229.83 for 4 active reps approxLL diffs: (0.01,0.16) iter 79: time=241.04 for 3 active reps approxLL diffs: (0.02,0.03) iter 80: time=243.53 for 3 active reps approxLL diffs: (0.01,0.03) iter 81: time=220.75 for 2 active reps approxLL diffs: (0.02,0.03) iter 82: time=225.77 for 2 active reps approxLL diffs: (0.01,0.02) iter 83: time=221.02 for 2 active reps approxLL diffs: (0.01,0.01) iter 84: time=193.30 for 1 active reps approxLL diffs: (0.00,0.00) Converged at iter 84: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 80.1%, memory/overhead = 19.9% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 442.7) # of SNPs remaining after outlier window removal: 569721/579570 Intercept of LD Score regression for ref stats: 1.121 (0.017) Estimated attenuation: 0.125 (0.017) Intercept of LD Score regression for cur stats: 1.117 (0.017) Calibration factor (ref/cur) to multiply by: 1.004 (0.001) Time for computing Bayesian mixed model assoc stats = 34176.2 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=418.31 for 1 active reps iter 2: time=211.27 for 1 active reps approxLL diffs: (17716.96,17716.96) iter 3: time=214.39 for 1 active reps approxLL diffs: (3803.90,3803.90) iter 4: time=214.84 for 1 active reps approxLL diffs: (1465.86,1465.86) iter 5: time=214.99 for 1 active reps approxLL diffs: (770.43,770.43) iter 6: time=227.16 for 1 active reps approxLL diffs: (399.78,399.78) iter 7: time=214.48 for 1 active reps approxLL diffs: (242.35,242.35) iter 8: time=214.14 for 1 active reps approxLL diffs: (149.97,149.97) iter 9: time=203.59 for 1 active reps approxLL diffs: (98.94,98.94) iter 10: time=205.05 for 1 active reps approxLL diffs: (68.69,68.69) iter 11: time=200.44 for 1 active reps approxLL diffs: (55.30,55.30) iter 12: time=194.27 for 1 active reps approxLL diffs: (47.24,47.24) iter 13: time=202.57 for 1 active reps approxLL diffs: (34.10,34.10) iter 14: time=198.38 for 1 active reps approxLL diffs: (25.74,25.74) iter 15: time=195.93 for 1 active reps approxLL diffs: (23.38,23.38) iter 16: time=193.30 for 1 active reps approxLL diffs: (23.13,23.13) iter 17: time=201.47 for 1 active reps approxLL diffs: (22.10,22.10) iter 18: time=199.45 for 1 active reps approxLL diffs: (14.98,14.98) iter 19: time=195.07 for 1 active reps approxLL diffs: (10.01,10.01) iter 20: time=196.28 for 1 active reps approxLL diffs: (7.07,7.07) iter 21: time=192.13 for 1 active reps approxLL diffs: (5.99,5.99) iter 22: time=194.81 for 1 active reps approxLL diffs: (4.96,4.96) iter 23: time=194.10 for 1 active reps approxLL diffs: (2.57,2.57) iter 24: time=196.71 for 1 active reps approxLL diffs: (2.05,2.05) iter 25: time=213.33 for 1 active reps approxLL diffs: (1.41,1.41) iter 26: time=205.05 for 1 active reps approxLL diffs: (0.66,0.66) iter 27: time=197.01 for 1 active reps approxLL diffs: (0.47,0.47) iter 28: time=196.37 for 1 active reps approxLL diffs: (0.74,0.74) iter 29: time=201.32 for 1 active reps approxLL diffs: (1.90,1.90) iter 30: time=192.25 for 1 active reps approxLL diffs: (1.45,1.45) iter 31: time=206.05 for 1 active reps approxLL diffs: (0.43,0.43) iter 32: time=202.93 for 1 active reps approxLL diffs: (0.25,0.25) iter 33: time=199.38 for 1 active reps approxLL diffs: (0.27,0.27) iter 34: time=204.05 for 1 active reps approxLL diffs: (0.39,0.39) iter 35: time=200.06 for 1 active reps approxLL diffs: (0.41,0.41) iter 36: time=199.21 for 1 active reps approxLL diffs: (0.73,0.73) iter 37: time=197.90 for 1 active reps approxLL diffs: (1.33,1.33) iter 38: time=199.61 for 1 active reps approxLL diffs: (1.20,1.20) iter 39: time=221.18 for 1 active reps approxLL diffs: (0.74,0.74) iter 40: time=220.78 for 1 active reps approxLL diffs: (0.39,0.39) iter 41: time=201.09 for 1 active reps approxLL diffs: (0.31,0.31) iter 42: time=204.06 for 1 active reps approxLL diffs: (0.39,0.39) iter 43: time=218.47 for 1 active reps approxLL diffs: (0.23,0.23) iter 44: time=208.92 for 1 active reps approxLL diffs: (0.05,0.05) iter 45: time=212.75 for 1 active reps approxLL diffs: (0.02,0.02) iter 46: time=216.13 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 46: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 58.7%, memory/overhead = 41.3% Time for computing and writing betas = 9615.45 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.25949 (705851 good SNPs) lambdaGC: 1.35837 Mean BOLT_LMM_INF: 2.399 (705851 good SNPs) lambdaGC: 1.37633 Mean BOLT_LMM: 2.49859 (705851 good SNPs) lambdaGC: 1.39045 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 8153.18 sec Total elapsed time for analysis = 127812 sec