+-----------------------------+ | ___ | | 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_PLATELET_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_PLATELET_DISTRIB_WIDTH.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_PLATELET_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 = 3750.7 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: 444656 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 = 444656 Singular values of covariate matrix: S[0] = 2.27087e+06 S[1] = 4764.64 S[2] = 469.777 S[3] = 293.545 S[4] = 199.894 S[5] = 194.416 S[6] = 183.624 S[7] = 175.075 S[8] = 170.795 S[9] = 165.487 S[10] = 162.758 S[11] = 153.765 S[12] = 145.765 S[13] = 143.696 S[14] = 141.118 S[15] = 135.834 S[16] = 132.475 S[17] = 130.181 S[18] = 126.191 S[19] = 116.291 S[20] = 112.09 S[21] = 99.7989 S[22] = 44.8494 S[23] = 23.9381 S[24] = 19.3142 S[25] = 0.983994 S[26] = 0.983796 S[27] = 0.983629 S[28] = 0.983485 S[29] = 0.983403 S[30] = 0.983204 S[31] = 0.983142 S[32] = 0.983109 S[33] = 0.983002 S[34] = 0.98291 S[35] = 0.982639 S[36] = 0.982597 S[37] = 0.98255 S[38] = 0.98237 S[39] = 0.982149 S[40] = 0.982039 S[41] = 0.981892 S[42] = 0.981762 S[43] = 0.96311 S[44] = 0.888288 S[45] = 6.1381e-12 S[46] = 5.70237e-13 S[47] = 3.48959e-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: 442066.294610 Dimension of all-1s proj space (Nused-1): 444655 Time for covariate data setup + Bolt initialization = 4214.41 sec Phenotype 1: N = 444656 mean = 0.00344202 std = 0.993739 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 440.772 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 444656) 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=332.73 rNorms/orig: (0.6,0.7) res2s: 719019..141905 iter 2: time=335.58 rNorms/orig: (0.6,0.6) res2s: 847854..182822 iter 3: time=304.87 rNorms/orig: (0.3,0.3) res2s: 987223..217056 iter 4: time=305.74 rNorms/orig: (0.2,0.2) res2s: 1.03717e+06..232067 iter 5: time=311.51 rNorms/orig: (0.1,0.2) res2s: 1.06356e+06..238890 iter 6: time=312.06 rNorms/orig: (0.09,0.1) res2s: 1.07629e+06..242561 iter 7: time=316.40 rNorms/orig: (0.06,0.06) res2s: 1.08222e+06..244425 iter 8: time=338.42 rNorms/orig: (0.04,0.04) res2s: 1.08508e+06..245276 iter 9: time=350.01 rNorms/orig: (0.02,0.03) res2s: 1.08641e+06..245677 iter 10: time=377.21 rNorms/orig: (0.02,0.02) res2s: 1.08708e+06..245870 iter 11: time=365.80 rNorms/orig: (0.01,0.01) res2s: 1.0874e+06..245958 iter 12: time=374.27 rNorms/orig: (0.007,0.007) res2s: 1.08754e+06..245996 iter 13: time=366.56 rNorms/orig: (0.004,0.005) res2s: 1.0876e+06..246012 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 49.4%, memory/overhead = 50.6% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.144953 Estimating MC scaling f_REML at log(delta) = -0.00573988, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=327.87 rNorms/orig: (1,1) res2s: 75890.2..40957.2 iter 2: time=345.61 rNorms/orig: (1,1) res2s: 108261..64551 iter 3: time=325.80 rNorms/orig: (0.8,0.9) res2s: 164649..96214.7 iter 4: time=323.79 rNorms/orig: (0.6,0.7) res2s: 202658..118168 iter 5: time=336.51 rNorms/orig: (0.5,0.5) res2s: 231209..132673 iter 6: time=347.29 rNorms/orig: (0.4,0.4) res2s: 251908..143800 iter 7: time=331.94 rNorms/orig: (0.3,0.3) res2s: 266190..151903 iter 8: time=345.80 rNorms/orig: (0.2,0.3) res2s: 276143..157185 iter 9: time=325.65 rNorms/orig: (0.2,0.2) res2s: 282793..160743 iter 10: time=338.80 rNorms/orig: (0.1,0.2) res2s: 287638..163195 iter 11: time=354.36 rNorms/orig: (0.1,0.1) res2s: 290838..164790 iter 12: time=355.70 rNorms/orig: (0.09,0.09) res2s: 292936..165805 iter 13: time=377.03 rNorms/orig: (0.07,0.07) res2s: 294135..166419 iter 14: time=354.46 rNorms/orig: (0.05,0.06) res2s: 294998..166845 iter 15: time=348.42 rNorms/orig: (0.04,0.04) res2s: 295546..167117 iter 16: time=334.67 rNorms/orig: (0.03,0.03) res2s: 295889..167279 iter 17: time=357.41 rNorms/orig: (0.02,0.03) res2s: 296087..167398 iter 18: time=372.98 rNorms/orig: (0.02,0.02) res2s: 296236..167468 iter 19: time=406.43 rNorms/orig: (0.01,0.02) res2s: 296333..167512 iter 20: time=335.04 rNorms/orig: (0.01,0.01) res2s: 296386..167540 iter 21: time=315.64 rNorms/orig: (0.009,0.009) res2s: 296418..167558 iter 22: time=293.50 rNorms/orig: (0.007,0.007) res2s: 296440..167569 iter 23: time=318.91 rNorms/orig: (0.005,0.005) res2s: 296453..167576 iter 24: time=324.61 rNorms/orig: (0.004,0.004) res2s: 296462..167580 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 49.4%, memory/overhead = 50.6% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.192063 Estimating MC scaling f_REML at log(delta) = 0.620351, h2 = 0.348397... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=314.30 rNorms/orig: (0.8,0.9) res2s: 298728..89362.1 iter 2: time=312.19 rNorms/orig: (0.8,0.9) res2s: 383155..125623 iter 3: time=328.40 rNorms/orig: (0.5,0.6) res2s: 497289..163361 iter 4: time=349.69 rNorms/orig: (0.3,0.4) res2s: 551510..183774 iter 5: time=376.97 rNorms/orig: (0.3,0.3) res2s: 584817..194820 iter 6: time=383.04 rNorms/orig: (0.2,0.2) res2s: 604237..201859 iter 7: time=377.23 rNorms/orig: (0.1,0.1) res2s: 615105..206113 iter 8: time=382.35 rNorms/orig: (0.09,0.1) res2s: 621362..208423 iter 9: time=387.75 rNorms/orig: (0.07,0.08) res2s: 624817..209721 iter 10: time=395.54 rNorms/orig: (0.05,0.05) res2s: 626918..210468 iter 11: time=393.37 rNorms/orig: (0.03,0.04) res2s: 628076..210872 iter 12: time=403.23 rNorms/orig: (0.02,0.03) res2s: 628704..211085 iter 13: time=394.98 rNorms/orig: (0.02,0.02) res2s: 629000..211193 iter 14: time=374.37 rNorms/orig: (0.01,0.01) res2s: 629181..211256 iter 15: time=308.74 rNorms/orig: (0.008,0.009) res2s: 629277..211289 iter 16: time=316.05 rNorms/orig: (0.006,0.007) res2s: 629326..211306 iter 17: time=337.91 rNorms/orig: (0.004,0.005) res2s: 629350..211316 Converged at iter 17: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 48.9%, memory/overhead = 51.1% MCscaling: logDelta = 0.62, h2 = 0.348, f = 0.00283287 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.348 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.336651, logDelta = 0.620351, f = 0.00283287 Time for fitting variance components = 19427.1 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=670.04 rNorms/orig: (0.6,0.9) res2s: 92136..183376 iter 2: time=665.33 rNorms/orig: (0.6,0.9) res2s: 128894..225249 iter 3: time=674.71 rNorms/orig: (0.3,0.6) res2s: 167752..248038 iter 4: time=685.12 rNorms/orig: (0.3,0.4) res2s: 190190..258947 iter 5: time=715.38 rNorms/orig: (0.2,0.3) res2s: 202207..266675 iter 6: time=740.19 rNorms/orig: (0.1,0.2) res2s: 209844..270720 iter 7: time=751.43 rNorms/orig: (0.09,0.2) res2s: 214782..273162 iter 8: time=753.86 rNorms/orig: (0.07,0.1) res2s: 217443..274639 iter 9: time=713.04 rNorms/orig: (0.05,0.09) res2s: 218954..275395 iter 10: time=693.38 rNorms/orig: (0.03,0.05) res2s: 219906..275839 iter 11: time=711.74 rNorms/orig: (0.02,0.04) res2s: 220395..276081 iter 12: time=696.07 rNorms/orig: (0.01,0.03) res2s: 220666..276216 iter 13: time=685.18 rNorms/orig: (0.009,0.02) res2s: 220814..276284 iter 14: time=648.05 rNorms/orig: (0.007,0.02) res2s: 220897..276326 iter 15: time=656.67 rNorms/orig: (0.005,0.01) res2s: 220944..276345 iter 16: time=653.80 rNorms/orig: (0.003,0.008) res2s: 220968..276356 iter 17: time=670.63 rNorms/orig: (0.002,0.006) res2s: 220983..276362 iter 18: time=657.81 rNorms/orig: (0.001,0.004) res2s: 220990..276365 iter 19: time=663.68 rNorms/orig: (0.0009,0.003) res2s: 220995..276367 iter 20: time=664.26 rNorms/orig: (0.0006,0.002) res2s: 220997..276368 iter 21: time=649.12 rNorms/orig: (0.0004,0.001) res2s: 220998..276368 iter 22: time=654.07 rNorms/orig: (0.0003,0.001) res2s: 220999..276369 iter 23: time=668.73 rNorms/orig: (0.0002,0.0008) res2s: 220999..276369 iter 24: time=665.56 rNorms/orig: (0.0001,0.0006) res2s: 220999..276369 iter 25: time=649.94 rNorms/orig: (9e-05,0.0004) res2s: 220999..276369 Converged at iter 25: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 74.6%, memory/overhead = 25.4% AvgPro: 2.403 AvgRetro: 2.365 Calibration: 1.016 (0.002) (30 SNPs) Ratio of medians: 1.011 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 17481.3 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 18.6638 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 > 444.7) # of SNPs remaining after outlier window removal: 565412/579570 Intercept of LD Score regression for ref stats: 1.158 (0.020) Estimated attenuation: 0.158 (0.021) Intercept of LD Score regression for cur stats: 1.145 (0.017) Calibration factor (ref/cur) to multiply by: 1.011 (0.003) LINREG intercept inflation = 0.989029 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 = 355724 Singular values of covariate matrix: S[0] = 2.03055e+06 S[1] = 4263.83 S[2] = 420.174 S[3] = 262.547 S[4] = 178.872 S[5] = 173.973 S[6] = 164.36 S[7] = 156.725 S[8] = 152.705 S[9] = 147.781 S[10] = 145.523 S[11] = 137.534 S[12] = 130.445 S[13] = 128.561 S[14] = 126.235 S[15] = 121.424 S[16] = 118.442 S[17] = 116.443 S[18] = 112.951 S[19] = 104.032 S[20] = 100.113 S[21] = 89.0818 S[22] = 40.0856 S[23] = 21.1236 S[24] = 17.2697 S[25] = 0.881988 S[26] = 0.880825 S[27] = 0.880516 S[28] = 0.880279 S[29] = 0.880179 S[30] = 0.87986 S[31] = 0.879557 S[32] = 0.879335 S[33] = 0.878898 S[34] = 0.878603 S[35] = 0.878483 S[36] = 0.87816 S[37] = 0.877875 S[38] = 0.877468 S[39] = 0.877285 S[40] = 0.876785 S[41] = 0.876341 S[42] = 0.87475 S[43] = 0.859461 S[44] = 0.795139 S[45] = 4.30686e-12 S[46] = 6.31569e-13 S[47] = 1.3943e-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: 353644.541073 Dimension of all-1s proj space (Nused-1): 355723 Beginning variational Bayes iter 1: time=642.00 for 18 active reps iter 2: time=421.28 for 18 active reps approxLL diffs: (15608.11,20416.18) iter 3: time=418.29 for 18 active reps approxLL diffs: (1705.42,3998.06) iter 4: time=441.33 for 18 active reps approxLL diffs: (339.53,1575.70) iter 5: time=421.53 for 18 active reps approxLL diffs: (100.33,848.38) iter 6: time=435.34 for 18 active reps approxLL diffs: (37.79,478.51) iter 7: time=403.53 for 18 active reps approxLL diffs: (17.44,307.88) iter 8: time=406.48 for 18 active reps approxLL diffs: (9.59,207.88) iter 9: time=408.00 for 18 active reps approxLL diffs: (5.87,149.62) iter 10: time=416.43 for 18 active reps approxLL diffs: (3.84,120.81) iter 11: time=425.60 for 18 active reps approxLL diffs: (2.64,104.12) iter 12: time=439.02 for 18 active reps approxLL diffs: (1.92,76.80) iter 13: time=397.79 for 18 active reps approxLL diffs: (1.45,65.02) iter 14: time=393.77 for 18 active reps approxLL diffs: (1.12,45.46) iter 15: time=404.90 for 18 active reps approxLL diffs: (0.87,41.37) iter 16: time=418.39 for 18 active reps approxLL diffs: (0.70,28.80) iter 17: time=423.01 for 18 active reps approxLL diffs: (0.58,26.14) iter 18: time=440.98 for 18 active reps approxLL diffs: (0.48,26.30) iter 19: time=453.30 for 18 active reps approxLL diffs: (0.40,19.48) iter 20: time=443.20 for 18 active reps approxLL diffs: (0.32,16.71) iter 21: time=455.88 for 18 active reps approxLL diffs: (0.26,13.24) iter 22: time=450.51 for 18 active reps approxLL diffs: (0.21,14.29) iter 23: time=445.64 for 18 active reps approxLL diffs: (0.16,10.98) iter 24: time=463.45 for 18 active reps approxLL diffs: (0.13,12.05) iter 25: time=472.58 for 18 active reps approxLL diffs: (0.11,9.62) iter 26: time=447.25 for 18 active reps approxLL diffs: (0.10,8.65) iter 27: time=456.50 for 18 active reps approxLL diffs: (0.08,8.82) iter 28: time=464.54 for 18 active reps approxLL diffs: (0.07,11.15) iter 29: time=461.93 for 18 active reps approxLL diffs: (0.06,15.79) iter 30: time=467.82 for 18 active reps approxLL diffs: (0.05,7.68) iter 31: time=477.49 for 18 active reps approxLL diffs: (0.04,3.14) iter 32: time=458.04 for 18 active reps approxLL diffs: (0.03,4.62) iter 33: time=463.14 for 18 active reps approxLL diffs: (0.03,3.81) iter 34: time=456.88 for 18 active reps approxLL diffs: (0.02,5.19) iter 35: time=458.86 for 18 active reps approxLL diffs: (0.02,6.08) iter 36: time=466.91 for 18 active reps approxLL diffs: (0.02,6.42) iter 37: time=468.56 for 18 active reps approxLL diffs: (0.01,8.60) iter 38: time=461.80 for 18 active reps approxLL diffs: (0.01,4.66) iter 39: time=457.95 for 18 active reps approxLL diffs: (0.01,2.87) iter 40: time=449.00 for 17 active reps approxLL diffs: (0.01,5.15) iter 41: time=449.03 for 17 active reps approxLL diffs: (0.01,2.94) iter 42: time=456.29 for 17 active reps approxLL diffs: (0.01,6.01) iter 43: time=469.36 for 17 active reps approxLL diffs: (0.01,2.56) iter 44: time=445.35 for 16 active reps approxLL diffs: (0.01,2.54) iter 45: time=445.01 for 15 active reps approxLL diffs: (0.01,2.75) iter 46: time=436.19 for 14 active reps approxLL diffs: (0.01,1.89) iter 47: time=437.06 for 14 active reps approxLL diffs: (0.01,3.57) iter 48: time=426.59 for 14 active reps approxLL diffs: (0.01,3.90) iter 49: time=441.74 for 14 active reps approxLL diffs: (0.01,1.97) iter 50: time=425.89 for 13 active reps approxLL diffs: (0.01,4.32) iter 51: time=401.39 for 12 active reps approxLL diffs: (0.01,3.57) iter 52: time=408.12 for 11 active reps approxLL diffs: (0.01,1.72) iter 53: time=415.63 for 11 active reps approxLL diffs: (0.01,1.35) iter 54: time=391.39 for 10 active reps approxLL diffs: (0.01,1.98) iter 55: time=349.29 for 8 active reps approxLL diffs: (0.01,2.53) iter 56: time=366.92 for 7 active reps approxLL diffs: (0.01,1.83) iter 57: time=374.27 for 7 active reps approxLL diffs: (0.00,2.68) iter 58: time=359.43 for 6 active reps approxLL diffs: (0.01,2.62) iter 59: time=371.77 for 6 active reps approxLL diffs: (0.01,1.93) iter 60: time=435.14 for 6 active reps approxLL diffs: (0.01,1.48) iter 61: time=331.88 for 6 active reps approxLL diffs: (0.01,0.92) iter 62: time=348.78 for 6 active reps approxLL diffs: (0.01,0.67) iter 63: time=349.02 for 6 active reps approxLL diffs: (0.01,0.40) iter 64: time=318.78 for 5 active reps approxLL diffs: (0.03,0.30) iter 65: time=329.53 for 5 active reps approxLL diffs: (0.02,0.21) iter 66: time=330.46 for 5 active reps approxLL diffs: (0.02,0.70) iter 67: time=332.30 for 5 active reps approxLL diffs: (0.02,1.37) iter 68: time=335.59 for 5 active reps approxLL diffs: (0.02,0.29) iter 69: time=313.89 for 5 active reps approxLL diffs: (0.02,0.08) iter 70: time=305.95 for 5 active reps approxLL diffs: (0.01,0.27) iter 71: time=251.25 for 4 active reps approxLL diffs: (0.01,0.90) iter 72: time=235.72 for 3 active reps approxLL diffs: (0.01,0.56) iter 73: time=238.92 for 3 active reps approxLL diffs: (0.01,0.09) iter 74: time=224.39 for 2 active reps approxLL diffs: (0.02,0.02) iter 75: time=228.41 for 2 active reps approxLL diffs: (0.01,0.02) iter 76: time=235.37 for 2 active reps approxLL diffs: (0.01,0.02) iter 77: time=215.30 for 1 active reps approxLL diffs: (0.05,0.05) iter 78: time=218.58 for 1 active reps approxLL diffs: (0.14,0.14) iter 79: time=250.60 for 1 active reps approxLL diffs: (0.31,0.31) iter 80: time=256.90 for 1 active reps approxLL diffs: (0.22,0.22) iter 81: time=255.93 for 1 active reps approxLL diffs: (0.05,0.05) iter 82: time=256.06 for 1 active reps approxLL diffs: (0.01,0.01) iter 83: time=257.80 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 83: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 73.7%, memory/overhead = 26.3% Computing predictions on left-out cross-validation fold Time for computing predictions = 8439.12 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.01: 0.245052 f2=0.3, p=0.02: 0.244594 f2=0.1, p=0.02: 0.241798 ... f2=0.5, p=0.5: 0.150880 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.9815 Absolute prediction MSE using standard LMM: 0.833412 Absolute prediction MSE, fold-best f2=0.3, p=0.01: 0.740981 Absolute pred MSE using f2=0.5, p=0.5: 0.833412 Absolute pred MSE using f2=0.5, p=0.2: 0.799402 Absolute pred MSE using f2=0.5, p=0.1: 0.774345 Absolute pred MSE using f2=0.5, p=0.05: 0.756704 Absolute pred MSE using f2=0.5, p=0.02: 0.746538 Absolute pred MSE using f2=0.5, p=0.01: 0.744823 Absolute pred MSE using f2=0.3, p=0.5: 0.820962 Absolute pred MSE using f2=0.3, p=0.2: 0.782040 Absolute pred MSE using f2=0.3, p=0.1: 0.759684 Absolute pred MSE using f2=0.3, p=0.05: 0.747125 Absolute pred MSE using f2=0.3, p=0.02: 0.741431 Absolute pred MSE using f2=0.3, p=0.01: 0.740981 Absolute pred MSE using f2=0.1, p=0.5: 0.806769 Absolute pred MSE using f2=0.1, p=0.2: 0.768585 Absolute pred MSE using f2=0.1, p=0.1: 0.751382 Absolute pred MSE using f2=0.1, p=0.05: 0.745559 Absolute pred MSE using f2=0.1, p=0.02: 0.744175 Absolute pred MSE using f2=0.1, p=0.01: 0.744195 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.151 Relative improvement in prediction MSE using non-inf model: 0.111 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.01 Time for estimating mixture parameters = 44990.9 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=747.60 for 23 active reps iter 2: time=521.74 for 23 active reps approxLL diffs: (20703.19,23728.15) iter 3: time=522.33 for 23 active reps approxLL diffs: (4442.88,5168.86) iter 4: time=521.78 for 23 active reps approxLL diffs: (1770.64,2057.95) iter 5: time=521.60 for 23 active reps approxLL diffs: (871.77,1030.63) iter 6: time=521.77 for 23 active reps approxLL diffs: (510.69,575.13) iter 7: time=523.44 for 23 active reps approxLL diffs: (322.75,392.88) iter 8: time=528.51 for 23 active reps approxLL diffs: (241.31,295.33) iter 9: time=527.01 for 23 active reps approxLL diffs: (185.66,229.65) iter 10: time=520.26 for 23 active reps approxLL diffs: (141.07,174.04) iter 11: time=520.90 for 23 active reps approxLL diffs: (99.23,130.62) iter 12: time=521.74 for 23 active reps approxLL diffs: (75.22,102.52) iter 13: time=473.71 for 23 active reps approxLL diffs: (56.47,72.81) iter 14: time=452.47 for 23 active reps approxLL diffs: (37.41,63.11) iter 15: time=452.91 for 23 active reps approxLL diffs: (29.32,53.27) iter 16: time=452.86 for 23 active reps approxLL diffs: (23.90,47.15) iter 17: time=463.33 for 23 active reps approxLL diffs: (19.88,43.16) iter 18: time=493.29 for 23 active reps approxLL diffs: (16.06,36.65) iter 19: time=505.17 for 23 active reps approxLL diffs: (12.37,30.91) iter 20: time=526.54 for 23 active reps approxLL diffs: (9.23,21.42) iter 21: time=522.80 for 23 active reps approxLL diffs: (7.36,25.71) iter 22: time=596.53 for 23 active reps approxLL diffs: (4.85,17.72) iter 23: time=621.74 for 23 active reps approxLL diffs: (4.44,13.56) iter 24: time=614.75 for 23 active reps approxLL diffs: (4.84,12.92) iter 25: time=622.31 for 23 active reps approxLL diffs: (4.13,16.17) iter 26: time=664.39 for 23 active reps approxLL diffs: (3.20,13.61) iter 27: time=600.05 for 23 active reps approxLL diffs: (2.80,12.66) iter 28: time=561.37 for 23 active reps approxLL diffs: (1.92,13.00) iter 29: time=587.34 for 23 active reps approxLL diffs: (1.80,7.27) iter 30: time=597.73 for 23 active reps approxLL diffs: (1.14,7.05) iter 31: time=582.54 for 23 active reps approxLL diffs: (0.93,10.12) iter 32: time=576.01 for 23 active reps approxLL diffs: (0.59,10.65) iter 33: time=525.12 for 23 active reps approxLL diffs: (0.49,6.00) iter 34: time=503.07 for 23 active reps approxLL diffs: (0.52,12.46) iter 35: time=503.08 for 23 active reps approxLL diffs: (0.46,7.59) iter 36: time=491.20 for 23 active reps approxLL diffs: (0.42,8.18) iter 37: time=506.38 for 23 active reps approxLL diffs: (0.32,8.31) iter 38: time=524.47 for 23 active reps approxLL diffs: (0.49,4.06) iter 39: time=524.05 for 23 active reps approxLL diffs: (0.35,7.90) iter 40: time=535.79 for 23 active reps approxLL diffs: (0.35,12.02) iter 41: time=547.65 for 23 active reps approxLL diffs: (0.14,8.00) iter 42: time=493.80 for 23 active reps approxLL diffs: (0.14,8.82) iter 43: time=541.38 for 23 active reps approxLL diffs: (0.06,11.17) iter 44: time=540.75 for 23 active reps approxLL diffs: (0.06,9.18) iter 45: time=467.97 for 23 active reps approxLL diffs: (0.06,6.70) iter 46: time=446.23 for 23 active reps approxLL diffs: (0.05,3.99) iter 47: time=467.01 for 23 active reps approxLL diffs: (0.03,4.43) iter 48: time=474.90 for 23 active reps approxLL diffs: (0.02,2.31) iter 49: time=475.55 for 23 active reps approxLL diffs: (0.02,3.81) iter 50: time=475.09 for 23 active reps approxLL diffs: (0.01,3.38) iter 51: time=476.74 for 23 active reps approxLL diffs: (0.01,8.55) iter 52: time=480.85 for 23 active reps approxLL diffs: (0.01,7.19) iter 53: time=457.21 for 22 active reps approxLL diffs: (0.01,1.76) iter 54: time=458.44 for 22 active reps approxLL diffs: (0.01,2.57) iter 55: time=435.14 for 21 active reps approxLL diffs: (0.01,5.90) iter 56: time=441.38 for 19 active reps approxLL diffs: (0.01,7.57) iter 57: time=443.89 for 19 active reps approxLL diffs: (0.01,8.34) iter 58: time=424.45 for 18 active reps approxLL diffs: (0.01,8.78) iter 59: time=411.60 for 17 active reps approxLL diffs: (0.01,1.72) iter 60: time=360.52 for 16 active reps approxLL diffs: (0.01,1.23) iter 61: time=348.64 for 14 active reps approxLL diffs: (0.01,2.16) iter 62: time=333.56 for 13 active reps approxLL diffs: (0.01,2.41) iter 63: time=315.54 for 12 active reps approxLL diffs: (0.01,1.46) iter 64: time=326.73 for 11 active reps approxLL diffs: (0.01,2.13) iter 65: time=311.54 for 10 active reps approxLL diffs: (0.01,3.65) iter 66: time=326.39 for 10 active reps approxLL diffs: (0.01,2.04) iter 67: time=340.94 for 10 active reps approxLL diffs: (0.02,9.55) iter 68: time=342.76 for 10 active reps approxLL diffs: (0.01,6.80) iter 69: time=317.77 for 10 active reps approxLL diffs: (0.01,3.97) iter 70: time=300.29 for 9 active reps approxLL diffs: (0.01,2.87) iter 71: time=254.07 for 8 active reps approxLL diffs: (0.01,6.44) iter 72: time=254.69 for 8 active reps approxLL diffs: (0.00,1.04) iter 73: time=261.62 for 6 active reps approxLL diffs: (0.02,1.63) iter 74: time=258.46 for 6 active reps approxLL diffs: (0.02,7.13) iter 75: time=255.53 for 6 active reps approxLL diffs: (0.01,0.90) iter 76: time=258.26 for 6 active reps approxLL diffs: (0.00,0.19) iter 77: time=241.12 for 5 active reps approxLL diffs: (0.01,0.30) iter 78: time=257.57 for 5 active reps approxLL diffs: (0.00,0.29) iter 79: time=252.29 for 4 active reps approxLL diffs: (0.03,0.15) iter 80: time=274.97 for 4 active reps approxLL diffs: (0.04,0.09) iter 81: time=264.46 for 4 active reps approxLL diffs: (0.01,0.17) iter 82: time=276.72 for 3 active reps approxLL diffs: (0.01,0.41) iter 83: time=265.39 for 2 active reps approxLL diffs: (0.04,0.34) iter 84: time=257.99 for 2 active reps approxLL diffs: (0.02,0.10) iter 85: time=267.42 for 2 active reps approxLL diffs: (0.02,0.04) iter 86: time=257.87 for 2 active reps approxLL diffs: (0.01,0.03) iter 87: time=257.33 for 2 active reps approxLL diffs: (0.01,0.04) iter 88: time=204.52 for 1 active reps approxLL diffs: (0.04,0.04) iter 89: time=204.20 for 1 active reps approxLL diffs: (0.03,0.03) iter 90: time=202.26 for 1 active reps approxLL diffs: (0.02,0.02) iter 91: time=216.65 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 91: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.9%, memory/overhead = 22.1% 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 > 444.7) # of SNPs remaining after outlier window removal: 565412/579570 Intercept of LD Score regression for ref stats: 1.158 (0.020) Estimated attenuation: 0.158 (0.021) Intercept of LD Score regression for cur stats: 1.158 (0.022) Calibration factor (ref/cur) to multiply by: 0.999 (0.003) Time for computing Bayesian mixed model assoc stats = 39827.1 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=446.64 for 1 active reps iter 2: time=224.46 for 1 active reps approxLL diffs: (23820.19,23820.19) iter 3: time=241.46 for 1 active reps approxLL diffs: (5192.82,5192.82) iter 4: time=218.40 for 1 active reps approxLL diffs: (2051.41,2051.41) iter 5: time=236.93 for 1 active reps approxLL diffs: (1031.55,1031.55) iter 6: time=246.52 for 1 active reps approxLL diffs: (556.60,556.60) iter 7: time=223.82 for 1 active reps approxLL diffs: (349.89,349.89) iter 8: time=238.74 for 1 active reps approxLL diffs: (281.22,281.22) iter 9: time=236.93 for 1 active reps approxLL diffs: (228.00,228.00) iter 10: time=225.25 for 1 active reps approxLL diffs: (156.26,156.26) iter 11: time=235.78 for 1 active reps approxLL diffs: (115.13,115.13) iter 12: time=244.21 for 1 active reps approxLL diffs: (87.78,87.78) iter 13: time=233.19 for 1 active reps approxLL diffs: (60.31,60.31) iter 14: time=220.75 for 1 active reps approxLL diffs: (50.78,50.78) iter 15: time=233.68 for 1 active reps approxLL diffs: (49.61,49.61) iter 16: time=232.10 for 1 active reps approxLL diffs: (38.27,38.27) iter 17: time=225.01 for 1 active reps approxLL diffs: (41.54,41.54) iter 18: time=244.64 for 1 active reps approxLL diffs: (25.65,25.65) iter 19: time=227.21 for 1 active reps approxLL diffs: (17.88,17.88) iter 20: time=229.27 for 1 active reps approxLL diffs: (12.25,12.25) iter 21: time=263.26 for 1 active reps approxLL diffs: (13.52,13.52) iter 22: time=280.28 for 1 active reps approxLL diffs: (7.31,7.31) iter 23: time=275.74 for 1 active reps approxLL diffs: (8.46,8.46) iter 24: time=275.68 for 1 active reps approxLL diffs: (7.75,7.75) iter 25: time=289.88 for 1 active reps approxLL diffs: (6.62,6.62) iter 26: time=283.11 for 1 active reps approxLL diffs: (10.70,10.70) iter 27: time=280.20 for 1 active reps approxLL diffs: (6.49,6.49) iter 28: time=268.85 for 1 active reps approxLL diffs: (4.35,4.35) iter 29: time=283.11 for 1 active reps approxLL diffs: (7.60,7.60) iter 30: time=279.96 for 1 active reps approxLL diffs: (8.81,8.81) iter 31: time=275.79 for 1 active reps approxLL diffs: (5.48,5.48) iter 32: time=275.56 for 1 active reps approxLL diffs: (1.93,1.93) iter 33: time=274.52 for 1 active reps approxLL diffs: (2.98,2.98) iter 34: time=297.70 for 1 active reps approxLL diffs: (3.72,3.72) iter 35: time=301.56 for 1 active reps approxLL diffs: (2.16,2.16) iter 36: time=288.07 for 1 active reps approxLL diffs: (1.09,1.09) iter 37: time=294.99 for 1 active reps approxLL diffs: (0.78,0.78) iter 38: time=319.97 for 1 active reps approxLL diffs: (1.32,1.32) iter 39: time=320.54 for 1 active reps approxLL diffs: (2.11,2.11) iter 40: time=320.43 for 1 active reps approxLL diffs: (1.48,1.48) iter 41: time=314.64 for 1 active reps approxLL diffs: (2.31,2.31) iter 42: time=309.48 for 1 active reps approxLL diffs: (2.42,2.42) iter 43: time=327.54 for 1 active reps approxLL diffs: (9.44,9.44) iter 44: time=313.03 for 1 active reps approxLL diffs: (3.27,3.27) iter 45: time=312.50 for 1 active reps approxLL diffs: (0.44,0.44) iter 46: time=317.94 for 1 active reps approxLL diffs: (0.60,0.60) iter 47: time=313.78 for 1 active reps approxLL diffs: (0.42,0.42) iter 48: time=331.53 for 1 active reps approxLL diffs: (0.18,0.18) iter 49: time=317.92 for 1 active reps approxLL diffs: (0.12,0.12) iter 50: time=316.26 for 1 active reps approxLL diffs: (0.20,0.20) iter 51: time=324.48 for 1 active reps approxLL diffs: (0.49,0.49) iter 52: time=312.29 for 1 active reps approxLL diffs: (0.90,0.90) iter 53: time=310.71 for 1 active reps approxLL diffs: (0.68,0.68) iter 54: time=314.04 for 1 active reps approxLL diffs: (0.29,0.29) iter 55: time=292.49 for 1 active reps approxLL diffs: (0.26,0.26) iter 56: time=302.13 for 1 active reps approxLL diffs: (0.30,0.30) iter 57: time=281.46 for 1 active reps approxLL diffs: (0.19,0.19) iter 58: time=274.83 for 1 active reps approxLL diffs: (0.18,0.18) iter 59: time=284.05 for 1 active reps approxLL diffs: (0.27,0.27) iter 60: time=272.88 for 1 active reps approxLL diffs: (0.19,0.19) iter 61: time=272.74 for 1 active reps approxLL diffs: (0.11,0.11) iter 62: time=281.22 for 1 active reps approxLL diffs: (0.23,0.23) iter 63: time=286.00 for 1 active reps approxLL diffs: (0.52,0.52) iter 64: time=277.67 for 1 active reps approxLL diffs: (0.45,0.45) iter 65: time=285.92 for 1 active reps approxLL diffs: (0.14,0.14) iter 66: time=275.19 for 1 active reps approxLL diffs: (0.04,0.04) iter 67: time=287.50 for 1 active reps approxLL diffs: (0.03,0.03) iter 68: time=304.12 for 1 active reps approxLL diffs: (0.02,0.02) iter 69: time=274.41 for 1 active reps approxLL diffs: (0.02,0.02) iter 70: time=274.21 for 1 active reps approxLL diffs: (0.02,0.02) iter 71: time=291.05 for 1 active reps approxLL diffs: (0.02,0.02) iter 72: time=273.55 for 1 active reps approxLL diffs: (0.02,0.02) iter 73: time=272.80 for 1 active reps approxLL diffs: (0.02,0.02) iter 74: time=299.15 for 1 active reps approxLL diffs: (0.02,0.02) iter 75: time=287.42 for 1 active reps approxLL diffs: (0.02,0.02) iter 76: time=281.53 for 1 active reps approxLL diffs: (0.02,0.02) iter 77: time=273.10 for 1 active reps approxLL diffs: (0.01,0.01) iter 78: time=275.11 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 78: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 55.9%, memory/overhead = 44.1% Time for computing and writing betas = 21835.3 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.29867 (705851 good SNPs) lambdaGC: 1.37446 Mean BOLT_LMM_INF: 2.49644 (705851 good SNPs) lambdaGC: 1.39698 Mean BOLT_LMM: 2.62596 (705851 good SNPs) lambdaGC: 1.41144 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 7802.12 sec Total elapsed time for analysis = 159788 sec