+-----------------------------+ | ___ | | 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_RED_COUNT \ --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_RED_COUNT.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_RED_COUNT.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 = 4526.05 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: 445174 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 = 445174 Singular values of covariate matrix: S[0] = 2.27218e+06 S[1] = 4767.52 S[2] = 470.058 S[3] = 293.745 S[4] = 199.992 S[5] = 194.515 S[6] = 183.729 S[7] = 175.225 S[8] = 170.926 S[9] = 165.594 S[10] = 162.854 S[11] = 153.859 S[12] = 145.85 S[13] = 143.779 S[14] = 141.225 S[15] = 135.93 S[16] = 132.54 S[17] = 130.279 S[18] = 126.213 S[19] = 116.302 S[20] = 112.142 S[21] = 99.8354 S[22] = 44.8381 S[23] = 23.9816 S[24] = 19.3269 S[25] = 0.984494 S[26] = 0.984311 S[27] = 0.984252 S[28] = 0.984102 S[29] = 0.983933 S[30] = 0.983811 S[31] = 0.983677 S[32] = 0.983597 S[33] = 0.98354 S[34] = 0.983358 S[35] = 0.983265 S[36] = 0.983209 S[37] = 0.983089 S[38] = 0.982946 S[39] = 0.982685 S[40] = 0.982593 S[41] = 0.982481 S[42] = 0.98228 S[43] = 0.964122 S[44] = 0.88877 S[45] = 2.88553e-12 S[46] = 5.20265e-13 S[47] = 3.36067e-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: 442581.233195 Dimension of all-1s proj space (Nused-1): 445173 Time for covariate data setup + Bolt initialization = 5570.14 sec Phenotype 1: N = 445174 mean = -0.0158659 std = 0.987356 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 504.87 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 445174) 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=393.74 rNorms/orig: (0.6,0.7) res2s: 722093..139119 iter 2: time=375.78 rNorms/orig: (0.6,0.6) res2s: 853548..177163 iter 3: time=370.99 rNorms/orig: (0.3,0.3) res2s: 988233..218243 iter 4: time=365.19 rNorms/orig: (0.2,0.3) res2s: 1.03972e+06..231209 iter 5: time=379.51 rNorms/orig: (0.1,0.2) res2s: 1.06551e+06..238800 iter 6: time=379.45 rNorms/orig: (0.09,0.1) res2s: 1.07872e+06..242758 iter 7: time=367.95 rNorms/orig: (0.06,0.06) res2s: 1.08537e+06..244700 iter 8: time=374.07 rNorms/orig: (0.04,0.04) res2s: 1.08845e+06..245510 iter 9: time=378.07 rNorms/orig: (0.02,0.03) res2s: 1.08991e+06..245883 iter 10: time=387.33 rNorms/orig: (0.02,0.02) res2s: 1.09065e+06..246075 iter 11: time=369.76 rNorms/orig: (0.01,0.01) res2s: 1.09096e+06..246159 iter 12: time=364.01 rNorms/orig: (0.006,0.007) res2s: 1.0911e+06..246201 iter 13: time=363.34 rNorms/orig: (0.004,0.005) res2s: 1.09116e+06..246214 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.2%, memory/overhead = 53.8% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.110616 Estimating MC scaling f_REML at log(delta) = -0.0057401, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=344.11 rNorms/orig: (1,1) res2s: 76354.9..39997.2 iter 2: time=358.44 rNorms/orig: (1,1) res2s: 109266..61292 iter 3: time=412.36 rNorms/orig: (0.8,0.9) res2s: 164529..98977.8 iter 4: time=395.89 rNorms/orig: (0.6,0.7) res2s: 203101..117990 iter 5: time=404.76 rNorms/orig: (0.5,0.5) res2s: 230942..133799 iter 6: time=404.71 rNorms/orig: (0.4,0.4) res2s: 251547..145690 iter 7: time=424.02 rNorms/orig: (0.3,0.3) res2s: 266600..154207 iter 8: time=389.37 rNorms/orig: (0.2,0.3) res2s: 276903..159350 iter 9: time=343.79 rNorms/orig: (0.2,0.2) res2s: 283932..162709 iter 10: time=315.57 rNorms/orig: (0.1,0.2) res2s: 289024..165173 iter 11: time=327.31 rNorms/orig: (0.1,0.1) res2s: 292195..166728 iter 12: time=334.49 rNorms/orig: (0.08,0.09) res2s: 294307..167892 iter 13: time=331.39 rNorms/orig: (0.07,0.07) res2s: 295529..168431 iter 14: time=345.48 rNorms/orig: (0.05,0.05) res2s: 296389..168812 iter 15: time=341.79 rNorms/orig: (0.04,0.04) res2s: 296939..169049 iter 16: time=344.01 rNorms/orig: (0.03,0.03) res2s: 297300..169212 iter 17: time=356.75 rNorms/orig: (0.02,0.03) res2s: 297498..169310 iter 18: time=346.38 rNorms/orig: (0.02,0.02) res2s: 297621..169376 iter 19: time=338.07 rNorms/orig: (0.01,0.01) res2s: 297710..169418 iter 20: time=313.18 rNorms/orig: (0.01,0.01) res2s: 297764..169444 iter 21: time=312.82 rNorms/orig: (0.009,0.009) res2s: 297795..169459 iter 22: time=327.64 rNorms/orig: (0.007,0.007) res2s: 297816..169469 iter 23: time=338.64 rNorms/orig: (0.005,0.005) res2s: 297829..169475 iter 24: time=338.50 rNorms/orig: (0.004,0.004) res2s: 297837..169479 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.1%, memory/overhead = 53.9% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.225667 Estimating MC scaling f_REML at log(delta) = 0.731498, h2 = 0.323608... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=330.71 rNorms/orig: (0.8,0.9) res2s: 374116..98527.5 iter 2: time=341.50 rNorms/orig: (0.7,0.8) res2s: 471439..133392 iter 3: time=306.46 rNorms/orig: (0.5,0.5) res2s: 590630..178240 iter 4: time=316.44 rNorms/orig: (0.3,0.4) res2s: 646542..194952 iter 5: time=332.32 rNorms/orig: (0.2,0.2) res2s: 678084..206064 iter 6: time=318.99 rNorms/orig: (0.2,0.2) res2s: 696514..212703 iter 7: time=335.91 rNorms/orig: (0.1,0.1) res2s: 707163..216462 iter 8: time=342.01 rNorms/orig: (0.08,0.08) res2s: 712871..218270 iter 9: time=350.83 rNorms/orig: (0.06,0.06) res2s: 715973..219222 iter 10: time=324.05 rNorms/orig: (0.04,0.04) res2s: 717773..219784 iter 11: time=333.93 rNorms/orig: (0.03,0.03) res2s: 718648..220068 iter 12: time=331.51 rNorms/orig: (0.02,0.02) res2s: 719115..220234 iter 13: time=327.24 rNorms/orig: (0.01,0.01) res2s: 719330..220295 iter 14: time=328.24 rNorms/orig: (0.008,0.009) res2s: 719452..220330 iter 15: time=326.01 rNorms/orig: (0.006,0.006) res2s: 719514..220347 iter 16: time=338.03 rNorms/orig: (0.004,0.004) res2s: 719546..220357 Converged at iter 16: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 46.5%, memory/overhead = 53.5% MCscaling: logDelta = 0.73, h2 = 0.324, f = 0.00153564 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.324 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.307590, logDelta = 0.731498, f = 0.00153564 Time for fitting variance components = 19357.5 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=667.72 rNorms/orig: (0.6,0.9) res2s: 98206.4..198858 iter 2: time=657.50 rNorms/orig: (0.5,0.9) res2s: 133551..240944 iter 3: time=664.95 rNorms/orig: (0.3,0.5) res2s: 180426..260670 iter 4: time=662.99 rNorms/orig: (0.2,0.4) res2s: 198589..271055 iter 5: time=672.41 rNorms/orig: (0.2,0.3) res2s: 210438..277428 iter 6: time=636.30 rNorms/orig: (0.1,0.2) res2s: 217889..281141 iter 7: time=657.09 rNorms/orig: (0.08,0.1) res2s: 222350..283129 iter 8: time=632.89 rNorms/orig: (0.05,0.1) res2s: 224390..284229 iter 9: time=646.55 rNorms/orig: (0.04,0.07) res2s: 225524..284811 iter 10: time=631.82 rNorms/orig: (0.03,0.04) res2s: 226274..285142 iter 11: time=656.62 rNorms/orig: (0.02,0.03) res2s: 226629..285316 iter 12: time=649.49 rNorms/orig: (0.01,0.02) res2s: 226819..285406 iter 13: time=645.31 rNorms/orig: (0.008,0.02) res2s: 226898..285452 iter 14: time=671.62 rNorms/orig: (0.005,0.01) res2s: 226948..285478 iter 15: time=663.91 rNorms/orig: (0.003,0.008) res2s: 226972..285491 iter 16: time=671.53 rNorms/orig: (0.002,0.006) res2s: 226985..285498 iter 17: time=640.46 rNorms/orig: (0.001,0.004) res2s: 226992..285501 iter 18: time=652.94 rNorms/orig: (0.0009,0.003) res2s: 226996..285503 iter 19: time=646.36 rNorms/orig: (0.0006,0.002) res2s: 226998..285503 iter 20: time=643.31 rNorms/orig: (0.0004,0.001) res2s: 226999..285504 iter 21: time=653.61 rNorms/orig: (0.0003,0.0009) res2s: 226999..285504 iter 22: time=640.36 rNorms/orig: (0.0002,0.0006) res2s: 227000..285504 iter 23: time=626.52 rNorms/orig: (0.0001,0.0005) res2s: 227000..285504 Converged at iter 23: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 72.5%, memory/overhead = 27.5% AvgPro: 3.874 AvgRetro: 3.816 Calibration: 1.015 (0.003) (30 SNPs) Ratio of medians: 1.015 Median of ratios: 1.013 Time for computing infinitesimal model assoc stats = 15386.5 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 9.98638 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 > 445.2) # of SNPs remaining after outlier window removal: 574191/579570 Intercept of LD Score regression for ref stats: 1.201 (0.021) Estimated attenuation: 0.159 (0.017) Intercept of LD Score regression for cur stats: 1.197 (0.019) Calibration factor (ref/cur) to multiply by: 1.003 (0.003) LINREG intercept inflation = 0.997064 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 = 356139 Singular values of covariate matrix: S[0] = 2.03251e+06 S[1] = 4263.95 S[2] = 420.393 S[3] = 262.744 S[4] = 178.722 S[5] = 173.84 S[6] = 164.236 S[7] = 156.744 S[8] = 152.936 S[9] = 148.097 S[10] = 145.66 S[11] = 137.62 S[12] = 130.397 S[13] = 128.637 S[14] = 126.442 S[15] = 121.711 S[16] = 118.675 S[17] = 116.655 S[18] = 113.091 S[19] = 103.915 S[20] = 100.144 S[21] = 89.4454 S[22] = 40.022 S[23] = 21.3923 S[24] = 17.2893 S[25] = 0.8823 S[26] = 0.881937 S[27] = 0.881573 S[28] = 0.881398 S[29] = 0.880701 S[30] = 0.880538 S[31] = 0.880375 S[32] = 0.879997 S[33] = 0.879777 S[34] = 0.879507 S[35] = 0.879248 S[36] = 0.878894 S[37] = 0.878662 S[38] = 0.878192 S[39] = 0.877712 S[40] = 0.877334 S[41] = 0.87713 S[42] = 0.876527 S[43] = 0.861918 S[44] = 0.794987 S[45] = 5.32803e-12 S[46] = 5.10875e-13 S[47] = 2.08709e-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: 354056.119310 Dimension of all-1s proj space (Nused-1): 356138 Beginning variational Bayes iter 1: time=648.69 for 18 active reps iter 2: time=431.30 for 18 active reps approxLL diffs: (13674.93,16398.01) iter 3: time=438.44 for 18 active reps approxLL diffs: (1403.47,2991.26) iter 4: time=449.30 for 18 active reps approxLL diffs: (273.98,1112.41) iter 5: time=443.24 for 18 active reps approxLL diffs: (79.65,553.00) iter 6: time=434.07 for 18 active reps approxLL diffs: (29.73,321.01) iter 7: time=440.40 for 18 active reps approxLL diffs: (13.94,209.72) iter 8: time=447.60 for 18 active reps approxLL diffs: (7.68,136.61) iter 9: time=446.42 for 18 active reps approxLL diffs: (4.76,110.17) iter 10: time=456.24 for 18 active reps approxLL diffs: (3.26,76.52) iter 11: time=448.89 for 18 active reps approxLL diffs: (2.38,60.13) iter 12: time=441.46 for 18 active reps approxLL diffs: (1.80,43.54) iter 13: time=442.06 for 18 active reps approxLL diffs: (1.40,34.15) iter 14: time=444.17 for 18 active reps approxLL diffs: (1.12,24.57) iter 15: time=479.57 for 18 active reps approxLL diffs: (0.92,20.78) iter 16: time=451.35 for 18 active reps approxLL diffs: (0.77,16.63) iter 17: time=456.94 for 18 active reps approxLL diffs: (0.59,18.46) iter 18: time=465.70 for 18 active reps approxLL diffs: (0.45,12.29) iter 19: time=458.30 for 18 active reps approxLL diffs: (0.35,8.77) iter 20: time=463.94 for 18 active reps approxLL diffs: (0.28,9.21) iter 21: time=476.16 for 18 active reps approxLL diffs: (0.22,12.08) iter 22: time=441.65 for 18 active reps approxLL diffs: (0.17,11.22) iter 23: time=428.20 for 18 active reps approxLL diffs: (0.14,8.44) iter 24: time=429.03 for 18 active reps approxLL diffs: (0.11,6.64) iter 25: time=438.44 for 18 active reps approxLL diffs: (0.09,5.26) iter 26: time=442.71 for 18 active reps approxLL diffs: (0.08,3.81) iter 27: time=430.60 for 18 active reps approxLL diffs: (0.07,4.22) iter 28: time=433.63 for 18 active reps approxLL diffs: (0.06,5.70) iter 29: time=428.99 for 18 active reps approxLL diffs: (0.05,5.67) iter 30: time=429.40 for 18 active reps approxLL diffs: (0.04,2.61) iter 31: time=434.15 for 18 active reps approxLL diffs: (0.03,1.83) iter 32: time=435.78 for 18 active reps approxLL diffs: (0.03,3.89) iter 33: time=430.72 for 18 active reps approxLL diffs: (0.03,5.93) iter 34: time=433.24 for 18 active reps approxLL diffs: (0.02,4.23) iter 35: time=434.01 for 18 active reps approxLL diffs: (0.02,3.41) iter 36: time=432.59 for 18 active reps approxLL diffs: (0.02,4.04) iter 37: time=430.72 for 18 active reps approxLL diffs: (0.01,7.24) iter 38: time=433.53 for 18 active reps approxLL diffs: (0.01,8.22) iter 39: time=439.90 for 18 active reps approxLL diffs: (0.01,2.12) iter 40: time=414.84 for 17 active reps approxLL diffs: (0.02,1.34) iter 41: time=419.22 for 17 active reps approxLL diffs: (0.01,1.59) iter 42: time=415.48 for 17 active reps approxLL diffs: (0.01,1.38) iter 43: time=425.31 for 17 active reps approxLL diffs: (0.01,0.69) iter 44: time=383.77 for 16 active reps approxLL diffs: (0.01,0.70) iter 45: time=396.52 for 16 active reps approxLL diffs: (0.01,0.58) iter 46: time=382.55 for 14 active reps approxLL diffs: (0.01,0.88) iter 47: time=388.88 for 14 active reps approxLL diffs: (0.01,0.42) iter 48: time=361.30 for 13 active reps approxLL diffs: (0.01,0.43) iter 49: time=359.73 for 13 active reps approxLL diffs: (0.01,0.46) iter 50: time=374.75 for 13 active reps approxLL diffs: (0.01,0.39) iter 51: time=356.69 for 12 active reps approxLL diffs: (0.01,0.48) iter 52: time=340.38 for 10 active reps approxLL diffs: (0.01,1.86) iter 53: time=344.62 for 10 active reps approxLL diffs: (0.01,2.93) iter 54: time=351.98 for 10 active reps approxLL diffs: (0.01,1.05) iter 55: time=345.12 for 10 active reps approxLL diffs: (0.01,0.35) iter 56: time=280.20 for 8 active reps approxLL diffs: (0.01,0.23) iter 57: time=293.78 for 7 active reps approxLL diffs: (0.01,0.35) iter 58: time=297.85 for 7 active reps approxLL diffs: (0.01,0.39) iter 59: time=275.69 for 6 active reps approxLL diffs: (0.03,0.22) iter 60: time=340.72 for 6 active reps approxLL diffs: (0.02,0.42) iter 61: time=308.47 for 6 active reps approxLL diffs: (0.01,0.89) iter 62: time=303.53 for 6 active reps approxLL diffs: (0.01,0.60) iter 63: time=285.06 for 5 active reps approxLL diffs: (0.02,0.20) iter 64: time=305.56 for 5 active reps approxLL diffs: (0.02,0.16) iter 65: time=289.54 for 5 active reps approxLL diffs: (0.01,0.41) iter 66: time=289.11 for 5 active reps approxLL diffs: (0.01,0.41) iter 67: time=264.28 for 4 active reps approxLL diffs: (0.01,0.15) iter 68: time=270.48 for 4 active reps approxLL diffs: (0.01,0.10) iter 69: time=290.41 for 3 active reps approxLL diffs: (0.02,0.05) iter 70: time=284.20 for 3 active reps approxLL diffs: (0.01,0.08) iter 71: time=264.45 for 2 active reps approxLL diffs: (0.01,0.14) iter 72: time=225.46 for 1 active reps approxLL diffs: (0.31,0.31) iter 73: time=224.60 for 1 active reps approxLL diffs: (1.15,1.15) iter 74: time=205.01 for 1 active reps approxLL diffs: (2.11,2.11) iter 75: time=212.01 for 1 active reps approxLL diffs: (0.56,0.56) iter 76: time=221.12 for 1 active reps approxLL diffs: (0.31,0.31) iter 77: time=209.59 for 1 active reps approxLL diffs: (0.25,0.25) iter 78: time=206.46 for 1 active reps approxLL diffs: (0.11,0.11) iter 79: time=202.39 for 1 active reps approxLL diffs: (0.06,0.06) iter 80: time=190.21 for 1 active reps approxLL diffs: (0.18,0.18) iter 81: time=181.43 for 1 active reps approxLL diffs: (1.75,1.75) iter 82: time=182.36 for 1 active reps approxLL diffs: (6.69,6.69) iter 83: time=182.66 for 1 active reps approxLL diffs: (1.03,1.03) iter 84: time=181.45 for 1 active reps approxLL diffs: (0.34,0.34) iter 85: time=183.26 for 1 active reps approxLL diffs: (0.39,0.39) iter 86: time=183.36 for 1 active reps approxLL diffs: (0.71,0.71) iter 87: time=185.15 for 1 active reps approxLL diffs: (0.57,0.57) iter 88: time=183.34 for 1 active reps approxLL diffs: (0.15,0.15) iter 89: time=185.68 for 1 active reps approxLL diffs: (0.08,0.08) iter 90: time=182.52 for 1 active reps approxLL diffs: (0.11,0.11) iter 91: time=181.77 for 1 active reps approxLL diffs: (0.22,0.22) iter 92: time=182.22 for 1 active reps approxLL diffs: (0.43,0.43) iter 93: time=182.24 for 1 active reps approxLL diffs: (0.63,0.63) iter 94: time=181.02 for 1 active reps approxLL diffs: (0.46,0.46) iter 95: time=182.35 for 1 active reps approxLL diffs: (0.19,0.19) iter 96: time=182.37 for 1 active reps approxLL diffs: (0.07,0.07) iter 97: time=188.06 for 1 active reps approxLL diffs: (0.03,0.03) iter 98: time=187.71 for 1 active reps approxLL diffs: (0.02,0.02) iter 99: time=182.13 for 1 active reps approxLL diffs: (0.01,0.01) iter 100: time=181.59 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 100: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 73.0%, memory/overhead = 27.0% Computing predictions on left-out cross-validation fold Time for computing predictions = 7920.58 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.05: 0.183121 f2=0.5, p=0.02: 0.182454 f2=0.3, p=0.02: 0.181530 ... f2=0.5, p=0.5: 0.136915 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.964013 Absolute prediction MSE using standard LMM: 0.832025 Absolute prediction MSE, fold-best f2=0.3, p=0.05: 0.787481 Absolute pred MSE using f2=0.5, p=0.5: 0.832025 Absolute pred MSE using f2=0.5, p=0.2: 0.815943 Absolute pred MSE using f2=0.5, p=0.1: 0.802104 Absolute pred MSE using f2=0.5, p=0.05: 0.792233 Absolute pred MSE using f2=0.5, p=0.02: 0.788125 Absolute pred MSE using f2=0.5, p=0.01: 0.789432 Absolute pred MSE using f2=0.3, p=0.5: 0.826381 Absolute pred MSE using f2=0.3, p=0.2: 0.805679 Absolute pred MSE using f2=0.3, p=0.1: 0.793150 Absolute pred MSE using f2=0.3, p=0.05: 0.787481 Absolute pred MSE using f2=0.3, p=0.02: 0.789015 Absolute pred MSE using f2=0.3, p=0.01: 0.791559 Absolute pred MSE using f2=0.1, p=0.5: 0.818471 Absolute pred MSE using f2=0.1, p=0.2: 0.797647 Absolute pred MSE using f2=0.1, p=0.1: 0.789087 Absolute pred MSE using f2=0.1, p=0.05: 0.789681 Absolute pred MSE using f2=0.1, p=0.02: 0.796053 Absolute pred MSE using f2=0.1, p=0.01: 0.799279 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.137 Relative improvement in prediction MSE using non-inf model: 0.054 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.05 Time for estimating mixture parameters = 45633.8 sec === Computing Bayesian mixed model assoc stats with mixture prior === Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Beginning variational Bayes iter 1: time=706.97 for 23 active reps iter 2: time=489.26 for 23 active reps approxLL diffs: (17936.12,20529.39) iter 3: time=489.10 for 23 active reps approxLL diffs: (3102.33,3721.04) iter 4: time=492.70 for 23 active reps approxLL diffs: (984.77,1235.26) iter 5: time=494.06 for 23 active reps approxLL diffs: (407.04,559.38) iter 6: time=491.47 for 23 active reps approxLL diffs: (205.57,301.13) iter 7: time=488.88 for 23 active reps approxLL diffs: (114.25,179.64) iter 8: time=491.13 for 23 active reps approxLL diffs: (78.88,126.24) iter 9: time=487.65 for 23 active reps approxLL diffs: (51.75,87.36) iter 10: time=488.38 for 23 active reps approxLL diffs: (35.35,59.70) iter 11: time=498.19 for 23 active reps approxLL diffs: (28.66,44.86) iter 12: time=497.72 for 23 active reps approxLL diffs: (24.08,33.07) iter 13: time=496.04 for 23 active reps approxLL diffs: (19.87,26.46) iter 14: time=493.88 for 23 active reps approxLL diffs: (14.41,20.41) iter 15: time=497.16 for 23 active reps approxLL diffs: (8.75,16.68) iter 16: time=501.89 for 23 active reps approxLL diffs: (5.62,14.09) iter 17: time=491.65 for 23 active reps approxLL diffs: (4.73,11.66) iter 18: time=491.23 for 23 active reps approxLL diffs: (4.22,10.12) iter 19: time=502.31 for 23 active reps approxLL diffs: (3.99,8.90) iter 20: time=503.03 for 23 active reps approxLL diffs: (3.43,7.68) iter 21: time=501.06 for 23 active reps approxLL diffs: (3.22,6.78) iter 22: time=501.18 for 23 active reps approxLL diffs: (2.70,5.81) iter 23: time=517.05 for 23 active reps approxLL diffs: (2.10,5.36) iter 24: time=555.07 for 23 active reps approxLL diffs: (1.79,6.36) iter 25: time=581.21 for 23 active reps approxLL diffs: (1.46,3.83) iter 26: time=577.90 for 23 active reps approxLL diffs: (1.03,3.53) iter 27: time=565.78 for 23 active reps approxLL diffs: (0.79,3.05) iter 28: time=560.75 for 23 active reps approxLL diffs: (0.83,2.35) iter 29: time=545.33 for 23 active reps approxLL diffs: (0.86,2.27) iter 30: time=551.17 for 23 active reps approxLL diffs: (0.77,2.75) iter 31: time=601.89 for 23 active reps approxLL diffs: (0.55,2.37) iter 32: time=563.79 for 23 active reps approxLL diffs: (0.41,1.39) iter 33: time=593.45 for 23 active reps approxLL diffs: (0.30,1.15) iter 34: time=578.90 for 23 active reps approxLL diffs: (0.25,1.31) iter 35: time=594.08 for 23 active reps approxLL diffs: (0.19,0.99) iter 36: time=566.80 for 23 active reps approxLL diffs: (0.14,1.29) iter 37: time=587.27 for 23 active reps approxLL diffs: (0.12,2.49) iter 38: time=556.15 for 23 active reps approxLL diffs: (0.10,2.19) iter 39: time=566.26 for 23 active reps approxLL diffs: (0.09,1.86) iter 40: time=536.75 for 23 active reps approxLL diffs: (0.09,1.19) iter 41: time=509.55 for 23 active reps approxLL diffs: (0.07,0.82) iter 42: time=508.80 for 23 active reps approxLL diffs: (0.05,0.57) iter 43: time=532.97 for 23 active reps approxLL diffs: (0.05,1.13) iter 44: time=544.37 for 23 active reps approxLL diffs: (0.04,0.85) iter 45: time=541.68 for 23 active reps approxLL diffs: (0.04,1.31) iter 46: time=549.18 for 23 active reps approxLL diffs: (0.03,1.67) iter 47: time=507.76 for 23 active reps approxLL diffs: (0.02,0.97) iter 48: time=502.95 for 23 active reps approxLL diffs: (0.02,0.75) iter 49: time=497.95 for 23 active reps approxLL diffs: (0.02,0.55) iter 50: time=496.54 for 23 active reps approxLL diffs: (0.02,0.42) iter 51: time=494.35 for 23 active reps approxLL diffs: (0.01,0.70) iter 52: time=496.91 for 23 active reps approxLL diffs: (0.01,0.63) iter 53: time=498.86 for 23 active reps approxLL diffs: (0.01,0.58) iter 54: time=478.27 for 22 active reps approxLL diffs: (0.01,0.36) iter 55: time=460.46 for 21 active reps approxLL diffs: (0.02,0.36) iter 56: time=462.28 for 21 active reps approxLL diffs: (0.02,0.50) iter 57: time=461.22 for 21 active reps approxLL diffs: (0.01,1.49) iter 58: time=458.91 for 21 active reps approxLL diffs: (0.01,1.20) iter 59: time=457.53 for 19 active reps approxLL diffs: (0.01,0.74) iter 60: time=428.39 for 18 active reps approxLL diffs: (0.01,0.71) iter 61: time=410.86 for 17 active reps approxLL diffs: (0.01,0.94) iter 62: time=411.01 for 17 active reps approxLL diffs: (0.01,1.44) iter 63: time=412.95 for 17 active reps approxLL diffs: (0.01,1.51) iter 64: time=385.77 for 16 active reps approxLL diffs: (0.01,1.05) iter 65: time=399.53 for 15 active reps approxLL diffs: (0.01,0.97) iter 66: time=360.25 for 13 active reps approxLL diffs: (0.02,1.08) iter 67: time=368.45 for 13 active reps approxLL diffs: (0.02,0.47) iter 68: time=365.24 for 13 active reps approxLL diffs: (0.02,0.44) iter 69: time=370.41 for 13 active reps approxLL diffs: (0.01,0.56) iter 70: time=346.74 for 12 active reps approxLL diffs: (0.00,0.57) iter 71: time=337.77 for 10 active reps approxLL diffs: (0.01,0.99) iter 72: time=323.26 for 9 active reps approxLL diffs: (0.01,1.27) iter 73: time=300.86 for 8 active reps approxLL diffs: (0.01,0.41) iter 74: time=309.82 for 7 active reps approxLL diffs: (0.01,0.76) iter 75: time=317.66 for 7 active reps approxLL diffs: (0.01,0.64) iter 76: time=304.02 for 6 active reps approxLL diffs: (0.01,0.28) iter 77: time=273.27 for 5 active reps approxLL diffs: (0.01,0.07) iter 78: time=265.77 for 5 active reps approxLL diffs: (0.01,0.05) iter 79: time=262.48 for 3 active reps approxLL diffs: (0.02,0.03) iter 80: time=274.90 for 3 active reps approxLL diffs: (0.01,0.07) iter 81: time=268.81 for 3 active reps approxLL diffs: (0.01,0.24) iter 82: time=246.01 for 2 active reps approxLL diffs: (0.01,0.99) iter 83: time=195.91 for 1 active reps approxLL diffs: (1.40,1.40) iter 84: time=193.80 for 1 active reps approxLL diffs: (0.47,0.47) iter 85: time=193.80 for 1 active reps approxLL diffs: (0.07,0.07) iter 86: time=192.07 for 1 active reps approxLL diffs: (0.03,0.03) iter 87: time=191.90 for 1 active reps approxLL diffs: (0.06,0.06) iter 88: time=192.78 for 1 active reps approxLL diffs: (0.13,0.13) iter 89: time=185.58 for 1 active reps approxLL diffs: (0.29,0.29) iter 90: time=193.28 for 1 active reps approxLL diffs: (0.35,0.35) iter 91: time=200.35 for 1 active reps approxLL diffs: (0.15,0.15) iter 92: time=198.46 for 1 active reps approxLL diffs: (0.05,0.05) iter 93: time=199.52 for 1 active reps approxLL diffs: (0.04,0.04) iter 94: time=198.88 for 1 active reps approxLL diffs: (0.04,0.04) iter 95: time=199.19 for 1 active reps approxLL diffs: (0.04,0.04) iter 96: time=198.56 for 1 active reps approxLL diffs: (0.03,0.03) iter 97: time=198.89 for 1 active reps approxLL diffs: (0.02,0.02) iter 98: time=199.59 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 98: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.7%, memory/overhead = 22.3% 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 > 445.2) # of SNPs remaining after outlier window removal: 574191/579570 Intercept of LD Score regression for ref stats: 1.201 (0.021) Estimated attenuation: 0.159 (0.017) Intercept of LD Score regression for cur stats: 1.199 (0.022) Calibration factor (ref/cur) to multiply by: 1.001 (0.001) Time for computing Bayesian mixed model assoc stats = 42037.3 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=435.93 for 1 active reps iter 2: time=195.45 for 1 active reps approxLL diffs: (20724.68,20724.68) iter 3: time=192.75 for 1 active reps approxLL diffs: (3723.59,3723.59) iter 4: time=189.54 for 1 active reps approxLL diffs: (1231.54,1231.54) iter 5: time=193.67 for 1 active reps approxLL diffs: (554.25,554.25) iter 6: time=197.23 for 1 active reps approxLL diffs: (295.79,295.79) iter 7: time=197.31 for 1 active reps approxLL diffs: (175.21,175.21) iter 8: time=198.21 for 1 active reps approxLL diffs: (118.04,118.04) iter 9: time=195.94 for 1 active reps approxLL diffs: (79.21,79.21) iter 10: time=197.35 for 1 active reps approxLL diffs: (58.13,58.13) iter 11: time=199.59 for 1 active reps approxLL diffs: (41.24,41.24) iter 12: time=197.34 for 1 active reps approxLL diffs: (28.93,28.93) iter 13: time=198.67 for 1 active reps approxLL diffs: (23.28,23.28) iter 14: time=193.71 for 1 active reps approxLL diffs: (18.21,18.21) iter 15: time=195.71 for 1 active reps approxLL diffs: (15.62,15.62) iter 16: time=195.34 for 1 active reps approxLL diffs: (12.96,12.96) iter 17: time=184.98 for 1 active reps approxLL diffs: (10.31,10.31) iter 18: time=189.32 for 1 active reps approxLL diffs: (8.56,8.56) iter 19: time=190.49 for 1 active reps approxLL diffs: (7.19,7.19) iter 20: time=185.65 for 1 active reps approxLL diffs: (5.39,5.39) iter 21: time=187.39 for 1 active reps approxLL diffs: (3.90,3.90) iter 22: time=195.90 for 1 active reps approxLL diffs: (3.44,3.44) iter 23: time=195.92 for 1 active reps approxLL diffs: (2.48,2.48) iter 24: time=196.10 for 1 active reps approxLL diffs: (2.03,2.03) iter 25: time=196.40 for 1 active reps approxLL diffs: (2.29,2.29) iter 26: time=196.10 for 1 active reps approxLL diffs: (2.14,2.14) iter 27: time=192.03 for 1 active reps approxLL diffs: (1.95,1.95) iter 28: time=193.74 for 1 active reps approxLL diffs: (1.55,1.55) iter 29: time=194.31 for 1 active reps approxLL diffs: (1.18,1.18) iter 30: time=191.91 for 1 active reps approxLL diffs: (0.97,0.97) iter 31: time=191.86 for 1 active reps approxLL diffs: (0.77,0.77) iter 32: time=190.51 for 1 active reps approxLL diffs: (0.73,0.73) iter 33: time=192.98 for 1 active reps approxLL diffs: (0.78,0.78) iter 34: time=192.41 for 1 active reps approxLL diffs: (0.67,0.67) iter 35: time=190.08 for 1 active reps approxLL diffs: (0.48,0.48) iter 36: time=186.58 for 1 active reps approxLL diffs: (0.34,0.34) iter 37: time=186.03 for 1 active reps approxLL diffs: (0.25,0.25) iter 38: time=183.98 for 1 active reps approxLL diffs: (0.19,0.19) iter 39: time=180.27 for 1 active reps approxLL diffs: (0.17,0.17) iter 40: time=181.62 for 1 active reps approxLL diffs: (0.16,0.16) iter 41: time=180.08 for 1 active reps approxLL diffs: (0.17,0.17) iter 42: time=181.01 for 1 active reps approxLL diffs: (0.17,0.17) iter 43: time=180.58 for 1 active reps approxLL diffs: (0.12,0.12) iter 44: time=181.02 for 1 active reps approxLL diffs: (0.07,0.07) iter 45: time=183.06 for 1 active reps approxLL diffs: (0.04,0.04) iter 46: time=181.16 for 1 active reps approxLL diffs: (0.03,0.03) iter 47: time=180.23 for 1 active reps approxLL diffs: (0.02,0.02) iter 48: time=182.06 for 1 active reps approxLL diffs: (0.02,0.02) iter 49: time=179.57 for 1 active reps approxLL diffs: (0.02,0.02) iter 50: time=186.47 for 1 active reps approxLL diffs: (0.02,0.02) iter 51: time=184.86 for 1 active reps approxLL diffs: (0.02,0.02) iter 52: time=184.70 for 1 active reps approxLL diffs: (0.02,0.02) iter 53: time=179.12 for 1 active reps approxLL diffs: (0.02,0.02) iter 54: time=195.13 for 1 active reps approxLL diffs: (0.03,0.03) iter 55: time=195.32 for 1 active reps approxLL diffs: (0.05,0.05) iter 56: time=191.35 for 1 active reps approxLL diffs: (0.12,0.12) iter 57: time=185.82 for 1 active reps approxLL diffs: (0.44,0.44) iter 58: time=180.76 for 1 active reps approxLL diffs: (1.09,1.09) iter 59: time=182.36 for 1 active reps approxLL diffs: (0.67,0.67) iter 60: time=183.66 for 1 active reps approxLL diffs: (0.22,0.22) iter 61: time=185.55 for 1 active reps approxLL diffs: (0.21,0.21) iter 62: time=186.06 for 1 active reps approxLL diffs: (0.23,0.23) iter 63: time=180.68 for 1 active reps approxLL diffs: (0.18,0.18) iter 64: time=180.84 for 1 active reps approxLL diffs: (0.10,0.10) iter 65: time=183.30 for 1 active reps approxLL diffs: (0.08,0.08) iter 66: time=184.12 for 1 active reps approxLL diffs: (0.09,0.09) iter 67: time=180.00 for 1 active reps approxLL diffs: (0.09,0.09) iter 68: time=180.19 for 1 active reps approxLL diffs: (0.07,0.07) iter 69: time=178.77 for 1 active reps approxLL diffs: (0.05,0.05) iter 70: time=180.77 for 1 active reps approxLL diffs: (0.03,0.03) iter 71: time=185.33 for 1 active reps approxLL diffs: (0.01,0.01) iter 72: time=184.75 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 72: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 53.5%, memory/overhead = 46.5% Time for computing and writing betas = 13813.6 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.30896 (705851 good SNPs) lambdaGC: 1.52235 Mean BOLT_LMM_INF: 2.47163 (705851 good SNPs) lambdaGC: 1.54012 Mean BOLT_LMM: 2.52623 (705851 good SNPs) lambdaGC: 1.54595 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 6602.53 sec Total elapsed time for analysis = 153442 sec