+-----------------------------+ | ___ | | BOLT-LMM, v2.3.1 /_ / | | December 19, 2017 /_/ | | Po-Ru Loh // | | / | +-----------------------------+ Copyright (C) 2014-2017 Harvard University. Distributed under the GNU GPLv3 open source license. Compiled with USE_SSE: fast aligned memory access Compiled with USE_MKL: Intel Math Kernel Library linear algebra Boost version: 1_58 Command line options: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt \ --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed \ --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim \ --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam \ --allowX \ --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt \ --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe1000.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt \ --maxMissingPerSnp=0.15 \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab \ --phenoCol=bmd_HEEL_TSCOREz \ --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.bmd_HEEL_TSCOREz.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.bmd_HEEL_TSCOREz.stats.gz \ --verboseStats Setting number of threads to 8 fam: /n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam bim(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim bed(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed === Reading genotype data === Total indivs in PLINK data: Nbed = 488377 Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt Removed 976 individual(s) Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt WARNING: Unable to find individual to remove: FID IID WARNING: Unable to find individual to remove: 6023494 6023494 WARNING: Unable to find individual to remove: 6022857 6022857 WARNING: Unable to find individual to remove: 6020026 6020026 WARNING: Unable to find individual to remove: 6017119 6017119 Removed 28074 individual(s) WARNING: 1684 individual(s) not found in data set Total indivs stored in memory: N = 459327 Reading bim file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim Read 63487 snps Reading bim file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim Read 61966 snps Reading bim file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim Read 52300 snps Reading bim file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim Read 47443 snps Reading bim file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim Read 46314 snps Reading bim file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim Read 53695 snps Reading bim file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim Read 42722 snps Reading bim file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim Read 38591 snps Reading bim file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim Read 34310 snps Reading bim file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim Read 38308 snps Reading bim file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim Read 40824 snps Reading bim file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim Read 37302 snps Reading bim file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim Read 26806 snps Reading bim file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim Read 25509 snps Reading bim file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim Read 24467 snps Reading bim file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim Read 28960 snps Reading bim file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim Read 28835 snps Reading bim file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim Read 21962 snps Reading bim file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim Read 26186 snps Reading bim file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim Read 19959 snps Reading bim file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim Read 11342 snps Reading bim file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim Read 12968 snps Reading bim file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim Read 20214 snps Total snps in PLINK data: Mbed = 804470 Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe1000.txt Excluded 69333 SNP(s) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89009265 WARNING: Unable to find SNP to exclude: Affx-89005343 WARNING: Unable to find SNP to exclude: Affx-89020964 WARNING: Unable to find SNP to exclude: Affx-89013736 Excluded 16710 SNP(s) WARNING: 342 SNP(s) not found in data set Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89022776 WARNING: Unable to find SNP to exclude: Affx-89017694 WARNING: Unable to find SNP to exclude: Affx-89018603 WARNING: Unable to find SNP to exclude: Affx-79443721 Excluded 8428 SNP(s) WARNING: 112 SNP(s) not found in data set Breakdown of SNP pre-filtering results: 709999 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 94471 excluded SNPs Filling in genetic map coordinates using reference file: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz Allocating 709999 x 459328/4 bytes to store genotypes Reading genotypes and performing QC filtering on snps and indivs... Reading bed file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed Expecting 7751445265 (+3) bytes for 488377 indivs, 63487 snps Reading bed file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed Expecting 7565738770 (+3) bytes for 488377 indivs, 61966 snps Reading bed file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed Expecting 6385568500 (+3) bytes for 488377 indivs, 52300 snps Reading bed file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed Expecting 5792553085 (+3) bytes for 488377 indivs, 47443 snps Reading bed file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed Expecting 5654707830 (+3) bytes for 488377 indivs, 46314 snps Reading bed file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed Expecting 6555891025 (+3) bytes for 488377 indivs, 53695 snps Reading bed file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed Expecting 5216142590 (+3) bytes for 488377 indivs, 42722 snps Reading bed file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed Expecting 4711768145 (+3) bytes for 488377 indivs, 38591 snps Reading bed file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed Expecting 4189079450 (+3) bytes for 488377 indivs, 34310 snps Reading bed file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed Expecting 4677215260 (+3) bytes for 488377 indivs, 38308 snps Reading bed file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed Expecting 4984406280 (+3) bytes for 488377 indivs, 40824 snps Reading bed file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed Expecting 4554387690 (+3) bytes for 488377 indivs, 37302 snps Reading bed file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed Expecting 3272878570 (+3) bytes for 488377 indivs, 26806 snps Reading bed file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed Expecting 3114521355 (+3) bytes for 488377 indivs, 25509 snps Reading bed file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed Expecting 2987298365 (+3) bytes for 488377 indivs, 24467 snps Reading bed file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed Expecting 3535871200 (+3) bytes for 488377 indivs, 28960 snps Reading bed file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed Expecting 3520609325 (+3) bytes for 488377 indivs, 28835 snps Reading bed file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed Expecting 2681450390 (+3) bytes for 488377 indivs, 21962 snps Reading bed file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed Expecting 3197179670 (+3) bytes for 488377 indivs, 26186 snps Reading bed file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed Expecting 2436894105 (+3) bytes for 488377 indivs, 19959 snps Reading bed file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed Expecting 1384801490 (+3) bytes for 488377 indivs, 11342 snps Reading bed file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed Expecting 1583327960 (+3) bytes for 488377 indivs, 12968 snps Reading bed file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed Expecting 2468028330 (+3) bytes for 488377 indivs, 20214 snps Total indivs after QC: 459327 Total post-QC SNPs: M = 709999 Variance component 1: 709999 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 8485.86 sec === Reading phenotype and covariate data === WARNING: Ignoring indiv not in genotype data: FID=6018780, IID=6018780 WARNING: Ignoring indiv not in genotype data: FID=6012488, IID=6012488 WARNING: Ignoring indiv not in genotype data: FID=5998913, IID=5998913 WARNING: Ignoring indiv not in genotype data: FID=5989416, IID=5989416 WARNING: Ignoring indiv not in genotype data: FID=5985954, IID=5985954 Read data for 460238 indivs (ignored 914 without genotypes) from: /n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz WARNING: Ignoring indiv not in genotype data: FID=1000129, IID=1000129 WARNING: Ignoring indiv not in genotype data: FID=1000170, IID=1000170 WARNING: Ignoring indiv not in genotype data: FID=1000224, IID=1000224 WARNING: Ignoring indiv not in genotype data: FID=1000362, IID=1000362 WARNING: Ignoring indiv not in genotype data: FID=1000379, IID=1000379 Read data for 502655 indivs (ignored 43331 without genotypes) from: /n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab Number of indivs with no missing phenotype(s) to use: 445921 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 = 445921 Singular values of covariate matrix: S[0] = 2.27231e+06 S[1] = 4775.19 S[2] = 470.133 S[3] = 294.157 S[4] = 202.556 S[5] = 196.33 S[6] = 184.663 S[7] = 176.25 S[8] = 170.458 S[9] = 164.969 S[10] = 161.755 S[11] = 152.686 S[12] = 143.827 S[13] = 141.533 S[14] = 139.322 S[15] = 137.09 S[16] = 133.644 S[17] = 131.067 S[18] = 127.995 S[19] = 114.573 S[20] = 111.776 S[21] = 101.002 S[22] = 43.9469 S[23] = 23.7855 S[24] = 19.3602 S[25] = 0.9852 S[26] = 0.985107 S[27] = 0.984792 S[28] = 0.984772 S[29] = 0.984695 S[30] = 0.984644 S[31] = 0.984555 S[32] = 0.984435 S[33] = 0.984372 S[34] = 0.984165 S[35] = 0.984042 S[36] = 0.983985 S[37] = 0.983908 S[38] = 0.983778 S[39] = 0.983747 S[40] = 0.983614 S[41] = 0.983405 S[42] = 0.983243 S[43] = 0.965402 S[44] = 0.888462 S[45] = 6.27155e-12 S[46] = 5.14881e-13 S[47] = 2.25716e-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: 443315.159348 Dimension of all-1s proj space (Nused-1): 445920 Time for covariate data setup + Bolt initialization = 3910.34 sec Phenotype 1: N = 445921 mean = -0.0181462 std = 0.991077 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 429.365 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 445921) Estimating MC scaling f_REML at log(delta) = 1.09285, h2 = 0.25... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=318.86 rNorms/orig: (0.6,0.6) res2s: 735833..134118 iter 2: time=297.47 rNorms/orig: (0.5,0.6) res2s: 892042..178639 iter 3: time=298.40 rNorms/orig: (0.3,0.3) res2s: 1.00687e+06..209794 iter 4: time=299.32 rNorms/orig: (0.2,0.2) res2s: 1.04823e+06..222809 iter 5: time=313.38 rNorms/orig: (0.1,0.1) res2s: 1.0709e+06..229127 iter 6: time=306.79 rNorms/orig: (0.09,0.09) res2s: 1.08216e+06..232047 iter 7: time=309.26 rNorms/orig: (0.05,0.06) res2s: 1.08768e+06..233489 iter 8: time=295.48 rNorms/orig: (0.04,0.04) res2s: 1.09036e+06..234182 iter 9: time=296.17 rNorms/orig: (0.02,0.03) res2s: 1.09166e+06..234461 iter 10: time=291.17 rNorms/orig: (0.01,0.02) res2s: 1.09225e+06..234622 iter 11: time=301.28 rNorms/orig: (0.01,0.01) res2s: 1.09251e+06..234687 iter 12: time=297.21 rNorms/orig: (0.006,0.007) res2s: 1.09264e+06..234717 iter 13: time=318.32 rNorms/orig: (0.004,0.005) res2s: 1.0927e+06..234730 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.3%, memory/overhead = 48.7% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.225168 Estimating MC scaling f_REML at log(delta) = -0.00575995, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=305.51 rNorms/orig: (1,1) res2s: 79312.2..37547.8 iter 2: time=305.16 rNorms/orig: (1,1) res2s: 120036..63194.8 iter 3: time=310.11 rNorms/orig: (0.8,0.9) res2s: 174337..93212 iter 4: time=303.87 rNorms/orig: (0.6,0.6) res2s: 208135..113088 iter 5: time=300.94 rNorms/orig: (0.5,0.5) res2s: 234444..127510 iter 6: time=305.59 rNorms/orig: (0.4,0.4) res2s: 254052..137200 iter 7: time=310.16 rNorms/orig: (0.3,0.3) res2s: 268786..144031 iter 8: time=302.11 rNorms/orig: (0.2,0.3) res2s: 278518..148777 iter 9: time=305.56 rNorms/orig: (0.2,0.2) res2s: 285329..151488 iter 10: time=302.01 rNorms/orig: (0.1,0.2) res2s: 289862..153707 iter 11: time=314.59 rNorms/orig: (0.1,0.1) res2s: 292722..155018 iter 12: time=307.53 rNorms/orig: (0.08,0.09) res2s: 294680..155858 iter 13: time=309.95 rNorms/orig: (0.07,0.07) res2s: 295993..156365 iter 14: time=303.29 rNorms/orig: (0.05,0.06) res2s: 296738..156758 iter 15: time=314.31 rNorms/orig: (0.04,0.04) res2s: 297268..157005 iter 16: time=305.41 rNorms/orig: (0.03,0.03) res2s: 297589..157164 iter 17: time=305.44 rNorms/orig: (0.02,0.03) res2s: 297790..157251 iter 18: time=308.15 rNorms/orig: (0.02,0.02) res2s: 297908..157320 iter 19: time=304.17 rNorms/orig: (0.01,0.02) res2s: 297988..157356 iter 20: time=296.89 rNorms/orig: (0.01,0.01) res2s: 298039..157381 iter 21: time=299.81 rNorms/orig: (0.009,0.009) res2s: 298070..157394 iter 22: time=306.53 rNorms/orig: (0.007,0.007) res2s: 298090..157403 iter 23: time=313.70 rNorms/orig: (0.005,0.005) res2s: 298103..157409 iter 24: time=309.73 rNorms/orig: (0.004,0.004) res2s: 298111..157413 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.3%, memory/overhead = 48.7% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.129235 Estimating MC scaling f_REML at log(delta) = 0.394856, h2 = 0.401164... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=299.55 rNorms/orig: (0.9,0.9) res2s: 193867..63851.1 iter 2: time=313.30 rNorms/orig: (0.9,1) res2s: 272145..98864.5 iter 3: time=316.52 rNorms/orig: (0.6,0.7) res2s: 358241..133400 iter 4: time=318.01 rNorms/orig: (0.4,0.5) res2s: 402232..152966 iter 5: time=304.46 rNorms/orig: (0.3,0.4) res2s: 432505..165400 iter 6: time=311.25 rNorms/orig: (0.2,0.3) res2s: 452228..172802 iter 7: time=319.70 rNorms/orig: (0.2,0.2) res2s: 465130..177462 iter 8: time=326.91 rNorms/orig: (0.1,0.1) res2s: 472810..180349 iter 9: time=303.89 rNorms/orig: (0.1,0.1) res2s: 477590..181829 iter 10: time=305.47 rNorms/orig: (0.07,0.08) res2s: 480410..182918 iter 11: time=309.41 rNorms/orig: (0.05,0.06) res2s: 482006..183492 iter 12: time=304.40 rNorms/orig: (0.04,0.04) res2s: 482978..183822 iter 13: time=302.37 rNorms/orig: (0.03,0.03) res2s: 483560..184002 iter 14: time=314.14 rNorms/orig: (0.02,0.02) res2s: 483856..184128 iter 15: time=321.20 rNorms/orig: (0.02,0.02) res2s: 484045..184198 iter 16: time=327.90 rNorms/orig: (0.01,0.01) res2s: 484146..184239 iter 17: time=350.62 rNorms/orig: (0.008,0.009) res2s: 484202..184259 iter 18: time=402.42 rNorms/orig: (0.006,0.007) res2s: 484234..184273 iter 19: time=389.43 rNorms/orig: (0.004,0.005) res2s: 484254..184279 Converged at iter 19: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.9%, memory/overhead = 49.1% MCscaling: logDelta = 0.39, h2 = 0.401, f = 0.00274672 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.401 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.382071, logDelta = 0.394856, f = 0.00274672 Time for fitting variance components = 18091.6 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=720.56 rNorms/orig: (0.7,2) res2s: 63608.3..148613 iter 2: time=653.84 rNorms/orig: (0.6,1) res2s: 98870.5..176133 iter 3: time=664.81 rNorms/orig: (0.4,0.7) res2s: 135583..216020 iter 4: time=662.04 rNorms/orig: (0.4,0.5) res2s: 156763..228816 iter 5: time=666.29 rNorms/orig: (0.2,0.4) res2s: 170088..240928 iter 6: time=658.07 rNorms/orig: (0.2,0.3) res2s: 178537..247156 iter 7: time=656.99 rNorms/orig: (0.1,0.2) res2s: 183756..251760 iter 8: time=643.36 rNorms/orig: (0.1,0.2) res2s: 187180..254306 iter 9: time=674.35 rNorms/orig: (0.07,0.1) res2s: 188911..255744 iter 10: time=651.01 rNorms/orig: (0.05,0.08) res2s: 190263..256692 iter 11: time=646.47 rNorms/orig: (0.04,0.06) res2s: 190976..257229 iter 12: time=646.46 rNorms/orig: (0.03,0.05) res2s: 191406..257542 iter 13: time=648.66 rNorms/orig: (0.02,0.04) res2s: 191644..257711 iter 14: time=641.46 rNorms/orig: (0.01,0.02) res2s: 191783..257817 iter 15: time=642.03 rNorms/orig: (0.009,0.02) res2s: 191874..257878 iter 16: time=644.66 rNorms/orig: (0.007,0.01) res2s: 191926..257914 iter 17: time=642.91 rNorms/orig: (0.005,0.01) res2s: 191955..257933 iter 18: time=647.16 rNorms/orig: (0.003,0.008) res2s: 191974..257945 iter 19: time=648.51 rNorms/orig: (0.002,0.006) res2s: 191985..257953 iter 20: time=646.91 rNorms/orig: (0.002,0.004) res2s: 191991..257956 iter 21: time=647.27 rNorms/orig: (0.001,0.003) res2s: 191994..257958 iter 22: time=646.19 rNorms/orig: (0.0007,0.002) res2s: 191996..257959 iter 23: time=646.43 rNorms/orig: (0.0005,0.002) res2s: 191997..257960 iter 24: time=645.74 rNorms/orig: (0.0004,0.001) res2s: 191998..257960 iter 25: time=642.11 rNorms/orig: (0.0003,0.0009) res2s: 191998..257960 iter 26: time=645.42 rNorms/orig: (0.0002,0.0007) res2s: 191998..257960 iter 27: time=641.18 rNorms/orig: (0.0001,0.0005) res2s: 191998..257960 Converged at iter 27: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 76.0%, memory/overhead = 24.0% AvgPro: 1.979 AvgRetro: 1.938 Calibration: 1.021 (0.003) (30 SNPs) Ratio of medians: 1.054 Median of ratios: 1.016 Time for computing infinitesimal model assoc stats = 18025.7 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 22.4 sec === Reading LD Scores for calibration of Bayesian assoc stats === Looking up LD Scores... Looking for column header 'SNP': column number = 1 Looking for column header 'LDSCORE': column number = 5 Found LD Scores for 605211/709999 SNPs Estimating inflation of LINREG chisq stats using MLMe as reference... Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 583483/709999 Masking windows around outlier snps (chisq > 445.9) # of SNPs remaining after outlier window removal: 571746/583483 Intercept of LD Score regression for ref stats: 1.265 (0.024) Estimated attenuation: 0.184 (0.015) Intercept of LD Score regression for cur stats: 1.234 (0.019) Calibration factor (ref/cur) to multiply by: 1.025 (0.004) LINREG intercept inflation = 0.975983 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 = 356736 Singular values of covariate matrix: S[0] = 2.03311e+06 S[1] = 4269.33 S[2] = 420.512 S[3] = 263.082 S[4] = 181.158 S[5] = 175.617 S[6] = 165.105 S[7] = 157.651 S[8] = 152.405 S[9] = 147.541 S[10] = 144.721 S[11] = 136.587 S[12] = 128.655 S[13] = 126.68 S[14] = 124.684 S[15] = 122.631 S[16] = 119.436 S[17] = 117.182 S[18] = 114.549 S[19] = 102.384 S[20] = 99.8681 S[21] = 90.4737 S[22] = 39.4452 S[23] = 21.2936 S[24] = 17.2978 S[25] = 0.883952 S[26] = 0.88259 S[27] = 0.882227 S[28] = 0.882046 S[29] = 0.881706 S[30] = 0.881355 S[31] = 0.881049 S[32] = 0.880861 S[33] = 0.880241 S[34] = 0.880205 S[35] = 0.879962 S[36] = 0.87951 S[37] = 0.87936 S[38] = 0.87897 S[39] = 0.878779 S[40] = 0.878203 S[41] = 0.87795 S[42] = 0.874468 S[43] = 0.861821 S[44] = 0.795023 S[45] = 3.69077e-12 S[46] = 4.87035e-13 S[47] = 2.57566e-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: 354642.695734 Dimension of all-1s proj space (Nused-1): 356735 Beginning variational Bayes iter 1: time=643.62 for 18 active reps iter 2: time=421.17 for 18 active reps approxLL diffs: (22041.49,25952.60) iter 3: time=433.89 for 18 active reps approxLL diffs: (2624.92,4864.19) iter 4: time=430.88 for 18 active reps approxLL diffs: (553.73,1803.55) iter 5: time=428.98 for 18 active reps approxLL diffs: (163.82,902.83) iter 6: time=416.79 for 18 active reps approxLL diffs: (58.04,522.73) iter 7: time=423.56 for 18 active reps approxLL diffs: (24.17,324.01) iter 8: time=424.98 for 18 active reps approxLL diffs: (11.70,213.07) iter 9: time=420.31 for 18 active reps approxLL diffs: (6.41,171.68) iter 10: time=423.83 for 18 active reps approxLL diffs: (3.86,134.03) iter 11: time=410.99 for 18 active reps approxLL diffs: (2.52,82.23) iter 12: time=423.05 for 18 active reps approxLL diffs: (1.73,60.82) iter 13: time=420.19 for 18 active reps approxLL diffs: (1.21,54.40) iter 14: time=417.79 for 18 active reps approxLL diffs: (0.87,37.42) iter 15: time=428.91 for 18 active reps approxLL diffs: (0.65,32.24) iter 16: time=420.63 for 18 active reps approxLL diffs: (0.51,23.94) iter 17: time=426.31 for 18 active reps approxLL diffs: (0.41,22.04) iter 18: time=415.82 for 18 active reps approxLL diffs: (0.33,18.75) iter 19: time=415.39 for 18 active reps approxLL diffs: (0.27,18.33) iter 20: time=418.21 for 18 active reps approxLL diffs: (0.21,12.59) iter 21: time=411.72 for 18 active reps approxLL diffs: (0.17,9.98) iter 22: time=418.87 for 18 active reps approxLL diffs: (0.14,7.75) iter 23: time=421.49 for 18 active reps approxLL diffs: (0.12,6.89) iter 24: time=423.93 for 18 active reps approxLL diffs: (0.10,6.60) iter 25: time=418.43 for 18 active reps approxLL diffs: (0.09,5.39) iter 26: time=419.90 for 18 active reps approxLL diffs: (0.07,5.39) iter 27: time=414.08 for 18 active reps approxLL diffs: (0.06,8.86) iter 28: time=432.45 for 18 active reps approxLL diffs: (0.05,8.91) iter 29: time=411.85 for 18 active reps approxLL diffs: (0.05,8.79) iter 30: time=411.12 for 18 active reps approxLL diffs: (0.04,8.06) iter 31: time=414.98 for 18 active reps approxLL diffs: (0.04,6.49) iter 32: time=414.42 for 18 active reps approxLL diffs: (0.03,4.86) iter 33: time=419.40 for 18 active reps approxLL diffs: (0.03,4.83) iter 34: time=413.29 for 18 active reps approxLL diffs: (0.03,5.69) iter 35: time=415.46 for 18 active reps approxLL diffs: (0.02,6.41) iter 36: time=412.74 for 18 active reps approxLL diffs: (0.02,2.66) iter 37: time=412.73 for 18 active reps approxLL diffs: (0.02,2.18) iter 38: time=414.17 for 18 active reps approxLL diffs: (0.02,4.77) iter 39: time=421.50 for 18 active reps approxLL diffs: (0.01,4.45) iter 40: time=423.74 for 18 active reps approxLL diffs: (0.01,4.04) iter 41: time=428.29 for 18 active reps approxLL diffs: (0.01,3.02) iter 42: time=402.53 for 17 active reps approxLL diffs: (0.01,4.63) iter 43: time=413.92 for 17 active reps approxLL diffs: (0.01,2.93) iter 44: time=387.48 for 16 active reps approxLL diffs: (0.02,1.92) iter 45: time=387.79 for 16 active reps approxLL diffs: (0.01,1.51) iter 46: time=399.16 for 16 active reps approxLL diffs: (0.01,1.23) iter 47: time=387.75 for 16 active reps approxLL diffs: (0.01,1.19) iter 48: time=391.50 for 16 active reps approxLL diffs: (0.01,2.99) iter 49: time=406.04 for 15 active reps approxLL diffs: (0.01,2.81) iter 50: time=399.43 for 15 active reps approxLL diffs: (0.01,3.03) iter 51: time=381.40 for 14 active reps approxLL diffs: (0.01,2.61) iter 52: time=391.41 for 14 active reps approxLL diffs: (0.01,2.57) iter 53: time=387.57 for 14 active reps approxLL diffs: (0.01,1.58) iter 54: time=363.87 for 13 active reps approxLL diffs: (0.01,1.04) iter 55: time=367.46 for 13 active reps approxLL diffs: (0.01,1.57) iter 56: time=361.73 for 13 active reps approxLL diffs: (0.01,6.71) iter 57: time=356.64 for 13 active reps approxLL diffs: (0.01,3.04) iter 58: time=350.71 for 11 active reps approxLL diffs: (0.01,0.83) iter 59: time=356.05 for 11 active reps approxLL diffs: (0.01,1.37) iter 60: time=331.37 for 10 active reps approxLL diffs: (0.01,0.95) iter 61: time=330.14 for 10 active reps approxLL diffs: (0.01,0.49) iter 62: time=309.49 for 9 active reps approxLL diffs: (0.01,0.63) iter 63: time=277.77 for 8 active reps approxLL diffs: (0.01,0.76) iter 64: time=273.62 for 8 active reps approxLL diffs: (0.01,0.56) iter 65: time=291.31 for 7 active reps approxLL diffs: (0.04,0.24) iter 66: time=290.27 for 7 active reps approxLL diffs: (0.05,0.51) iter 67: time=302.01 for 7 active reps approxLL diffs: (0.10,0.48) iter 68: time=305.71 for 7 active reps approxLL diffs: (0.13,0.39) iter 69: time=294.00 for 7 active reps approxLL diffs: (0.06,0.22) iter 70: time=300.85 for 7 active reps approxLL diffs: (0.02,0.23) iter 71: time=311.99 for 7 active reps approxLL diffs: (0.00,0.20) iter 72: time=279.61 for 6 active reps approxLL diffs: (0.03,0.20) iter 73: time=275.30 for 6 active reps approxLL diffs: (0.04,0.27) iter 74: time=275.02 for 6 active reps approxLL diffs: (0.06,0.36) iter 75: time=277.75 for 6 active reps approxLL diffs: (0.03,0.27) iter 76: time=314.99 for 6 active reps approxLL diffs: (0.02,0.26) iter 77: time=311.46 for 6 active reps approxLL diffs: (0.01,0.18) iter 78: time=311.25 for 6 active reps approxLL diffs: (0.01,0.10) iter 79: time=278.25 for 5 active reps approxLL diffs: (0.01,0.18) iter 80: time=272.93 for 5 active reps approxLL diffs: (0.01,0.20) iter 81: time=249.40 for 5 active reps approxLL diffs: (0.01,0.10) iter 82: time=258.42 for 5 active reps approxLL diffs: (0.01,0.06) iter 83: time=241.43 for 4 active reps approxLL diffs: (0.00,0.13) iter 84: time=246.14 for 3 active reps approxLL diffs: (0.08,0.37) iter 85: time=258.43 for 3 active reps approxLL diffs: (0.09,1.50) iter 86: time=245.41 for 3 active reps approxLL diffs: (0.11,1.40) iter 87: time=241.95 for 3 active reps approxLL diffs: (0.10,0.43) iter 88: time=239.27 for 3 active reps approxLL diffs: (0.06,0.09) iter 89: time=244.39 for 3 active reps approxLL diffs: (0.03,0.06) iter 90: time=242.40 for 3 active reps approxLL diffs: (0.02,0.16) iter 91: time=240.41 for 3 active reps approxLL diffs: (0.02,0.38) iter 92: time=240.81 for 3 active reps approxLL diffs: (0.02,0.41) iter 93: time=237.58 for 3 active reps approxLL diffs: (0.02,0.15) iter 94: time=234.70 for 3 active reps approxLL diffs: (0.02,0.04) iter 95: time=253.25 for 3 active reps approxLL diffs: (0.01,0.06) iter 96: time=227.03 for 2 active reps approxLL diffs: (0.01,0.10) iter 97: time=239.68 for 2 active reps approxLL diffs: (0.01,0.11) iter 98: time=205.10 for 1 active reps approxLL diffs: (0.09,0.09) iter 99: time=188.34 for 1 active reps approxLL diffs: (0.07,0.07) iter 100: time=198.02 for 1 active reps approxLL diffs: (0.10,0.10) iter 101: time=175.08 for 1 active reps approxLL diffs: (0.17,0.17) iter 102: time=178.42 for 1 active reps approxLL diffs: (0.26,0.26) iter 103: time=188.78 for 1 active reps approxLL diffs: (0.23,0.23) iter 104: time=175.16 for 1 active reps approxLL diffs: (0.12,0.12) iter 105: time=172.13 for 1 active reps approxLL diffs: (0.04,0.04) iter 106: time=178.89 for 1 active reps approxLL diffs: (0.02,0.02) iter 107: time=177.88 for 1 active reps approxLL diffs: (0.01,0.01) iter 108: time=172.95 for 1 active reps approxLL diffs: (0.01,0.01) iter 109: time=175.94 for 1 active reps approxLL diffs: (0.01,0.01) iter 110: time=183.83 for 1 active reps approxLL diffs: (0.02,0.02) iter 111: time=176.11 for 1 active reps approxLL diffs: (0.02,0.02) iter 112: time=180.28 for 1 active reps approxLL diffs: (0.02,0.02) iter 113: time=186.58 for 1 active reps approxLL diffs: (0.04,0.04) iter 114: time=176.11 for 1 active reps approxLL diffs: (0.06,0.06) iter 115: time=172.59 for 1 active reps approxLL diffs: (0.15,0.15) iter 116: time=178.44 for 1 active reps approxLL diffs: (0.31,0.31) iter 117: time=176.14 for 1 active reps approxLL diffs: (0.36,0.36) iter 118: time=178.11 for 1 active reps approxLL diffs: (0.24,0.24) iter 119: time=177.19 for 1 active reps approxLL diffs: (0.12,0.12) iter 120: time=173.95 for 1 active reps approxLL diffs: (0.05,0.05) iter 121: time=172.65 for 1 active reps approxLL diffs: (0.02,0.02) iter 122: time=179.55 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 122: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 75.0%, memory/overhead = 25.0% Computing predictions on left-out cross-validation fold Time for computing predictions = 8139.16 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.05: 0.255040 f2=0.3, p=0.02: 0.253695 f2=0.5, p=0.02: 0.253637 ... f2=0.5, p=0.5: 0.190079 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.983976 Absolute prediction MSE using standard LMM: 0.796942 Absolute prediction MSE, fold-best f2=0.3, p=0.05: 0.733022 Absolute pred MSE using f2=0.5, p=0.5: 0.796942 Absolute pred MSE using f2=0.5, p=0.2: 0.770726 Absolute pred MSE using f2=0.5, p=0.1: 0.750849 Absolute pred MSE using f2=0.5, p=0.05: 0.738680 Absolute pred MSE using f2=0.5, p=0.02: 0.734402 Absolute pred MSE using f2=0.5, p=0.01: 0.735981 Absolute pred MSE using f2=0.3, p=0.5: 0.787396 Absolute pred MSE using f2=0.3, p=0.2: 0.755581 Absolute pred MSE using f2=0.3, p=0.1: 0.739185 Absolute pred MSE using f2=0.3, p=0.05: 0.733022 Absolute pred MSE using f2=0.3, p=0.02: 0.734346 Absolute pred MSE using f2=0.3, p=0.01: 0.736844 Absolute pred MSE using f2=0.1, p=0.5: 0.774673 Absolute pred MSE using f2=0.1, p=0.2: 0.744770 Absolute pred MSE using f2=0.1, p=0.1: 0.736034 Absolute pred MSE using f2=0.1, p=0.05: 0.738551 Absolute pred MSE using f2=0.1, p=0.02: 0.744895 Absolute pred MSE using f2=0.1, p=0.01: 0.747842 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.190 Relative improvement in prediction MSE using non-inf model: 0.080 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.05 Time for estimating mixture parameters = 51182.7 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=701.19 for 23 active reps iter 2: time=467.98 for 23 active reps approxLL diffs: (29138.26,32171.74) iter 3: time=467.09 for 23 active reps approxLL diffs: (5535.10,6352.30) iter 4: time=469.50 for 23 active reps approxLL diffs: (1784.52,2085.54) iter 5: time=476.81 for 23 active reps approxLL diffs: (802.09,920.89) iter 6: time=489.21 for 23 active reps approxLL diffs: (406.80,504.76) iter 7: time=488.67 for 23 active reps approxLL diffs: (240.65,310.89) iter 8: time=486.07 for 23 active reps approxLL diffs: (165.38,205.86) iter 9: time=483.06 for 23 active reps approxLL diffs: (122.76,147.99) iter 10: time=483.25 for 23 active reps approxLL diffs: (88.28,109.38) iter 11: time=480.51 for 23 active reps approxLL diffs: (62.62,86.03) iter 12: time=471.44 for 23 active reps approxLL diffs: (44.68,68.32) iter 13: time=471.76 for 23 active reps approxLL diffs: (34.76,47.65) iter 14: time=475.45 for 23 active reps approxLL diffs: (27.01,34.93) iter 15: time=475.03 for 23 active reps approxLL diffs: (20.46,27.31) iter 16: time=474.57 for 23 active reps approxLL diffs: (16.40,26.30) iter 17: time=474.91 for 23 active reps approxLL diffs: (12.71,22.16) iter 18: time=469.81 for 23 active reps approxLL diffs: (9.47,18.15) iter 19: time=478.30 for 23 active reps approxLL diffs: (7.66,13.31) iter 20: time=470.58 for 23 active reps approxLL diffs: (4.95,12.61) iter 21: time=476.16 for 23 active reps approxLL diffs: (4.30,11.10) iter 22: time=475.66 for 23 active reps approxLL diffs: (4.44,10.35) iter 23: time=473.23 for 23 active reps approxLL diffs: (3.58,7.49) iter 24: time=473.77 for 23 active reps approxLL diffs: (3.41,7.48) iter 25: time=472.44 for 23 active reps approxLL diffs: (3.11,6.97) iter 26: time=469.85 for 23 active reps approxLL diffs: (2.46,6.18) iter 27: time=473.05 for 23 active reps approxLL diffs: (1.73,6.35) iter 28: time=477.33 for 23 active reps approxLL diffs: (1.26,4.45) iter 29: time=489.47 for 23 active reps approxLL diffs: (1.10,4.05) iter 30: time=484.87 for 23 active reps approxLL diffs: (1.10,3.90) iter 31: time=481.42 for 23 active reps approxLL diffs: (1.02,5.51) iter 32: time=479.86 for 23 active reps approxLL diffs: (0.87,3.39) iter 33: time=476.29 for 23 active reps approxLL diffs: (0.68,2.69) iter 34: time=493.75 for 23 active reps approxLL diffs: (0.62,2.23) iter 35: time=472.64 for 23 active reps approxLL diffs: (0.42,2.55) iter 36: time=489.51 for 23 active reps approxLL diffs: (0.30,2.71) iter 37: time=474.86 for 23 active reps approxLL diffs: (0.27,3.51) iter 38: time=487.80 for 23 active reps approxLL diffs: (0.27,3.52) iter 39: time=476.74 for 23 active reps approxLL diffs: (0.26,1.79) iter 40: time=481.45 for 23 active reps approxLL diffs: (0.18,3.70) iter 41: time=477.26 for 23 active reps approxLL diffs: (0.14,2.05) iter 42: time=470.64 for 23 active reps approxLL diffs: (0.16,1.36) iter 43: time=469.68 for 23 active reps approxLL diffs: (0.16,1.66) iter 44: time=474.82 for 23 active reps approxLL diffs: (0.13,1.39) iter 45: time=468.36 for 23 active reps approxLL diffs: (0.09,1.71) iter 46: time=471.09 for 23 active reps approxLL diffs: (0.09,1.27) iter 47: time=473.43 for 23 active reps approxLL diffs: (0.09,1.34) iter 48: time=469.51 for 23 active reps approxLL diffs: (0.07,1.22) iter 49: time=478.53 for 23 active reps approxLL diffs: (0.07,1.68) iter 50: time=464.74 for 23 active reps approxLL diffs: (0.05,0.89) iter 51: time=466.44 for 23 active reps approxLL diffs: (0.03,0.81) iter 52: time=480.20 for 23 active reps approxLL diffs: (0.03,0.81) iter 53: time=468.06 for 23 active reps approxLL diffs: (0.03,0.78) iter 54: time=463.08 for 23 active reps approxLL diffs: (0.03,0.82) iter 55: time=464.06 for 23 active reps approxLL diffs: (0.03,1.16) iter 56: time=471.26 for 23 active reps approxLL diffs: (0.03,2.74) iter 57: time=468.01 for 23 active reps approxLL diffs: (0.03,1.94) iter 58: time=474.51 for 23 active reps approxLL diffs: (0.04,0.54) iter 59: time=470.02 for 23 active reps approxLL diffs: (0.02,0.43) iter 60: time=469.28 for 23 active reps approxLL diffs: (0.02,0.47) iter 61: time=466.13 for 23 active reps approxLL diffs: (0.01,0.51) iter 62: time=455.73 for 22 active reps approxLL diffs: (0.01,0.28) iter 63: time=437.83 for 21 active reps approxLL diffs: (0.02,0.44) iter 64: time=439.77 for 21 active reps approxLL diffs: (0.01,0.46) iter 65: time=437.12 for 21 active reps approxLL diffs: (0.01,0.34) iter 66: time=415.03 for 20 active reps approxLL diffs: (0.02,0.46) iter 67: time=410.61 for 20 active reps approxLL diffs: (0.02,0.67) iter 68: time=417.09 for 20 active reps approxLL diffs: (0.01,0.48) iter 69: time=404.45 for 20 active reps approxLL diffs: (0.01,1.65) iter 70: time=439.83 for 19 active reps approxLL diffs: (0.01,1.43) iter 71: time=409.90 for 18 active reps approxLL diffs: (0.01,0.87) iter 72: time=374.50 for 16 active reps approxLL diffs: (0.01,0.63) iter 73: time=349.03 for 13 active reps approxLL diffs: (0.01,0.32) iter 74: time=333.60 for 12 active reps approxLL diffs: (0.01,0.61) iter 75: time=333.88 for 12 active reps approxLL diffs: (0.01,0.46) iter 76: time=349.68 for 11 active reps approxLL diffs: (0.02,0.25) iter 77: time=343.81 for 11 active reps approxLL diffs: (0.01,0.15) iter 78: time=343.81 for 11 active reps approxLL diffs: (0.01,0.11) iter 79: time=343.99 for 11 active reps approxLL diffs: (0.01,0.23) iter 80: time=359.12 for 11 active reps approxLL diffs: (0.01,0.36) iter 81: time=342.51 for 11 active reps approxLL diffs: (0.01,0.54) iter 82: time=338.65 for 11 active reps approxLL diffs: (0.01,0.47) iter 83: time=331.61 for 10 active reps approxLL diffs: (0.00,0.26) iter 84: time=282.62 for 7 active reps approxLL diffs: (0.03,0.36) iter 85: time=285.77 for 7 active reps approxLL diffs: (0.02,0.41) iter 86: time=286.39 for 7 active reps approxLL diffs: (0.01,0.31) iter 87: time=293.15 for 7 active reps approxLL diffs: (0.01,0.14) iter 88: time=263.86 for 6 active reps approxLL diffs: (0.01,0.08) iter 89: time=241.45 for 5 active reps approxLL diffs: (0.01,0.05) iter 90: time=248.21 for 5 active reps approxLL diffs: (0.00,0.04) iter 91: time=234.44 for 4 active reps approxLL diffs: (0.01,0.05) iter 92: time=238.34 for 3 active reps approxLL diffs: (0.01,0.06) iter 93: time=227.42 for 3 active reps approxLL diffs: (0.01,0.08) iter 94: time=236.28 for 3 active reps approxLL diffs: (0.02,0.13) iter 95: time=236.44 for 3 active reps approxLL diffs: (0.02,0.19) iter 96: time=242.33 for 3 active reps approxLL diffs: (0.01,0.15) iter 97: time=212.67 for 2 active reps approxLL diffs: (0.02,0.07) iter 98: time=215.47 for 2 active reps approxLL diffs: (0.01,0.03) iter 99: time=222.61 for 2 active reps approxLL diffs: (0.01,0.01) iter 100: time=175.05 for 1 active reps approxLL diffs: (0.01,0.01) iter 101: time=176.94 for 1 active reps approxLL diffs: (0.01,0.01) iter 102: time=179.87 for 1 active reps approxLL diffs: (0.01,0.01) iter 103: time=173.01 for 1 active reps approxLL diffs: (0.02,0.02) iter 104: time=171.83 for 1 active reps approxLL diffs: (0.02,0.02) iter 105: time=163.25 for 1 active reps approxLL diffs: (0.04,0.04) iter 106: time=166.01 for 1 active reps approxLL diffs: (0.07,0.07) iter 107: time=162.70 for 1 active reps approxLL diffs: (0.11,0.11) iter 108: time=194.14 for 1 active reps approxLL diffs: (0.09,0.09) iter 109: time=194.12 for 1 active reps approxLL diffs: (0.03,0.03) iter 110: time=188.60 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 110: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 80.7%, memory/overhead = 19.3% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 583483/709999 Masking windows around outlier snps (chisq > 445.9) # of SNPs remaining after outlier window removal: 571746/583483 Intercept of LD Score regression for ref stats: 1.265 (0.024) Estimated attenuation: 0.184 (0.015) Intercept of LD Score regression for cur stats: 1.260 (0.024) Calibration factor (ref/cur) to multiply by: 1.004 (0.002) Time for computing Bayesian mixed model assoc stats = 43935.8 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=502.84 for 1 active reps iter 2: time=276.99 for 1 active reps approxLL diffs: (32452.58,32452.58) iter 3: time=260.05 for 1 active reps approxLL diffs: (6359.66,6359.66) iter 4: time=265.41 for 1 active reps approxLL diffs: (2084.86,2084.86) iter 5: time=290.72 for 1 active reps approxLL diffs: (912.65,912.65) iter 6: time=301.65 for 1 active reps approxLL diffs: (494.75,494.75) iter 7: time=204.93 for 1 active reps approxLL diffs: (300.20,300.20) iter 8: time=174.69 for 1 active reps approxLL diffs: (202.70,202.70) iter 9: time=185.10 for 1 active reps approxLL diffs: (145.77,145.77) iter 10: time=207.11 for 1 active reps approxLL diffs: (105.24,105.24) iter 11: time=257.92 for 1 active reps approxLL diffs: (75.04,75.04) iter 12: time=294.59 for 1 active reps approxLL diffs: (56.02,56.02) iter 13: time=279.03 for 1 active reps approxLL diffs: (38.44,38.44) iter 14: time=241.69 for 1 active reps approxLL diffs: (29.92,29.92) iter 15: time=222.34 for 1 active reps approxLL diffs: (26.63,26.63) iter 16: time=261.51 for 1 active reps approxLL diffs: (24.19,24.19) iter 17: time=285.90 for 1 active reps approxLL diffs: (19.63,19.63) iter 18: time=283.83 for 1 active reps approxLL diffs: (13.33,13.33) iter 19: time=270.29 for 1 active reps approxLL diffs: (9.49,9.49) iter 20: time=270.21 for 1 active reps approxLL diffs: (6.47,6.47) iter 21: time=278.66 for 1 active reps approxLL diffs: (7.46,7.46) iter 22: time=296.99 for 1 active reps approxLL diffs: (8.14,8.14) iter 23: time=283.70 for 1 active reps approxLL diffs: (5.36,5.36) iter 24: time=272.52 for 1 active reps approxLL diffs: (4.00,4.00) iter 25: time=271.38 for 1 active reps approxLL diffs: (3.09,3.09) iter 26: time=284.71 for 1 active reps approxLL diffs: (2.13,2.13) iter 27: time=294.32 for 1 active reps approxLL diffs: (1.68,1.68) iter 28: time=264.25 for 1 active reps approxLL diffs: (1.94,1.94) iter 29: time=228.72 for 1 active reps approxLL diffs: (2.11,2.11) iter 30: time=178.88 for 1 active reps approxLL diffs: (1.17,1.17) iter 31: time=193.51 for 1 active reps approxLL diffs: (0.69,0.69) iter 32: time=178.94 for 1 active reps approxLL diffs: (0.78,0.78) iter 33: time=181.07 for 1 active reps approxLL diffs: (1.07,1.07) iter 34: time=190.73 for 1 active reps approxLL diffs: (1.34,1.34) iter 35: time=202.68 for 1 active reps approxLL diffs: (1.66,1.66) iter 36: time=239.63 for 1 active reps approxLL diffs: (1.29,1.29) iter 37: time=221.27 for 1 active reps approxLL diffs: (0.91,0.91) iter 38: time=239.58 for 1 active reps approxLL diffs: (0.62,0.62) iter 39: time=258.70 for 1 active reps approxLL diffs: (0.37,0.37) iter 40: time=301.33 for 1 active reps approxLL diffs: (0.34,0.34) iter 41: time=260.20 for 1 active reps approxLL diffs: (0.50,0.50) iter 42: time=250.73 for 1 active reps approxLL diffs: (0.86,0.86) iter 43: time=266.44 for 1 active reps approxLL diffs: (1.16,1.16) iter 44: time=275.08 for 1 active reps approxLL diffs: (0.98,0.98) iter 45: time=300.06 for 1 active reps approxLL diffs: (0.73,0.73) iter 46: time=284.50 for 1 active reps approxLL diffs: (0.53,0.53) iter 47: time=273.40 for 1 active reps approxLL diffs: (0.17,0.17) iter 48: time=272.15 for 1 active reps approxLL diffs: (0.07,0.07) iter 49: time=273.91 for 1 active reps approxLL diffs: (0.07,0.07) iter 50: time=281.69 for 1 active reps approxLL diffs: (0.07,0.07) iter 51: time=293.39 for 1 active reps approxLL diffs: (0.08,0.08) iter 52: time=273.48 for 1 active reps approxLL diffs: (0.09,0.09) iter 53: time=248.39 for 1 active reps approxLL diffs: (0.11,0.11) iter 54: time=270.86 for 1 active reps approxLL diffs: (0.15,0.15) iter 55: time=267.65 for 1 active reps approxLL diffs: (0.25,0.25) iter 56: time=208.25 for 1 active reps approxLL diffs: (0.38,0.38) iter 57: time=254.80 for 1 active reps approxLL diffs: (0.46,0.46) iter 58: time=267.21 for 1 active reps approxLL diffs: (0.37,0.37) iter 59: time=281.08 for 1 active reps approxLL diffs: (0.22,0.22) iter 60: time=275.47 for 1 active reps approxLL diffs: (0.13,0.13) iter 61: time=290.93 for 1 active reps approxLL diffs: (0.13,0.13) iter 62: time=281.39 for 1 active reps approxLL diffs: (0.24,0.24) iter 63: time=277.40 for 1 active reps approxLL diffs: (0.48,0.48) iter 64: time=279.50 for 1 active reps approxLL diffs: (0.57,0.57) iter 65: time=290.28 for 1 active reps approxLL diffs: (0.40,0.40) iter 66: time=267.40 for 1 active reps approxLL diffs: (0.25,0.25) iter 67: time=280.73 for 1 active reps approxLL diffs: (0.17,0.17) iter 68: time=283.98 for 1 active reps approxLL diffs: (0.13,0.13) iter 69: time=278.01 for 1 active reps approxLL diffs: (0.09,0.09) iter 70: time=278.41 for 1 active reps approxLL diffs: (0.05,0.05) iter 71: time=278.43 for 1 active reps approxLL diffs: (0.02,0.02) iter 72: time=274.72 for 1 active reps approxLL diffs: (0.01,0.01) iter 73: time=275.35 for 1 active reps approxLL diffs: (0.01,0.01) iter 74: time=267.96 for 1 active reps approxLL diffs: (0.02,0.02) iter 75: time=256.06 for 1 active reps approxLL diffs: (0.03,0.03) iter 76: time=273.91 for 1 active reps approxLL diffs: (0.04,0.04) iter 77: time=274.44 for 1 active reps approxLL diffs: (0.05,0.05) iter 78: time=299.34 for 1 active reps approxLL diffs: (0.05,0.05) iter 79: time=270.00 for 1 active reps approxLL diffs: (0.05,0.05) iter 80: time=276.73 for 1 active reps approxLL diffs: (0.04,0.04) iter 81: time=284.17 for 1 active reps approxLL diffs: (0.03,0.03) iter 82: time=277.71 for 1 active reps approxLL diffs: (0.02,0.02) iter 83: time=283.94 for 1 active reps approxLL diffs: (0.01,0.01) iter 84: time=285.42 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 84: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 58.9%, memory/overhead = 41.1% Time for computing and writing betas = 22267.8 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.40624 (709968 good SNPs) lambdaGC: 1.54563 Mean BOLT_LMM_INF: 2.71756 (709968 good SNPs) lambdaGC: 1.62584 Mean BOLT_LMM: 2.81066 (709968 good SNPs) lambdaGC: 1.64333 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 8425.14 sec Total elapsed time for analysis = 174777 sec Command being timed: "/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam --allowX --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe1000.txt --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt --maxMissingPerSnp=0.15 --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v3.061518.tab --phenoCol=bmd_HEEL_TSCOREz --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.bmd_HEEL_TSCOREz.predbetas.txt.gz --statsFile=bolt_460K_selfRepWhite.bmd_HEEL_TSCOREz.stats.gz --verboseStats" User time (seconds): 1243824.65 System time (seconds): 27065.16 Percent of CPU this job got: 727% Elapsed (wall clock) time (h:mm:ss or m:ss): 48:33:16 Average shared text size (kbytes): 0 Average unshared data size (kbytes): 0 Average stack size (kbytes): 0 Average total size (kbytes): 0 Maximum resident set size (kbytes): 86995824 Average resident set size (kbytes): 0 Major (requiring I/O) page faults: 70 Minor (reclaiming a frame) page faults: 3623587395 Voluntary context switches: 6524358 Involuntary context switches: 8033360 Swaps: 0 File system inputs: 380828416 File system outputs: 111112 Socket messages sent: 0 Socket messages received: 0 Signals delivered: 0 Page size (bytes): 4096 Exit status: 0