+-----------------------------+ | ___ | | 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_MEAN_CORPUSCULAR_HEMOGLOBIN \ --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_MEAN_CORPUSCULAR_HEMOGLOBIN.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_MEAN_CORPUSCULAR_HEMOGLOBIN.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 = 2707.52 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: 443686 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 = 443686 Singular values of covariate matrix: S[0] = 2.2689e+06 S[1] = 4756.66 S[2] = 469.319 S[3] = 293.226 S[4] = 199.669 S[5] = 194.193 S[6] = 183.394 S[7] = 174.895 S[8] = 170.627 S[9] = 165.356 S[10] = 162.619 S[11] = 153.598 S[12] = 145.559 S[13] = 143.518 S[14] = 141.024 S[15] = 135.707 S[16] = 132.305 S[17] = 130.063 S[18] = 126.049 S[19] = 116.128 S[20] = 111.944 S[21] = 99.6116 S[22] = 44.8259 S[23] = 23.9595 S[24] = 19.2897 S[25] = 0.982969 S[26] = 0.982747 S[27] = 0.982661 S[28] = 0.982553 S[29] = 0.982386 S[30] = 0.982153 S[31] = 0.982113 S[32] = 0.981987 S[33] = 0.981893 S[34] = 0.981801 S[35] = 0.981624 S[36] = 0.981496 S[37] = 0.98141 S[38] = 0.981331 S[39] = 0.981084 S[40] = 0.981009 S[41] = 0.980699 S[42] = 0.980497 S[43] = 0.960188 S[44] = 0.88707 S[45] = 4.47516e-12 S[46] = 4.62384e-13 S[47] = 3.61223e-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: 441102.566063 Dimension of all-1s proj space (Nused-1): 443685 Time for covariate data setup + Bolt initialization = 4367.63 sec Phenotype 1: N = 443686 mean = 0.0379958 std = 0.978278 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 442.888 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 443686) 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=373.58 rNorms/orig: (0.6,0.8) res2s: 719139..118444 iter 2: time=393.98 rNorms/orig: (0.5,0.6) res2s: 852831..162664 iter 3: time=401.89 rNorms/orig: (0.3,0.4) res2s: 985213..197053 iter 4: time=400.10 rNorms/orig: (0.2,0.2) res2s: 1.03407e+06..214390 iter 5: time=389.39 rNorms/orig: (0.1,0.2) res2s: 1.06078e+06..221464 iter 6: time=412.01 rNorms/orig: (0.09,0.1) res2s: 1.07358e+06..225917 iter 7: time=423.88 rNorms/orig: (0.06,0.07) res2s: 1.07997e+06..227939 iter 8: time=462.58 rNorms/orig: (0.04,0.04) res2s: 1.08317e+06..228922 iter 9: time=497.65 rNorms/orig: (0.02,0.03) res2s: 1.08459e+06..229333 iter 10: time=478.15 rNorms/orig: (0.01,0.02) res2s: 1.08528e+06..229534 iter 11: time=402.76 rNorms/orig: (0.01,0.01) res2s: 1.08559e+06..229625 iter 12: time=431.18 rNorms/orig: (0.006,0.008) res2s: 1.08574e+06..229668 iter 13: time=442.08 rNorms/orig: (0.004,0.005) res2s: 1.08579e+06..229684 iter 14: time=448.75 rNorms/orig: (0.003,0.003) res2s: 1.08582e+06..229693 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 47.4%, memory/overhead = 52.6% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.168258 Estimating MC scaling f_REML at log(delta) = -0.00573825, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=456.95 rNorms/orig: (1,1) res2s: 75861.9..31563.2 iter 2: time=458.33 rNorms/orig: (1,1) res2s: 109325..54515.1 iter 3: time=533.72 rNorms/orig: (0.8,1) res2s: 164321..83402.2 iter 4: time=452.48 rNorms/orig: (0.6,0.7) res2s: 201045..106552 iter 5: time=457.43 rNorms/orig: (0.5,0.6) res2s: 229663..120226 iter 6: time=470.49 rNorms/orig: (0.4,0.4) res2s: 249941..132527 iter 7: time=542.85 rNorms/orig: (0.3,0.3) res2s: 264626..140680 iter 8: time=404.29 rNorms/orig: (0.2,0.3) res2s: 275304..146428 iter 9: time=406.46 rNorms/orig: (0.2,0.2) res2s: 282261..149892 iter 10: time=465.95 rNorms/orig: (0.1,0.2) res2s: 287081..152307 iter 11: time=411.59 rNorms/orig: (0.1,0.1) res2s: 290269..153872 iter 12: time=391.08 rNorms/orig: (0.09,0.09) res2s: 292385..154952 iter 13: time=431.37 rNorms/orig: (0.07,0.07) res2s: 293571..155546 iter 14: time=484.41 rNorms/orig: (0.05,0.06) res2s: 294433..155991 iter 15: time=439.91 rNorms/orig: (0.04,0.04) res2s: 294975..156254 iter 16: time=377.31 rNorms/orig: (0.03,0.03) res2s: 295312..156433 iter 17: time=375.22 rNorms/orig: (0.02,0.03) res2s: 295514..156534 iter 18: time=376.54 rNorms/orig: (0.02,0.02) res2s: 295665..156601 iter 19: time=377.57 rNorms/orig: (0.01,0.02) res2s: 295758..156646 iter 20: time=373.00 rNorms/orig: (0.01,0.01) res2s: 295813..156673 iter 21: time=374.73 rNorms/orig: (0.009,0.009) res2s: 295845..156690 iter 22: time=376.71 rNorms/orig: (0.007,0.007) res2s: 295866..156701 iter 23: time=379.46 rNorms/orig: (0.005,0.006) res2s: 295879..156708 iter 24: time=370.11 rNorms/orig: (0.004,0.004) res2s: 295887..156713 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 47.4%, memory/overhead = 52.6% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.18898 Estimating MC scaling f_REML at log(delta) = 0.57543, h2 = 0.358664... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=375.59 rNorms/orig: (0.8,1) res2s: 272789..68228.2 iter 2: time=373.94 rNorms/orig: (0.8,0.9) res2s: 355715..104675 iter 3: time=376.24 rNorms/orig: (0.5,0.7) res2s: 462077..140819 iter 4: time=373.49 rNorms/orig: (0.4,0.4) res2s: 514388..163767 iter 5: time=372.93 rNorms/orig: (0.3,0.3) res2s: 548158..175045 iter 6: time=369.33 rNorms/orig: (0.2,0.2) res2s: 567852..183583 iter 7: time=371.95 rNorms/orig: (0.1,0.2) res2s: 579729..188309 iter 8: time=375.27 rNorms/orig: (0.1,0.1) res2s: 586952..191105 iter 9: time=366.57 rNorms/orig: (0.08,0.09) res2s: 590865..192524 iter 10: time=373.01 rNorms/orig: (0.05,0.06) res2s: 593163..193364 iter 11: time=385.08 rNorms/orig: (0.04,0.04) res2s: 594426..193825 iter 12: time=370.39 rNorms/orig: (0.03,0.03) res2s: 595137..194091 iter 13: time=375.82 rNorms/orig: (0.02,0.02) res2s: 595471..194216 iter 14: time=372.66 rNorms/orig: (0.01,0.01) res2s: 595679..194295 iter 15: time=342.96 rNorms/orig: (0.009,0.01) res2s: 595789..194335 iter 16: time=338.19 rNorms/orig: (0.007,0.007) res2s: 595846..194357 iter 17: time=345.26 rNorms/orig: (0.005,0.005) res2s: 595876..194368 iter 18: time=344.49 rNorms/orig: (0.003,0.004) res2s: 595894..194374 Converged at iter 18: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 48.0%, memory/overhead = 52.0% MCscaling: logDelta = 0.58, h2 = 0.359, f = 0.000311065 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.359 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.326901, logDelta = 0.575430, f = 0.000311065 Time for fitting variance components = 23459.5 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=717.35 rNorms/orig: (0.6,1) res2s: 66653.3..177060 iter 2: time=728.01 rNorms/orig: (0.6,0.9) res2s: 103751..218587 iter 3: time=714.74 rNorms/orig: (0.4,0.7) res2s: 141034..242132 iter 4: time=714.30 rNorms/orig: (0.3,0.5) res2s: 165948..253463 iter 5: time=715.50 rNorms/orig: (0.2,0.4) res2s: 178191..261634 iter 6: time=749.69 rNorms/orig: (0.1,0.2) res2s: 187549..266005 iter 7: time=731.15 rNorms/orig: (0.1,0.2) res2s: 192943..268672 iter 8: time=733.56 rNorms/orig: (0.08,0.1) res2s: 196218..270299 iter 9: time=743.01 rNorms/orig: (0.05,0.1) res2s: 197869..271140 iter 10: time=738.71 rNorms/orig: (0.04,0.06) res2s: 198920..271648 iter 11: time=753.90 rNorms/orig: (0.03,0.05) res2s: 199503..271926 iter 12: time=719.61 rNorms/orig: (0.02,0.03) res2s: 199846..272081 iter 13: time=722.74 rNorms/orig: (0.01,0.03) res2s: 200008..272163 iter 14: time=710.35 rNorms/orig: (0.008,0.02) res2s: 200118..272211 iter 15: time=714.94 rNorms/orig: (0.006,0.01) res2s: 200172..272234 iter 16: time=714.82 rNorms/orig: (0.004,0.009) res2s: 200203..272248 iter 17: time=706.43 rNorms/orig: (0.003,0.007) res2s: 200219..272256 iter 18: time=730.51 rNorms/orig: (0.002,0.005) res2s: 200228..272260 iter 19: time=715.78 rNorms/orig: (0.001,0.003) res2s: 200233..272262 iter 20: time=659.61 rNorms/orig: (0.0009,0.003) res2s: 200236..272263 iter 21: time=697.76 rNorms/orig: (0.0005,0.002) res2s: 200237..272264 iter 22: time=695.50 rNorms/orig: (0.0003,0.001) res2s: 200238..272264 iter 23: time=688.66 rNorms/orig: (0.0002,0.001) res2s: 200238..272264 iter 24: time=692.44 rNorms/orig: (0.0001,0.0007) res2s: 200239..272264 iter 25: time=687.57 rNorms/orig: (0.0001,0.0005) res2s: 200239..272264 Converged at iter 25: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 73.8%, memory/overhead = 26.2% AvgPro: 3.178 AvgRetro: 3.066 Calibration: 1.037 (0.017) (30 SNPs) Ratio of medians: 1.015 Median of ratios: 1.015 WARNING: Calibration std error is high; consider increasing --numCalibSnps Using ratio of medians instead: 1.01505 Time for computing infinitesimal model assoc stats = 18357.4 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 16.241 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 > 443.7) # of SNPs remaining after outlier window removal: 566073/579570 Intercept of LD Score regression for ref stats: 1.145 (0.019) Estimated attenuation: 0.140 (0.021) Intercept of LD Score regression for cur stats: 1.144 (0.016) Calibration factor (ref/cur) to multiply by: 1.001 (0.005) LINREG intercept inflation = 0.998598 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 = 354948 Singular values of covariate matrix: S[0] = 2.03022e+06 S[1] = 4252.84 S[2] = 419.814 S[3] = 262.276 S[4] = 178.653 S[5] = 173.762 S[6] = 164.171 S[7] = 156.439 S[8] = 152.576 S[9] = 147.857 S[10] = 145.522 S[11] = 137.36 S[12] = 130.116 S[13] = 128.242 S[14] = 126.096 S[15] = 121.37 S[16] = 118.36 S[17] = 116.407 S[18] = 112.545 S[19] = 103.83 S[20] = 100.149 S[21] = 89.0378 S[22] = 40.1383 S[23] = 21.4147 S[24] = 17.2487 S[25] = 0.881497 S[26] = 0.880466 S[27] = 0.880187 S[28] = 0.880051 S[29] = 0.879614 S[30] = 0.87944 S[31] = 0.879033 S[32] = 0.878846 S[33] = 0.878772 S[34] = 0.878042 S[35] = 0.877833 S[36] = 0.877412 S[37] = 0.877119 S[38] = 0.876842 S[39] = 0.876732 S[40] = 0.876531 S[41] = 0.87611 S[42] = 0.874917 S[43] = 0.857235 S[44] = 0.793987 S[45] = 5.09704e-12 S[46] = 4.48957e-13 S[47] = 2.86681e-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: 352872.419422 Dimension of all-1s proj space (Nused-1): 354947 Beginning variational Bayes iter 1: time=716.16 for 18 active reps iter 2: time=475.26 for 18 active reps approxLL diffs: (18812.36,23322.77) iter 3: time=456.98 for 18 active reps approxLL diffs: (2259.11,5024.30) iter 4: time=462.76 for 18 active reps approxLL diffs: (502.06,2115.65) iter 5: time=465.01 for 18 active reps approxLL diffs: (164.03,1140.25) iter 6: time=466.57 for 18 active reps approxLL diffs: (68.08,636.35) iter 7: time=474.96 for 18 active reps approxLL diffs: (34.34,403.91) iter 8: time=468.42 for 18 active reps approxLL diffs: (19.83,277.33) iter 9: time=448.90 for 18 active reps approxLL diffs: (12.46,202.11) iter 10: time=454.15 for 18 active reps approxLL diffs: (8.48,137.41) iter 11: time=474.55 for 18 active reps approxLL diffs: (6.12,97.53) iter 12: time=481.24 for 18 active reps approxLL diffs: (4.52,74.37) iter 13: time=487.92 for 18 active reps approxLL diffs: (3.37,51.71) iter 14: time=500.82 for 18 active reps approxLL diffs: (2.55,43.18) iter 15: time=478.84 for 18 active reps approxLL diffs: (1.99,35.09) iter 16: time=474.28 for 18 active reps approxLL diffs: (1.59,37.79) iter 17: time=477.96 for 18 active reps approxLL diffs: (1.29,32.19) iter 18: time=486.75 for 18 active reps approxLL diffs: (1.05,31.05) iter 19: time=473.36 for 18 active reps approxLL diffs: (0.85,23.55) iter 20: time=463.42 for 18 active reps approxLL diffs: (0.69,18.30) iter 21: time=459.99 for 18 active reps approxLL diffs: (0.58,16.53) iter 22: time=476.36 for 18 active reps approxLL diffs: (0.49,15.02) iter 23: time=466.62 for 18 active reps approxLL diffs: (0.42,16.03) iter 24: time=463.60 for 18 active reps approxLL diffs: (0.36,12.36) iter 25: time=462.84 for 18 active reps approxLL diffs: (0.30,9.49) iter 26: time=460.04 for 18 active reps approxLL diffs: (0.26,7.45) iter 27: time=462.36 for 18 active reps approxLL diffs: (0.22,10.14) iter 28: time=458.39 for 18 active reps approxLL diffs: (0.19,8.44) iter 29: time=457.32 for 18 active reps approxLL diffs: (0.17,7.04) iter 30: time=467.92 for 18 active reps approxLL diffs: (0.15,5.92) iter 31: time=460.93 for 18 active reps approxLL diffs: (0.13,5.85) iter 32: time=455.69 for 18 active reps approxLL diffs: (0.12,5.46) iter 33: time=457.45 for 18 active reps approxLL diffs: (0.11,5.91) iter 34: time=469.74 for 18 active reps approxLL diffs: (0.10,5.66) iter 35: time=465.76 for 18 active reps approxLL diffs: (0.09,4.30) iter 36: time=460.30 for 18 active reps approxLL diffs: (0.08,4.27) iter 37: time=460.77 for 18 active reps approxLL diffs: (0.07,6.19) iter 38: time=466.94 for 18 active reps approxLL diffs: (0.06,5.75) iter 39: time=485.97 for 18 active reps approxLL diffs: (0.05,4.20) iter 40: time=492.41 for 18 active reps approxLL diffs: (0.05,2.77) iter 41: time=488.66 for 18 active reps approxLL diffs: (0.04,1.88) iter 42: time=468.48 for 18 active reps approxLL diffs: (0.04,1.58) iter 43: time=473.72 for 18 active reps approxLL diffs: (0.04,2.10) iter 44: time=497.19 for 18 active reps approxLL diffs: (0.03,4.41) iter 45: time=518.40 for 18 active reps approxLL diffs: (0.03,4.75) iter 46: time=480.20 for 18 active reps approxLL diffs: (0.01,2.79) iter 47: time=481.94 for 18 active reps approxLL diffs: (0.01,1.21) iter 48: time=481.46 for 17 active reps approxLL diffs: (0.02,0.74) iter 49: time=545.00 for 17 active reps approxLL diffs: (0.02,0.97) iter 50: time=544.39 for 17 active reps approxLL diffs: (0.02,0.61) iter 51: time=462.57 for 17 active reps approxLL diffs: (0.02,0.31) iter 52: time=396.32 for 17 active reps approxLL diffs: (0.02,0.24) iter 53: time=397.92 for 17 active reps approxLL diffs: (0.01,0.19) iter 54: time=396.01 for 17 active reps approxLL diffs: (0.01,0.16) iter 55: time=394.03 for 17 active reps approxLL diffs: (0.01,0.14) iter 56: time=402.30 for 17 active reps approxLL diffs: (0.01,0.13) iter 57: time=398.70 for 17 active reps approxLL diffs: (0.01,0.32) iter 58: time=379.33 for 16 active reps approxLL diffs: (0.01,0.99) iter 59: time=404.34 for 15 active reps approxLL diffs: (0.01,0.50) iter 60: time=387.63 for 14 active reps approxLL diffs: (0.01,0.68) iter 61: time=389.31 for 14 active reps approxLL diffs: (0.01,1.23) iter 62: time=390.72 for 14 active reps approxLL diffs: (0.01,1.98) iter 63: time=376.85 for 14 active reps approxLL diffs: (0.01,0.64) iter 64: time=355.01 for 13 active reps approxLL diffs: (0.01,0.46) iter 65: time=357.96 for 13 active reps approxLL diffs: (0.01,0.28) iter 66: time=337.33 for 12 active reps approxLL diffs: (0.01,0.38) iter 67: time=334.80 for 12 active reps approxLL diffs: (0.01,0.30) iter 68: time=333.89 for 12 active reps approxLL diffs: (0.01,0.14) iter 69: time=324.71 for 10 active reps approxLL diffs: (0.01,0.05) iter 70: time=321.85 for 10 active reps approxLL diffs: (0.01,0.06) iter 71: time=301.90 for 9 active reps approxLL diffs: (0.01,0.10) iter 72: time=261.61 for 8 active reps approxLL diffs: (0.01,0.12) iter 73: time=263.47 for 6 active reps approxLL diffs: (0.01,0.16) iter 74: time=264.14 for 6 active reps approxLL diffs: (0.01,0.40) iter 75: time=244.51 for 5 active reps approxLL diffs: (0.01,0.53) iter 76: time=246.54 for 5 active reps approxLL diffs: (0.01,0.38) iter 77: time=227.98 for 4 active reps approxLL diffs: (0.01,0.28) iter 78: time=231.20 for 4 active reps approxLL diffs: (0.01,0.26) iter 79: time=247.96 for 4 active reps approxLL diffs: (0.01,0.30) iter 80: time=261.30 for 3 active reps approxLL diffs: (0.01,0.33) iter 81: time=263.39 for 3 active reps approxLL diffs: (0.01,0.15) iter 82: time=264.71 for 3 active reps approxLL diffs: (0.01,0.08) iter 83: time=268.52 for 3 active reps approxLL diffs: (0.01,0.03) iter 84: time=265.07 for 3 active reps approxLL diffs: (0.01,0.03) iter 85: time=249.05 for 2 active reps approxLL diffs: (0.04,0.07) iter 86: time=245.12 for 2 active reps approxLL diffs: (0.10,0.16) iter 87: time=249.75 for 2 active reps approxLL diffs: (0.15,0.32) iter 88: time=246.10 for 2 active reps approxLL diffs: (0.10,0.45) iter 89: time=246.15 for 2 active reps approxLL diffs: (0.03,0.26) iter 90: time=247.94 for 2 active reps approxLL diffs: (0.01,0.06) iter 91: time=200.53 for 1 active reps approxLL diffs: (0.01,0.01) iter 92: time=199.02 for 1 active reps approxLL diffs: (0.00,0.00) Converged at iter 92: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.7%, memory/overhead = 22.3% Computing predictions on left-out cross-validation fold Time for computing predictions = 8694.86 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.02: 0.251570 f2=0.3, p=0.01: 0.250897 f2=0.3, p=0.05: 0.248090 ... f2=0.5, p=0.5: 0.172328 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.941278 Absolute prediction MSE using standard LMM: 0.77907 Absolute prediction MSE, fold-best f2=0.3, p=0.02: 0.70448 Absolute pred MSE using f2=0.5, p=0.5: 0.779070 Absolute pred MSE using f2=0.5, p=0.2: 0.750994 Absolute pred MSE using f2=0.5, p=0.1: 0.730530 Absolute pred MSE using f2=0.5, p=0.05: 0.716113 Absolute pred MSE using f2=0.5, p=0.02: 0.708652 Absolute pred MSE using f2=0.5, p=0.01: 0.707856 Absolute pred MSE using f2=0.3, p=0.5: 0.768582 Absolute pred MSE using f2=0.3, p=0.2: 0.736210 Absolute pred MSE using f2=0.3, p=0.1: 0.717856 Absolute pred MSE using f2=0.3, p=0.05: 0.707756 Absolute pred MSE using f2=0.3, p=0.02: 0.704480 Absolute pred MSE using f2=0.3, p=0.01: 0.705114 Absolute pred MSE using f2=0.1, p=0.5: 0.756274 Absolute pred MSE using f2=0.1, p=0.2: 0.724791 Absolute pred MSE using f2=0.1, p=0.1: 0.711380 Absolute pred MSE using f2=0.1, p=0.05: 0.707847 Absolute pred MSE using f2=0.1, p=0.02: 0.708999 Absolute pred MSE using f2=0.1, p=0.01: 0.709839 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.172 Relative improvement in prediction MSE using non-inf model: 0.096 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.02 Time for estimating mixture parameters = 50130.5 sec === Computing Bayesian mixed model assoc stats with mixture prior === Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Beginning variational Bayes iter 1: time=761.10 for 23 active reps iter 2: time=519.10 for 23 active reps approxLL diffs: (21626.54,28152.40) iter 3: time=516.67 for 23 active reps approxLL diffs: (4484.59,6394.71) iter 4: time=518.45 for 23 active reps approxLL diffs: (1645.29,2528.76) iter 5: time=519.39 for 23 active reps approxLL diffs: (841.86,1350.49) iter 6: time=519.95 for 23 active reps approxLL diffs: (471.24,776.49) iter 7: time=521.74 for 23 active reps approxLL diffs: (276.99,481.83) iter 8: time=518.90 for 23 active reps approxLL diffs: (179.15,335.93) iter 9: time=516.69 for 23 active reps approxLL diffs: (122.51,240.81) iter 10: time=515.15 for 23 active reps approxLL diffs: (81.88,154.56) iter 11: time=516.33 for 23 active reps approxLL diffs: (65.13,116.69) iter 12: time=518.76 for 23 active reps approxLL diffs: (50.42,92.37) iter 13: time=522.93 for 23 active reps approxLL diffs: (39.62,77.51) iter 14: time=517.83 for 23 active reps approxLL diffs: (29.41,60.27) iter 15: time=516.70 for 23 active reps approxLL diffs: (24.28,57.37) iter 16: time=517.18 for 23 active reps approxLL diffs: (19.96,46.76) iter 17: time=521.31 for 23 active reps approxLL diffs: (14.90,43.21) iter 18: time=518.35 for 23 active reps approxLL diffs: (16.37,36.28) iter 19: time=515.33 for 23 active reps approxLL diffs: (18.75,35.24) iter 20: time=516.23 for 23 active reps approxLL diffs: (16.41,36.84) iter 21: time=532.77 for 23 active reps approxLL diffs: (14.56,29.10) iter 22: time=527.86 for 23 active reps approxLL diffs: (12.00,22.87) iter 23: time=529.24 for 23 active reps approxLL diffs: (11.89,22.78) iter 24: time=521.12 for 23 active reps approxLL diffs: (9.73,18.27) iter 25: time=520.39 for 23 active reps approxLL diffs: (8.02,16.34) iter 26: time=523.48 for 23 active reps approxLL diffs: (4.69,15.01) iter 27: time=522.98 for 23 active reps approxLL diffs: (3.51,11.99) iter 28: time=526.53 for 23 active reps approxLL diffs: (2.64,10.24) iter 29: time=533.91 for 23 active reps approxLL diffs: (2.16,8.34) iter 30: time=531.49 for 23 active reps approxLL diffs: (1.88,9.54) iter 31: time=547.91 for 23 active reps approxLL diffs: (2.18,8.43) iter 32: time=541.20 for 23 active reps approxLL diffs: (2.12,6.52) iter 33: time=539.05 for 23 active reps approxLL diffs: (1.35,7.83) iter 34: time=542.83 for 23 active reps approxLL diffs: (1.47,6.99) iter 35: time=547.73 for 23 active reps approxLL diffs: (1.43,6.53) iter 36: time=542.18 for 23 active reps approxLL diffs: (1.00,4.19) iter 37: time=550.96 for 23 active reps approxLL diffs: (0.64,4.72) iter 38: time=492.90 for 23 active reps approxLL diffs: (0.41,7.94) iter 39: time=510.81 for 23 active reps approxLL diffs: (0.31,6.29) iter 40: time=580.71 for 23 active reps approxLL diffs: (0.30,4.14) iter 41: time=595.13 for 23 active reps approxLL diffs: (0.59,4.64) iter 42: time=592.68 for 23 active reps approxLL diffs: (0.46,6.16) iter 43: time=594.96 for 23 active reps approxLL diffs: (0.43,3.97) iter 44: time=600.50 for 23 active reps approxLL diffs: (0.71,4.46) iter 45: time=600.33 for 23 active reps approxLL diffs: (0.64,7.76) iter 46: time=602.10 for 23 active reps approxLL diffs: (0.23,3.50) iter 47: time=601.94 for 23 active reps approxLL diffs: (0.12,3.63) iter 48: time=597.28 for 23 active reps approxLL diffs: (0.08,2.34) iter 49: time=592.87 for 23 active reps approxLL diffs: (0.06,4.13) iter 50: time=590.22 for 23 active reps approxLL diffs: (0.06,2.01) iter 51: time=589.33 for 23 active reps approxLL diffs: (0.06,2.34) iter 52: time=588.80 for 23 active reps approxLL diffs: (0.06,3.79) iter 53: time=594.95 for 23 active reps approxLL diffs: (0.06,4.06) iter 54: time=595.38 for 23 active reps approxLL diffs: (0.03,7.08) iter 55: time=596.75 for 23 active reps approxLL diffs: (0.03,3.65) iter 56: time=595.57 for 23 active reps approxLL diffs: (0.03,1.48) iter 57: time=592.31 for 23 active reps approxLL diffs: (0.05,1.61) iter 58: time=596.92 for 23 active reps approxLL diffs: (0.04,3.60) iter 59: time=594.13 for 23 active reps approxLL diffs: (0.03,2.38) iter 60: time=563.77 for 23 active reps approxLL diffs: (0.03,3.14) iter 61: time=515.03 for 23 active reps approxLL diffs: (0.02,1.88) iter 62: time=510.85 for 23 active reps approxLL diffs: (0.01,2.60) iter 63: time=515.75 for 23 active reps approxLL diffs: (0.01,2.06) iter 64: time=511.89 for 23 active reps approxLL diffs: (0.01,1.82) iter 65: time=488.65 for 22 active reps approxLL diffs: (0.01,1.56) iter 66: time=462.51 for 21 active reps approxLL diffs: (0.01,2.07) iter 67: time=447.29 for 20 active reps approxLL diffs: (0.01,1.66) iter 68: time=445.80 for 18 active reps approxLL diffs: (0.01,1.67) iter 69: time=418.60 for 17 active reps approxLL diffs: (0.01,1.95) iter 70: time=414.91 for 17 active reps approxLL diffs: (0.01,1.96) iter 71: time=412.84 for 15 active reps approxLL diffs: (0.01,0.70) iter 72: time=414.59 for 15 active reps approxLL diffs: (0.01,0.98) iter 73: time=387.36 for 14 active reps approxLL diffs: (0.01,1.06) iter 74: time=382.39 for 13 active reps approxLL diffs: (0.01,1.10) iter 75: time=375.20 for 11 active reps approxLL diffs: (0.01,1.42) iter 76: time=331.80 for 10 active reps approxLL diffs: (0.01,1.00) iter 77: time=316.94 for 10 active reps approxLL diffs: (0.01,1.67) iter 78: time=316.91 for 10 active reps approxLL diffs: (0.00,2.82) iter 79: time=298.09 for 9 active reps approxLL diffs: (0.01,0.60) iter 80: time=300.62 for 9 active reps approxLL diffs: (0.01,0.63) iter 81: time=297.44 for 9 active reps approxLL diffs: (0.01,1.17) iter 82: time=255.31 for 8 active reps approxLL diffs: (0.01,1.87) iter 83: time=272.82 for 7 active reps approxLL diffs: (0.06,0.72) iter 84: time=275.50 for 7 active reps approxLL diffs: (0.06,0.41) iter 85: time=275.46 for 7 active reps approxLL diffs: (0.02,0.60) iter 86: time=279.04 for 7 active reps approxLL diffs: (0.01,1.49) iter 87: time=277.79 for 7 active reps approxLL diffs: (0.01,0.56) iter 88: time=277.65 for 7 active reps approxLL diffs: (0.01,0.10) iter 89: time=238.64 for 5 active reps approxLL diffs: (0.01,0.08) iter 90: time=216.71 for 4 active reps approxLL diffs: (0.01,0.20) iter 91: time=225.93 for 3 active reps approxLL diffs: (0.02,1.12) iter 92: time=229.60 for 3 active reps approxLL diffs: (0.01,2.97) iter 93: time=209.38 for 2 active reps approxLL diffs: (0.24,2.51) iter 94: time=209.37 for 2 active reps approxLL diffs: (0.07,0.34) iter 95: time=206.95 for 2 active reps approxLL diffs: (0.01,0.09) iter 96: time=173.48 for 1 active reps approxLL diffs: (0.05,0.05) iter 97: time=168.16 for 1 active reps approxLL diffs: (0.07,0.07) iter 98: time=161.64 for 1 active reps approxLL diffs: (0.13,0.13) iter 99: time=168.72 for 1 active reps approxLL diffs: (0.17,0.17) iter 100: time=173.60 for 1 active reps approxLL diffs: (0.16,0.16) iter 101: time=172.78 for 1 active reps approxLL diffs: (0.12,0.12) iter 102: time=178.55 for 1 active reps approxLL diffs: (0.08,0.08) iter 103: time=176.36 for 1 active reps approxLL diffs: (0.05,0.05) iter 104: time=175.62 for 1 active reps approxLL diffs: (0.03,0.03) iter 105: time=175.58 for 1 active reps approxLL diffs: (0.02,0.02) iter 106: time=176.57 for 1 active reps approxLL diffs: (0.02,0.02) iter 107: time=169.35 for 1 active reps approxLL diffs: (0.02,0.02) iter 108: time=179.36 for 1 active reps approxLL diffs: (0.02,0.02) iter 109: time=172.15 for 1 active reps approxLL diffs: (0.03,0.03) iter 110: time=184.21 for 1 active reps approxLL diffs: (0.02,0.02) iter 111: time=179.54 for 1 active reps approxLL diffs: (0.02,0.02) iter 112: time=181.52 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 112: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 79.4%, memory/overhead = 20.6% 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 > 443.7) # of SNPs remaining after outlier window removal: 566073/579570 Intercept of LD Score regression for ref stats: 1.145 (0.019) Estimated attenuation: 0.140 (0.021) Intercept of LD Score regression for cur stats: 1.147 (0.021) Calibration factor (ref/cur) to multiply by: 0.999 (0.003) Time for computing Bayesian mixed model assoc stats = 48548.8 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=415.17 for 1 active reps iter 2: time=189.63 for 1 active reps approxLL diffs: (28317.72,28317.72) iter 3: time=206.54 for 1 active reps approxLL diffs: (6413.62,6413.62) iter 4: time=225.12 for 1 active reps approxLL diffs: (2527.81,2527.81) iter 5: time=249.81 for 1 active reps approxLL diffs: (1353.82,1353.82) iter 6: time=282.29 for 1 active reps approxLL diffs: (760.58,760.58) iter 7: time=313.88 for 1 active reps approxLL diffs: (468.24,468.24) iter 8: time=323.94 for 1 active reps approxLL diffs: (322.25,322.25) iter 9: time=322.46 for 1 active reps approxLL diffs: (229.94,229.94) iter 10: time=306.24 for 1 active reps approxLL diffs: (147.95,147.95) iter 11: time=325.26 for 1 active reps approxLL diffs: (110.59,110.59) iter 12: time=319.95 for 1 active reps approxLL diffs: (90.93,90.93) iter 13: time=318.46 for 1 active reps approxLL diffs: (68.26,68.26) iter 14: time=313.62 for 1 active reps approxLL diffs: (61.30,61.30) iter 15: time=339.82 for 1 active reps approxLL diffs: (57.33,57.33) iter 16: time=328.35 for 1 active reps approxLL diffs: (42.12,42.12) iter 17: time=331.57 for 1 active reps approxLL diffs: (33.20,33.20) iter 18: time=331.51 for 1 active reps approxLL diffs: (32.94,32.94) iter 19: time=329.99 for 1 active reps approxLL diffs: (33.58,33.58) iter 20: time=339.66 for 1 active reps approxLL diffs: (32.93,32.93) iter 21: time=322.83 for 1 active reps approxLL diffs: (20.51,20.51) iter 22: time=327.15 for 1 active reps approxLL diffs: (15.74,15.74) iter 23: time=333.54 for 1 active reps approxLL diffs: (15.95,15.95) iter 24: time=311.98 for 1 active reps approxLL diffs: (16.43,16.43) iter 25: time=294.57 for 1 active reps approxLL diffs: (13.24,13.24) iter 26: time=314.77 for 1 active reps approxLL diffs: (9.21,9.21) iter 27: time=298.96 for 1 active reps approxLL diffs: (7.29,7.29) iter 28: time=289.83 for 1 active reps approxLL diffs: (5.66,5.66) iter 29: time=294.52 for 1 active reps approxLL diffs: (5.05,5.05) iter 30: time=301.61 for 1 active reps approxLL diffs: (4.34,4.34) iter 31: time=309.89 for 1 active reps approxLL diffs: (4.27,4.27) iter 32: time=314.74 for 1 active reps approxLL diffs: (3.79,3.79) iter 33: time=328.90 for 1 active reps approxLL diffs: (3.37,3.37) iter 34: time=333.28 for 1 active reps approxLL diffs: (3.26,3.26) iter 35: time=322.62 for 1 active reps approxLL diffs: (4.07,4.07) iter 36: time=323.87 for 1 active reps approxLL diffs: (2.80,2.80) iter 37: time=317.83 for 1 active reps approxLL diffs: (2.21,2.21) iter 38: time=293.00 for 1 active reps approxLL diffs: (2.93,2.93) iter 39: time=293.44 for 1 active reps approxLL diffs: (2.91,2.91) iter 40: time=291.71 for 1 active reps approxLL diffs: (1.37,1.37) iter 41: time=310.90 for 1 active reps approxLL diffs: (1.41,1.41) iter 42: time=286.18 for 1 active reps approxLL diffs: (1.99,1.99) iter 43: time=292.52 for 1 active reps approxLL diffs: (1.99,1.99) iter 44: time=297.98 for 1 active reps approxLL diffs: (1.54,1.54) iter 45: time=275.03 for 1 active reps approxLL diffs: (1.22,1.22) iter 46: time=299.98 for 1 active reps approxLL diffs: (0.99,0.99) iter 47: time=298.15 for 1 active reps approxLL diffs: (0.75,0.75) iter 48: time=283.29 for 1 active reps approxLL diffs: (0.59,0.59) iter 49: time=280.37 for 1 active reps approxLL diffs: (0.55,0.55) iter 50: time=278.24 for 1 active reps approxLL diffs: (0.47,0.47) iter 51: time=259.82 for 1 active reps approxLL diffs: (0.35,0.35) iter 52: time=260.39 for 1 active reps approxLL diffs: (0.26,0.26) iter 53: time=237.32 for 1 active reps approxLL diffs: (0.20,0.20) iter 54: time=212.03 for 1 active reps approxLL diffs: (0.17,0.17) iter 55: time=209.20 for 1 active reps approxLL diffs: (0.15,0.15) iter 56: time=210.81 for 1 active reps approxLL diffs: (0.13,0.13) iter 57: time=208.58 for 1 active reps approxLL diffs: (0.11,0.11) iter 58: time=208.05 for 1 active reps approxLL diffs: (0.09,0.09) iter 59: time=207.52 for 1 active reps approxLL diffs: (0.07,0.07) iter 60: time=214.64 for 1 active reps approxLL diffs: (0.05,0.05) iter 61: time=217.09 for 1 active reps approxLL diffs: (0.05,0.05) iter 62: time=221.60 for 1 active reps approxLL diffs: (0.04,0.04) iter 63: time=233.61 for 1 active reps approxLL diffs: (0.04,0.04) iter 64: time=224.62 for 1 active reps approxLL diffs: (0.05,0.05) iter 65: time=229.06 for 1 active reps approxLL diffs: (0.07,0.07) iter 66: time=239.04 for 1 active reps approxLL diffs: (0.08,0.08) iter 67: time=254.63 for 1 active reps approxLL diffs: (0.07,0.07) iter 68: time=287.96 for 1 active reps approxLL diffs: (0.05,0.05) iter 69: time=287.67 for 1 active reps approxLL diffs: (0.04,0.04) iter 70: time=285.14 for 1 active reps approxLL diffs: (0.06,0.06) iter 71: time=273.72 for 1 active reps approxLL diffs: (0.12,0.12) iter 72: time=268.29 for 1 active reps approxLL diffs: (0.23,0.23) iter 73: time=272.99 for 1 active reps approxLL diffs: (0.23,0.23) iter 74: time=255.85 for 1 active reps approxLL diffs: (0.23,0.23) iter 75: time=252.60 for 1 active reps approxLL diffs: (0.61,0.61) iter 76: time=251.01 for 1 active reps approxLL diffs: (1.22,1.22) iter 77: time=248.72 for 1 active reps approxLL diffs: (0.50,0.50) iter 78: time=255.55 for 1 active reps approxLL diffs: (0.32,0.32) iter 79: time=271.97 for 1 active reps approxLL diffs: (0.27,0.27) iter 80: time=269.60 for 1 active reps approxLL diffs: (0.16,0.16) iter 81: time=270.02 for 1 active reps approxLL diffs: (0.08,0.08) iter 82: time=278.18 for 1 active reps approxLL diffs: (0.05,0.05) iter 83: time=283.36 for 1 active reps approxLL diffs: (0.04,0.04) iter 84: time=273.45 for 1 active reps approxLL diffs: (0.03,0.03) iter 85: time=270.32 for 1 active reps approxLL diffs: (0.02,0.02) iter 86: time=283.06 for 1 active reps approxLL diffs: (0.01,0.01) iter 87: time=295.33 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 87: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 56.5%, memory/overhead = 43.5% Time for computing and writing betas = 24522.6 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.76073 (705850 good SNPs) lambdaGC: 1.4192 Mean BOLT_LMM_INF: 3.05918 (705850 good SNPs) lambdaGC: 1.44112 Mean BOLT_LMM: 3.20834 (705850 good SNPs) lambdaGC: 1.45467 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 7959.47 sec Total elapsed time for analysis = 180513 sec