+-----------------------------+ | ___ | | 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_LYMPHOCYTE_COUNT \ --covarFile=/n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz \ --covarCol=cov_ASSESS_CENTER \ --covarCol=cov_GENO_ARRAY \ --covarCol=cov_SEX \ --covarMaxLevels=30 \ --qCovarCol=cov_AGE \ --qCovarCol=cov_AGE_SQ \ --qCovarCol=PC{1:20} \ --LDscoresFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/LDSCORE.1000G_EUR.tab.gz \ --geneticMapFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz \ --lmmForceNonInf \ --numThreads=8 \ --predBetasFile=bolt_460K_selfRepWhite.blood_LYMPHOCYTE_COUNT.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_LYMPHOCYTE_COUNT.stats.gz \ --verboseStats Setting number of threads to 8 fam: /n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam bim(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim bed(s): /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed === Reading genotype data === Total indivs in PLINK data: Nbed = 488377 Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt Removed 976 individual(s) Reading remove file (indivs to remove): /n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt WARNING: Unable to find individual to remove: FID IID WARNING: Unable to find individual to remove: 6023494 6023494 WARNING: Unable to find individual to remove: 6022857 6022857 WARNING: Unable to find individual to remove: 6020026 6020026 WARNING: Unable to find individual to remove: 6017119 6017119 Removed 28074 individual(s) WARNING: 1684 individual(s) not found in data set Total indivs stored in memory: N = 459327 Reading bim file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bim Read 63487 snps Reading bim file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bim Read 61966 snps Reading bim file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bim Read 52300 snps Reading bim file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bim Read 47443 snps Reading bim file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bim Read 46314 snps Reading bim file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bim Read 53695 snps Reading bim file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bim Read 42722 snps Reading bim file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bim Read 38591 snps Reading bim file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bim Read 34310 snps Reading bim file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bim Read 38308 snps Reading bim file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bim Read 40824 snps Reading bim file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bim Read 37302 snps Reading bim file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bim Read 26806 snps Reading bim file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bim Read 25509 snps Reading bim file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bim Read 24467 snps Reading bim file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bim Read 28960 snps Reading bim file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bim Read 28835 snps Reading bim file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bim Read 21962 snps Reading bim file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bim Read 26186 snps Reading bim file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bim Read 19959 snps Reading bim file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bim Read 11342 snps Reading bim file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bim Read 12968 snps Reading bim file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bim Read 20214 snps Total snps in PLINK data: Mbed = 804470 Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt Excluded 73451 SNP(s) Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89009265 WARNING: Unable to find SNP to exclude: Affx-89005343 WARNING: Unable to find SNP to exclude: Affx-89020964 WARNING: Unable to find SNP to exclude: Affx-89013736 Excluded 16710 SNP(s) WARNING: 342 SNP(s) not found in data set Reading exclude file (SNPs to exclude): /n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt WARNING: Unable to find SNP to exclude: rs1800865 WARNING: Unable to find SNP to exclude: Affx-89022776 WARNING: Unable to find SNP to exclude: Affx-89017694 WARNING: Unable to find SNP to exclude: Affx-89018603 WARNING: Unable to find SNP to exclude: Affx-79443721 Excluded 8428 SNP(s) WARNING: 112 SNP(s) not found in data set Breakdown of SNP pre-filtering results: 705881 SNPs to include in model (i.e., GRM) 0 additional non-GRM SNPs loaded 98589 excluded SNPs Filling in genetic map coordinates using reference file: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz Allocating 705881 x 459328/4 bytes to store genotypes Reading genotypes and performing QC filtering on snps and indivs... Reading bed file #1: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr1.bed Expecting 7751445265 (+3) bytes for 488377 indivs, 63487 snps Reading bed file #2: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr2.bed Expecting 7565738770 (+3) bytes for 488377 indivs, 61966 snps Reading bed file #3: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr3.bed Expecting 6385568500 (+3) bytes for 488377 indivs, 52300 snps Reading bed file #4: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr4.bed Expecting 5792553085 (+3) bytes for 488377 indivs, 47443 snps Reading bed file #5: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr5.bed Expecting 5654707830 (+3) bytes for 488377 indivs, 46314 snps Reading bed file #6: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr6.bed Expecting 6555891025 (+3) bytes for 488377 indivs, 53695 snps Reading bed file #7: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr7.bed Expecting 5216142590 (+3) bytes for 488377 indivs, 42722 snps Reading bed file #8: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr8.bed Expecting 4711768145 (+3) bytes for 488377 indivs, 38591 snps Reading bed file #9: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr9.bed Expecting 4189079450 (+3) bytes for 488377 indivs, 34310 snps Reading bed file #10: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr10.bed Expecting 4677215260 (+3) bytes for 488377 indivs, 38308 snps Reading bed file #11: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr11.bed Expecting 4984406280 (+3) bytes for 488377 indivs, 40824 snps Reading bed file #12: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr12.bed Expecting 4554387690 (+3) bytes for 488377 indivs, 37302 snps Reading bed file #13: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr13.bed Expecting 3272878570 (+3) bytes for 488377 indivs, 26806 snps Reading bed file #14: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr14.bed Expecting 3114521355 (+3) bytes for 488377 indivs, 25509 snps Reading bed file #15: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr15.bed Expecting 2987298365 (+3) bytes for 488377 indivs, 24467 snps Reading bed file #16: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr16.bed Expecting 3535871200 (+3) bytes for 488377 indivs, 28960 snps Reading bed file #17: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr17.bed Expecting 3520609325 (+3) bytes for 488377 indivs, 28835 snps Reading bed file #18: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr18.bed Expecting 2681450390 (+3) bytes for 488377 indivs, 21962 snps Reading bed file #19: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr19.bed Expecting 3197179670 (+3) bytes for 488377 indivs, 26186 snps Reading bed file #20: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr20.bed Expecting 2436894105 (+3) bytes for 488377 indivs, 19959 snps Reading bed file #21: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr21.bed Expecting 1384801490 (+3) bytes for 488377 indivs, 11342 snps Reading bed file #22: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr22.bed Expecting 1583327960 (+3) bytes for 488377 indivs, 12968 snps Reading bed file #23: /n/groups/price/UKBiobank/download_500K/../qc0_500K/chr23.bed Expecting 2468028330 (+3) bytes for 488377 indivs, 20214 snps Total indivs after QC: 459327 Total post-QC SNPs: M = 705881 Variance component 1: 705881 post-QC SNPs (name: 'modelSnps') Time for SnpData setup = 2817.37 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: 443940 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 = 443940 Singular values of covariate matrix: S[0] = 2.26834e+06 S[1] = 4762.02 S[2] = 469.403 S[3] = 293.277 S[4] = 199.871 S[5] = 194.381 S[6] = 183.598 S[7] = 174.991 S[8] = 170.572 S[9] = 165.159 S[10] = 162.702 S[11] = 153.551 S[12] = 145.477 S[13] = 143.508 S[14] = 140.473 S[15] = 135.677 S[16] = 132.487 S[17] = 130.203 S[18] = 126.157 S[19] = 116.28 S[20] = 112.115 S[21] = 99.8473 S[22] = 44.8261 S[23] = 23.9598 S[24] = 19.3037 S[25] = 0.983101 S[26] = 0.98303 S[27] = 0.98291 S[28] = 0.982685 S[29] = 0.982606 S[30] = 0.982493 S[31] = 0.982457 S[32] = 0.9823 S[33] = 0.982213 S[34] = 0.981939 S[35] = 0.981893 S[36] = 0.98178 S[37] = 0.981741 S[38] = 0.981583 S[39] = 0.981349 S[40] = 0.981132 S[41] = 0.981028 S[42] = 0.980919 S[43] = 0.963685 S[44] = 0.887566 S[45] = 7.91003e-12 S[46] = 7.80755e-13 S[47] = 2.26747e-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: 441353.894921 Dimension of all-1s proj space (Nused-1): 443939 Time for covariate data setup + Bolt initialization = 4576.93 sec Phenotype 1: N = 443940 mean = -0.0166832 std = 0.99424 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 484.212 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 443940) 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=395.83 rNorms/orig: (0.6,0.7) res2s: 721965..148364 iter 2: time=380.04 rNorms/orig: (0.5,0.6) res2s: 866307..189427 iter 3: time=377.47 rNorms/orig: (0.3,0.4) res2s: 994490..225341 iter 4: time=376.16 rNorms/orig: (0.2,0.2) res2s: 1.03849e+06..240873 iter 5: time=372.27 rNorms/orig: (0.1,0.2) res2s: 1.0645e+06..248917 iter 6: time=384.16 rNorms/orig: (0.09,0.1) res2s: 1.0763e+06..252502 iter 7: time=406.02 rNorms/orig: (0.06,0.06) res2s: 1.0819e+06..254683 iter 8: time=414.19 rNorms/orig: (0.04,0.05) res2s: 1.08472e+06..255523 iter 9: time=439.52 rNorms/orig: (0.02,0.03) res2s: 1.08607e+06..255942 iter 10: time=395.04 rNorms/orig: (0.02,0.02) res2s: 1.08668e+06..256158 iter 11: time=395.55 rNorms/orig: (0.01,0.01) res2s: 1.08698e+06..256243 iter 12: time=395.44 rNorms/orig: (0.006,0.008) res2s: 1.08711e+06..256288 iter 13: time=413.27 rNorms/orig: (0.004,0.005) res2s: 1.08717e+06..256305 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 48.8%, memory/overhead = 51.2% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.0517741 Estimating MC scaling f_REML at log(delta) = -0.00574101, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=400.75 rNorms/orig: (1,1) res2s: 76660.7..44200.4 iter 2: time=454.03 rNorms/orig: (1,1) res2s: 112577..68634.7 iter 3: time=423.68 rNorms/orig: (0.8,0.9) res2s: 169616..102263 iter 4: time=422.54 rNorms/orig: (0.6,0.7) res2s: 203369..124933 iter 5: time=439.59 rNorms/orig: (0.5,0.5) res2s: 232338..141983 iter 6: time=411.49 rNorms/orig: (0.4,0.4) res2s: 252144..152726 iter 7: time=422.82 rNorms/orig: (0.3,0.3) res2s: 266238..162103 iter 8: time=443.99 rNorms/orig: (0.2,0.3) res2s: 276525..167323 iter 9: time=451.88 rNorms/orig: (0.2,0.2) res2s: 283405..170990 iter 10: time=381.29 rNorms/orig: (0.1,0.2) res2s: 287859..173694 iter 11: time=405.94 rNorms/orig: (0.1,0.1) res2s: 291056..175234 iter 12: time=403.00 rNorms/orig: (0.09,0.1) res2s: 293044..176377 iter 13: time=446.27 rNorms/orig: (0.07,0.07) res2s: 294283..177021 iter 14: time=402.84 rNorms/orig: (0.05,0.06) res2s: 295140..177494 iter 15: time=424.36 rNorms/orig: (0.04,0.04) res2s: 295694..177774 iter 16: time=452.36 rNorms/orig: (0.03,0.03) res2s: 296041..177948 iter 17: time=427.22 rNorms/orig: (0.02,0.03) res2s: 296236..178066 iter 18: time=384.38 rNorms/orig: (0.02,0.02) res2s: 296388..178141 iter 19: time=381.10 rNorms/orig: (0.01,0.02) res2s: 296480..178189 iter 20: time=382.54 rNorms/orig: (0.01,0.01) res2s: 296536..178216 iter 21: time=385.45 rNorms/orig: (0.009,0.009) res2s: 296569..178234 iter 22: time=380.60 rNorms/orig: (0.007,0.007) res2s: 296590..178246 iter 23: time=379.07 rNorms/orig: (0.005,0.005) res2s: 296603..178254 iter 24: time=378.90 rNorms/orig: (0.004,0.004) res2s: 296611..178258 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 48.3%, memory/overhead = 51.7% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.267834 Estimating MC scaling f_REML at log(delta) = 0.914904, h2 = 0.284826... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=376.87 rNorms/orig: (0.7,0.8) res2s: 528322..126953 iter 2: time=385.10 rNorms/orig: (0.6,0.7) res2s: 655167..167185 iter 3: time=381.40 rNorms/orig: (0.4,0.4) res2s: 780203..205321 iter 4: time=384.25 rNorms/orig: (0.2,0.3) res2s: 827145..223169 iter 5: time=389.64 rNorms/orig: (0.2,0.2) res2s: 856730..233066 iter 6: time=391.98 rNorms/orig: (0.1,0.1) res2s: 871191..237778 iter 7: time=387.97 rNorms/orig: (0.08,0.09) res2s: 878602..240853 iter 8: time=392.65 rNorms/orig: (0.05,0.06) res2s: 882632..242127 iter 9: time=392.03 rNorms/orig: (0.04,0.04) res2s: 884681..242808 iter 10: time=395.59 rNorms/orig: (0.02,0.03) res2s: 885670..243184 iter 11: time=381.04 rNorms/orig: (0.02,0.02) res2s: 886198..243344 iter 12: time=373.17 rNorms/orig: (0.01,0.01) res2s: 886447..243433 iter 13: time=374.65 rNorms/orig: (0.008,0.008) res2s: 886564..243471 iter 14: time=376.99 rNorms/orig: (0.005,0.006) res2s: 886625..243492 iter 15: time=379.84 rNorms/orig: (0.003,0.004) res2s: 886654..243501 Converged at iter 15: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 48.1%, memory/overhead = 51.9% MCscaling: logDelta = 0.91, h2 = 0.285, f = 0.000954606 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.285 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.275408, logDelta = 0.914904, f = 0.000954606 Time for fitting variance components = 21559.2 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=789.77 rNorms/orig: (0.5,0.9) res2s: 126681..221609 iter 2: time=762.22 rNorms/orig: (0.5,0.8) res2s: 167739..260352 iter 3: time=718.53 rNorms/orig: (0.3,0.5) res2s: 207156..279055 iter 4: time=714.77 rNorms/orig: (0.2,0.3) res2s: 226567..287204 iter 5: time=718.29 rNorms/orig: (0.1,0.2) res2s: 237422..292431 iter 6: time=715.34 rNorms/orig: (0.08,0.2) res2s: 242637..294907 iter 7: time=722.26 rNorms/orig: (0.06,0.1) res2s: 246230..296249 iter 8: time=721.64 rNorms/orig: (0.04,0.08) res2s: 247743..296987 iter 9: time=724.36 rNorms/orig: (0.02,0.05) res2s: 248566..297327 iter 10: time=738.49 rNorms/orig: (0.02,0.03) res2s: 249048..297509 iter 11: time=724.10 rNorms/orig: (0.01,0.02) res2s: 249252..297598 iter 12: time=724.30 rNorms/orig: (0.008,0.02) res2s: 249369..297642 iter 13: time=734.90 rNorms/orig: (0.005,0.01) res2s: 249421..297663 iter 14: time=720.34 rNorms/orig: (0.003,0.007) res2s: 249451..297674 iter 15: time=715.42 rNorms/orig: (0.002,0.005) res2s: 249464..297679 iter 16: time=712.84 rNorms/orig: (0.001,0.003) res2s: 249471..297681 iter 17: time=720.18 rNorms/orig: (0.0008,0.002) res2s: 249474..297682 iter 18: time=719.07 rNorms/orig: (0.0005,0.001) res2s: 249476..297683 iter 19: time=711.07 rNorms/orig: (0.0003,0.001) res2s: 249476..297683 iter 20: time=721.37 rNorms/orig: (0.0002,0.0007) res2s: 249477..297683 iter 21: time=708.52 rNorms/orig: (0.0001,0.0005) res2s: 249477..297683 Converged at iter 21: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 73.2%, memory/overhead = 26.8% AvgPro: 1.531 AvgRetro: 1.512 Calibration: 1.012 (0.001) (30 SNPs) Ratio of medians: 1.014 Median of ratios: 1.012 Time for computing infinitesimal model assoc stats = 15735.2 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 17.1212 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 > 443.9) # of SNPs remaining after outlier window removal: 576722/579570 Intercept of LD Score regression for ref stats: 1.167 (0.018) Estimated attenuation: 0.141 (0.016) Intercept of LD Score regression for cur stats: 1.175 (0.016) Calibration factor (ref/cur) to multiply by: 0.993 (0.003) LINREG intercept inflation = 1.00688 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 = 355152 Singular values of covariate matrix: S[0] = 2.02865e+06 S[1] = 4259.67 S[2] = 419.852 S[3] = 262.328 S[4] = 178.754 S[5] = 173.829 S[6] = 164.239 S[7] = 156.448 S[8] = 152.509 S[9] = 147.562 S[10] = 145.399 S[11] = 137.349 S[12] = 130.205 S[13] = 128.425 S[14] = 125.858 S[15] = 121.446 S[16] = 118.581 S[17] = 116.666 S[18] = 112.781 S[19] = 104.023 S[20] = 100.017 S[21] = 89.4656 S[22] = 39.9302 S[23] = 21.2197 S[24] = 17.2713 S[25] = 0.881829 S[26] = 0.881191 S[27] = 0.880309 S[28] = 0.879916 S[29] = 0.879473 S[30] = 0.879208 S[31] = 0.878646 S[32] = 0.878485 S[33] = 0.878327 S[34] = 0.877922 S[35] = 0.877672 S[36] = 0.877202 S[37] = 0.877109 S[38] = 0.876708 S[39] = 0.876539 S[40] = 0.87585 S[41] = 0.875452 S[42] = 0.875286 S[43] = 0.859769 S[44] = 0.79478 S[45] = 5.42254e-12 S[46] = 6.0277e-13 S[47] = 1.76716e-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: 353075.244161 Dimension of all-1s proj space (Nused-1): 355151 Beginning variational Bayes iter 1: time=694.35 for 18 active reps iter 2: time=441.17 for 18 active reps approxLL diffs: (9892.32,12113.49) iter 3: time=443.20 for 18 active reps approxLL diffs: (920.77,1976.60) iter 4: time=439.49 for 18 active reps approxLL diffs: (169.25,709.03) iter 5: time=460.53 for 18 active reps approxLL diffs: (48.99,357.29) iter 6: time=470.43 for 18 active reps approxLL diffs: (19.95,203.18) iter 7: time=454.59 for 18 active reps approxLL diffs: (10.19,121.35) iter 8: time=445.74 for 18 active reps approxLL diffs: (5.95,85.26) iter 9: time=436.55 for 18 active reps approxLL diffs: (3.79,58.46) iter 10: time=455.96 for 18 active reps approxLL diffs: (2.63,40.05) iter 11: time=455.78 for 18 active reps approxLL diffs: (1.93,31.79) iter 12: time=459.74 for 18 active reps approxLL diffs: (1.42,27.25) iter 13: time=460.68 for 18 active reps approxLL diffs: (1.05,22.33) iter 14: time=461.11 for 18 active reps approxLL diffs: (0.80,14.62) iter 15: time=469.31 for 18 active reps approxLL diffs: (0.58,13.53) iter 16: time=475.74 for 18 active reps approxLL diffs: (0.42,10.86) iter 17: time=489.55 for 18 active reps approxLL diffs: (0.31,11.08) iter 18: time=448.49 for 18 active reps approxLL diffs: (0.23,7.09) iter 19: time=452.44 for 18 active reps approxLL diffs: (0.18,4.95) iter 20: time=478.91 for 18 active reps approxLL diffs: (0.13,4.53) iter 21: time=501.11 for 18 active reps approxLL diffs: (0.10,3.25) iter 22: time=519.43 for 18 active reps approxLL diffs: (0.08,4.29) iter 23: time=519.31 for 18 active reps approxLL diffs: (0.06,7.48) iter 24: time=491.02 for 18 active reps approxLL diffs: (0.05,3.51) iter 25: time=490.53 for 18 active reps approxLL diffs: (0.04,4.46) iter 26: time=502.06 for 18 active reps approxLL diffs: (0.03,4.02) iter 27: time=508.64 for 18 active reps approxLL diffs: (0.02,3.06) iter 28: time=485.13 for 18 active reps approxLL diffs: (0.02,2.69) iter 29: time=471.55 for 18 active reps approxLL diffs: (0.02,3.15) iter 30: time=464.17 for 18 active reps approxLL diffs: (0.01,2.18) iter 31: time=488.16 for 18 active reps approxLL diffs: (0.01,1.70) iter 32: time=481.00 for 18 active reps approxLL diffs: (0.01,3.15) iter 33: time=449.47 for 17 active reps approxLL diffs: (0.01,4.49) iter 34: time=448.69 for 17 active reps approxLL diffs: (0.01,2.06) iter 35: time=448.36 for 17 active reps approxLL diffs: (0.01,1.43) iter 36: time=437.48 for 15 active reps approxLL diffs: (0.01,0.57) iter 37: time=437.96 for 15 active reps approxLL diffs: (0.01,0.83) iter 38: time=411.52 for 14 active reps approxLL diffs: (0.01,3.41) iter 39: time=396.70 for 13 active reps approxLL diffs: (0.01,2.36) iter 40: time=371.88 for 12 active reps approxLL diffs: (0.01,0.78) iter 41: time=386.67 for 11 active reps approxLL diffs: (0.01,0.62) iter 42: time=380.94 for 11 active reps approxLL diffs: (0.01,0.37) iter 43: time=385.35 for 11 active reps approxLL diffs: (0.01,0.45) iter 44: time=369.24 for 10 active reps approxLL diffs: (0.03,0.77) iter 45: time=364.67 for 10 active reps approxLL diffs: (0.05,0.47) iter 46: time=369.63 for 10 active reps approxLL diffs: (0.04,0.40) iter 47: time=366.34 for 10 active reps approxLL diffs: (0.01,0.93) iter 48: time=361.81 for 10 active reps approxLL diffs: (0.00,0.60) iter 49: time=345.25 for 9 active reps approxLL diffs: (0.02,0.18) iter 50: time=362.98 for 9 active reps approxLL diffs: (0.00,0.28) iter 51: time=322.64 for 8 active reps approxLL diffs: (0.02,0.34) iter 52: time=324.85 for 8 active reps approxLL diffs: (0.01,0.20) iter 53: time=336.12 for 8 active reps approxLL diffs: (0.01,0.16) iter 54: time=308.63 for 8 active reps approxLL diffs: (0.01,0.67) iter 55: time=302.35 for 6 active reps approxLL diffs: (0.01,2.70) iter 56: time=308.44 for 6 active reps approxLL diffs: (0.01,1.81) iter 57: time=268.31 for 4 active reps approxLL diffs: (0.01,0.71) iter 58: time=283.03 for 4 active reps approxLL diffs: (0.01,0.32) iter 59: time=296.91 for 4 active reps approxLL diffs: (0.01,0.17) iter 60: time=312.10 for 4 active reps approxLL diffs: (0.01,0.09) iter 61: time=294.32 for 4 active reps approxLL diffs: (0.02,0.09) iter 62: time=265.64 for 4 active reps approxLL diffs: (0.02,0.07) iter 63: time=266.19 for 4 active reps approxLL diffs: (0.01,0.03) iter 64: time=271.94 for 4 active reps approxLL diffs: (0.01,0.04) iter 65: time=295.30 for 3 active reps approxLL diffs: (0.01,0.05) iter 66: time=286.20 for 2 active reps approxLL diffs: (0.01,0.07) iter 67: time=263.14 for 1 active reps approxLL diffs: (0.07,0.07) iter 68: time=263.61 for 1 active reps approxLL diffs: (0.05,0.05) iter 69: time=267.69 for 1 active reps approxLL diffs: (0.02,0.02) iter 70: time=250.11 for 1 active reps approxLL diffs: (0.01,0.01) iter 71: time=252.22 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 71: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 75.1%, memory/overhead = 24.9% Computing predictions on left-out cross-validation fold Time for computing predictions = 9254.04 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.05: 0.144590 f2=0.5, p=0.02: 0.144071 f2=0.3, p=0.02: 0.143178 ... f2=0.5, p=0.5: 0.109594 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.982714 Absolute prediction MSE using standard LMM: 0.875014 Absolute prediction MSE, fold-best f2=0.3, p=0.05: 0.840623 Absolute pred MSE using f2=0.5, p=0.5: 0.875014 Absolute pred MSE using f2=0.5, p=0.2: 0.864192 Absolute pred MSE using f2=0.5, p=0.1: 0.853409 Absolute pred MSE using f2=0.5, p=0.05: 0.844676 Absolute pred MSE using f2=0.5, p=0.02: 0.841133 Absolute pred MSE using f2=0.5, p=0.01: 0.842117 Absolute pred MSE using f2=0.3, p=0.5: 0.871416 Absolute pred MSE using f2=0.3, p=0.2: 0.856271 Absolute pred MSE using f2=0.3, p=0.1: 0.845859 Absolute pred MSE using f2=0.3, p=0.05: 0.840623 Absolute pred MSE using f2=0.3, p=0.02: 0.842010 Absolute pred MSE using f2=0.3, p=0.01: 0.844895 Absolute pred MSE using f2=0.1, p=0.5: 0.865697 Absolute pred MSE using f2=0.1, p=0.2: 0.849911 Absolute pred MSE using f2=0.1, p=0.1: 0.842546 Absolute pred MSE using f2=0.1, p=0.05: 0.842912 Absolute pred MSE using f2=0.1, p=0.02: 0.850107 Absolute pred MSE using f2=0.1, p=0.01: 0.854906 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.110 Relative improvement in prediction MSE using non-inf model: 0.039 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.05 Time for estimating mixture parameters = 42085.1 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=785.17 for 23 active reps iter 2: time=541.82 for 23 active reps approxLL diffs: (14463.11,15472.67) iter 3: time=540.76 for 23 active reps approxLL diffs: (2329.52,2523.60) iter 4: time=538.15 for 23 active reps approxLL diffs: (703.81,796.08) iter 5: time=538.45 for 23 active reps approxLL diffs: (288.29,341.37) iter 6: time=567.66 for 23 active reps approxLL diffs: (140.62,177.83) iter 7: time=469.74 for 23 active reps approxLL diffs: (75.85,100.38) iter 8: time=476.42 for 23 active reps approxLL diffs: (46.35,60.04) iter 9: time=471.68 for 23 active reps approxLL diffs: (32.30,40.87) iter 10: time=469.86 for 23 active reps approxLL diffs: (25.72,30.79) iter 11: time=472.15 for 23 active reps approxLL diffs: (18.93,25.17) iter 12: time=469.96 for 23 active reps approxLL diffs: (14.85,20.90) iter 13: time=469.22 for 23 active reps approxLL diffs: (10.77,16.05) iter 14: time=466.39 for 23 active reps approxLL diffs: (7.94,13.40) iter 15: time=471.80 for 23 active reps approxLL diffs: (5.65,10.44) iter 16: time=470.49 for 23 active reps approxLL diffs: (3.57,8.16) iter 17: time=470.13 for 23 active reps approxLL diffs: (2.52,7.36) iter 18: time=471.72 for 23 active reps approxLL diffs: (2.12,5.76) iter 19: time=469.51 for 23 active reps approxLL diffs: (1.85,5.75) iter 20: time=470.15 for 23 active reps approxLL diffs: (1.62,4.20) iter 21: time=468.50 for 23 active reps approxLL diffs: (1.23,2.97) iter 22: time=469.79 for 23 active reps approxLL diffs: (1.20,2.47) iter 23: time=467.78 for 23 active reps approxLL diffs: (1.08,3.25) iter 24: time=467.37 for 23 active reps approxLL diffs: (0.56,2.75) iter 25: time=487.42 for 23 active reps approxLL diffs: (0.51,3.02) iter 26: time=514.40 for 23 active reps approxLL diffs: (0.22,2.69) iter 27: time=525.83 for 23 active reps approxLL diffs: (0.17,2.03) iter 28: time=514.10 for 23 active reps approxLL diffs: (0.21,1.70) iter 29: time=517.92 for 23 active reps approxLL diffs: (0.22,1.70) iter 30: time=520.94 for 23 active reps approxLL diffs: (0.16,1.57) iter 31: time=518.67 for 23 active reps approxLL diffs: (0.14,1.40) iter 32: time=524.36 for 23 active reps approxLL diffs: (0.09,1.53) iter 33: time=518.62 for 23 active reps approxLL diffs: (0.07,0.63) iter 34: time=515.50 for 23 active reps approxLL diffs: (0.07,1.15) iter 35: time=513.69 for 23 active reps approxLL diffs: (0.06,1.32) iter 36: time=524.50 for 23 active reps approxLL diffs: (0.06,0.84) iter 37: time=520.71 for 23 active reps approxLL diffs: (0.04,0.48) iter 38: time=519.73 for 23 active reps approxLL diffs: (0.03,1.03) iter 39: time=511.28 for 23 active reps approxLL diffs: (0.03,1.50) iter 40: time=515.18 for 23 active reps approxLL diffs: (0.03,0.86) iter 41: time=516.62 for 23 active reps approxLL diffs: (0.03,0.88) iter 42: time=508.51 for 23 active reps approxLL diffs: (0.02,0.48) iter 43: time=518.25 for 23 active reps approxLL diffs: (0.02,0.66) iter 44: time=517.72 for 23 active reps approxLL diffs: (0.02,1.00) iter 45: time=515.34 for 23 active reps approxLL diffs: (0.01,1.53) iter 46: time=543.62 for 23 active reps approxLL diffs: (0.00,0.93) iter 47: time=465.98 for 20 active reps approxLL diffs: (0.01,1.14) iter 48: time=469.03 for 18 active reps approxLL diffs: (0.01,0.63) iter 49: time=451.41 for 17 active reps approxLL diffs: (0.01,0.16) iter 50: time=431.71 for 17 active reps approxLL diffs: (0.01,0.07) iter 51: time=430.41 for 15 active reps approxLL diffs: (0.01,0.06) iter 52: time=431.45 for 15 active reps approxLL diffs: (0.01,0.20) iter 53: time=405.19 for 14 active reps approxLL diffs: (0.01,0.86) iter 54: time=360.51 for 12 active reps approxLL diffs: (0.01,1.38) iter 55: time=370.65 for 10 active reps approxLL diffs: (0.01,0.90) iter 56: time=301.30 for 8 active reps approxLL diffs: (0.01,0.37) iter 57: time=317.71 for 7 active reps approxLL diffs: (0.01,0.13) iter 58: time=329.51 for 7 active reps approxLL diffs: (0.00,0.10) iter 59: time=305.90 for 6 active reps approxLL diffs: (0.01,0.14) iter 60: time=318.07 for 6 active reps approxLL diffs: (0.01,0.20) iter 61: time=313.97 for 6 active reps approxLL diffs: (0.01,0.20) iter 62: time=300.51 for 6 active reps approxLL diffs: (0.01,0.17) iter 63: time=306.83 for 6 active reps approxLL diffs: (0.01,0.28) iter 64: time=299.10 for 6 active reps approxLL diffs: (0.01,0.43) iter 65: time=259.18 for 4 active reps approxLL diffs: (0.02,0.41) iter 66: time=268.37 for 4 active reps approxLL diffs: (0.03,0.13) iter 67: time=266.14 for 4 active reps approxLL diffs: (0.01,0.03) iter 68: time=266.66 for 4 active reps approxLL diffs: (0.00,0.03) iter 69: time=268.96 for 3 active reps approxLL diffs: (0.00,0.02) iter 70: time=254.90 for 2 active reps approxLL diffs: (0.01,0.02) iter 71: time=263.74 for 2 active reps approxLL diffs: (0.01,0.01) iter 72: time=251.65 for 2 active reps approxLL diffs: (0.01,0.01) Converged at iter 72: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 79.7%, memory/overhead = 20.3% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 443.9) # of SNPs remaining after outlier window removal: 576722/579570 Intercept of LD Score regression for ref stats: 1.167 (0.018) Estimated attenuation: 0.141 (0.016) Intercept of LD Score regression for cur stats: 1.164 (0.018) Calibration factor (ref/cur) to multiply by: 1.003 (0.001) Time for computing Bayesian mixed model assoc stats = 32530 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=451.26 for 1 active reps iter 2: time=202.71 for 1 active reps approxLL diffs: (15526.98,15526.98) iter 3: time=208.41 for 1 active reps approxLL diffs: (2514.66,2514.66) iter 4: time=209.21 for 1 active reps approxLL diffs: (790.52,790.52) iter 5: time=214.71 for 1 active reps approxLL diffs: (337.86,337.86) iter 6: time=211.23 for 1 active reps approxLL diffs: (171.84,171.84) iter 7: time=209.02 for 1 active reps approxLL diffs: (91.77,91.77) iter 8: time=213.16 for 1 active reps approxLL diffs: (56.37,56.37) iter 9: time=204.02 for 1 active reps approxLL diffs: (38.05,38.05) iter 10: time=204.20 for 1 active reps approxLL diffs: (30.13,30.13) iter 11: time=202.57 for 1 active reps approxLL diffs: (24.21,24.21) iter 12: time=215.11 for 1 active reps approxLL diffs: (18.81,18.81) iter 13: time=221.24 for 1 active reps approxLL diffs: (13.92,13.92) iter 14: time=214.05 for 1 active reps approxLL diffs: (9.39,9.39) iter 15: time=207.94 for 1 active reps approxLL diffs: (7.23,7.23) iter 16: time=213.18 for 1 active reps approxLL diffs: (6.06,6.06) iter 17: time=222.78 for 1 active reps approxLL diffs: (4.20,4.20) iter 18: time=222.54 for 1 active reps approxLL diffs: (4.12,4.12) iter 19: time=212.37 for 1 active reps approxLL diffs: (4.07,4.07) iter 20: time=211.91 for 1 active reps approxLL diffs: (2.33,2.33) iter 21: time=219.75 for 1 active reps approxLL diffs: (1.56,1.56) iter 22: time=220.77 for 1 active reps approxLL diffs: (1.50,1.50) iter 23: time=213.97 for 1 active reps approxLL diffs: (1.60,1.60) iter 24: time=215.87 for 1 active reps approxLL diffs: (1.37,1.37) iter 25: time=220.94 for 1 active reps approxLL diffs: (0.90,0.90) iter 26: time=208.82 for 1 active reps approxLL diffs: (0.85,0.85) iter 27: time=204.61 for 1 active reps approxLL diffs: (0.74,0.74) iter 28: time=211.89 for 1 active reps approxLL diffs: (0.51,0.51) iter 29: time=215.14 for 1 active reps approxLL diffs: (0.37,0.37) iter 30: time=205.95 for 1 active reps approxLL diffs: (0.36,0.36) iter 31: time=207.62 for 1 active reps approxLL diffs: (0.46,0.46) iter 32: time=199.09 for 1 active reps approxLL diffs: (0.71,0.71) iter 33: time=202.04 for 1 active reps approxLL diffs: (1.20,1.20) iter 34: time=207.10 for 1 active reps approxLL diffs: (1.68,1.68) iter 35: time=198.27 for 1 active reps approxLL diffs: (1.10,1.10) iter 36: time=198.96 for 1 active reps approxLL diffs: (0.61,0.61) iter 37: time=208.70 for 1 active reps approxLL diffs: (0.39,0.39) iter 38: time=204.89 for 1 active reps approxLL diffs: (0.19,0.19) iter 39: time=206.48 for 1 active reps approxLL diffs: (0.09,0.09) iter 40: time=226.16 for 1 active reps approxLL diffs: (0.06,0.06) iter 41: time=198.13 for 1 active reps approxLL diffs: (0.05,0.05) iter 42: time=214.90 for 1 active reps approxLL diffs: (0.05,0.05) iter 43: time=198.77 for 1 active reps approxLL diffs: (0.05,0.05) iter 44: time=226.59 for 1 active reps approxLL diffs: (0.05,0.05) iter 45: time=218.73 for 1 active reps approxLL diffs: (0.05,0.05) iter 46: time=218.16 for 1 active reps approxLL diffs: (0.06,0.06) iter 47: time=223.05 for 1 active reps approxLL diffs: (0.08,0.08) iter 48: time=218.20 for 1 active reps approxLL diffs: (0.11,0.11) iter 49: time=214.01 for 1 active reps approxLL diffs: (0.14,0.14) iter 50: time=200.65 for 1 active reps approxLL diffs: (0.14,0.14) iter 51: time=201.52 for 1 active reps approxLL diffs: (0.12,0.12) iter 52: time=210.52 for 1 active reps approxLL diffs: (0.08,0.08) iter 53: time=202.64 for 1 active reps approxLL diffs: (0.06,0.06) iter 54: time=214.41 for 1 active reps approxLL diffs: (0.05,0.05) iter 55: time=221.59 for 1 active reps approxLL diffs: (0.05,0.05) iter 56: time=207.63 for 1 active reps approxLL diffs: (0.05,0.05) iter 57: time=198.96 for 1 active reps approxLL diffs: (0.03,0.03) iter 58: time=207.08 for 1 active reps approxLL diffs: (0.01,0.01) iter 59: time=205.54 for 1 active reps approxLL diffs: (0.00,0.00) Converged at iter 59: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 54.2%, memory/overhead = 45.8% Time for computing and writing betas = 12674.3 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.21914 (705850 good SNPs) lambdaGC: 1.50707 Mean BOLT_LMM_INF: 2.30883 (705850 good SNPs) lambdaGC: 1.50679 Mean BOLT_LMM: 2.33277 (705850 good SNPs) lambdaGC: 1.51594 Note that LINREG may be confounded by a factor of 1.00688 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 7000.98 sec Total elapsed time for analysis = 139480 sec