+-----------------------------+ | ___ | | BOLT-LMM, v2.3.1 /_ / | | December 19, 2017 /_/ | | Po-Ru Loh // | | / | +-----------------------------+ Copyright (C) 2014-2017 Harvard University. Distributed under the GNU GPLv3 open source license. Compiled with USE_SSE: fast aligned memory access Compiled with USE_MKL: Intel Math Kernel Library linear algebra Boost version: 1_58 Command line options: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt \ --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed \ --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim \ --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam \ --allowX \ --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt \ --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab \ --phenoCol=blood_PLATELET_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_PLATELET_COUNT.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_PLATELET_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 = 4540.44 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: 444382 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 = 444382 Singular values of covariate matrix: S[0] = 2.27019e+06 S[1] = 4763.04 S[2] = 469.664 S[3] = 293.514 S[4] = 199.82 S[5] = 194.35 S[6] = 183.575 S[7] = 175.056 S[8] = 170.769 S[9] = 165.462 S[10] = 162.667 S[11] = 153.722 S[12] = 145.738 S[13] = 143.711 S[14] = 141.125 S[15] = 135.791 S[16] = 132.383 S[17] = 130.108 S[18] = 126.095 S[19] = 116.189 S[20] = 112.057 S[21] = 99.7626 S[22] = 44.8263 S[23] = 23.9814 S[24] = 19.3086 S[25] = 0.983791 S[26] = 0.983541 S[27] = 0.983452 S[28] = 0.983214 S[29] = 0.983097 S[30] = 0.982937 S[31] = 0.982882 S[32] = 0.982748 S[33] = 0.982628 S[34] = 0.982494 S[35] = 0.98242 S[36] = 0.982344 S[37] = 0.9822 S[38] = 0.982121 S[39] = 0.981726 S[40] = 0.981636 S[41] = 0.981608 S[42] = 0.981384 S[43] = 0.964224 S[44] = 0.887942 S[45] = 7.29877e-12 S[46] = 5.60313e-13 S[47] = 4.44617e-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: 441793.376137 Dimension of all-1s proj space (Nused-1): 444381 Time for covariate data setup + Bolt initialization = 5132.38 sec Phenotype 1: N = 444382 mean = 0.00158729 std = 0.997169 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 413.181 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 444382) 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=310.99 rNorms/orig: (0.6,0.7) res2s: 722049..124198 iter 2: time=297.58 rNorms/orig: (0.6,0.7) res2s: 852448..161409 iter 3: time=311.82 rNorms/orig: (0.3,0.4) res2s: 989156..202445 iter 4: time=300.56 rNorms/orig: (0.2,0.3) res2s: 1.04034e+06..217754 iter 5: time=303.64 rNorms/orig: (0.1,0.2) res2s: 1.06651e+06..226040 iter 6: time=301.41 rNorms/orig: (0.09,0.1) res2s: 1.07901e+06..230484 iter 7: time=293.83 rNorms/orig: (0.06,0.07) res2s: 1.08496e+06..232566 iter 8: time=290.66 rNorms/orig: (0.04,0.05) res2s: 1.08783e+06..233449 iter 9: time=300.92 rNorms/orig: (0.02,0.03) res2s: 1.08917e+06..233936 iter 10: time=308.23 rNorms/orig: (0.02,0.02) res2s: 1.08986e+06..234148 iter 11: time=303.62 rNorms/orig: (0.01,0.01) res2s: 1.09016e+06..234245 iter 12: time=299.02 rNorms/orig: (0.007,0.008) res2s: 1.09031e+06..234288 iter 13: time=291.76 rNorms/orig: (0.004,0.005) res2s: 1.09036e+06..234308 iter 14: time=302.73 rNorms/orig: (0.003,0.003) res2s: 1.09039e+06..234317 Converged at iter 14: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.6%, memory/overhead = 49.4% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.237639 Estimating MC scaling f_REML at log(delta) = -0.00574099, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=303.70 rNorms/orig: (1,1) res2s: 76375.7..33054.7 iter 2: time=297.74 rNorms/orig: (1,1) res2s: 109142..51545.6 iter 3: time=302.95 rNorms/orig: (0.8,0.9) res2s: 165094..84678.9 iter 4: time=293.72 rNorms/orig: (0.6,0.7) res2s: 203771..104661 iter 5: time=305.75 rNorms/orig: (0.5,0.6) res2s: 232395..120388 iter 6: time=322.53 rNorms/orig: (0.4,0.4) res2s: 252903..132661 iter 7: time=296.61 rNorms/orig: (0.3,0.3) res2s: 267323..141050 iter 8: time=295.04 rNorms/orig: (0.2,0.3) res2s: 277266..146101 iter 9: time=310.20 rNorms/orig: (0.2,0.2) res2s: 283965..150068 iter 10: time=304.72 rNorms/orig: (0.1,0.2) res2s: 288860..152557 iter 11: time=338.40 rNorms/orig: (0.1,0.1) res2s: 291998..154186 iter 12: time=310.62 rNorms/orig: (0.09,0.09) res2s: 294119..155242 iter 13: time=306.12 rNorms/orig: (0.07,0.08) res2s: 295322..155932 iter 14: time=299.01 rNorms/orig: (0.05,0.06) res2s: 296214..156374 iter 15: time=311.25 rNorms/orig: (0.04,0.04) res2s: 296753..156652 iter 16: time=336.20 rNorms/orig: (0.03,0.03) res2s: 297117..156846 iter 17: time=308.53 rNorms/orig: (0.02,0.03) res2s: 297310..156961 iter 18: time=307.67 rNorms/orig: (0.02,0.02) res2s: 297433..157040 iter 19: time=298.35 rNorms/orig: (0.01,0.02) res2s: 297521..157084 iter 20: time=306.99 rNorms/orig: (0.01,0.01) res2s: 297576..157113 iter 21: time=323.68 rNorms/orig: (0.009,0.009) res2s: 297607..157131 iter 22: time=306.34 rNorms/orig: (0.007,0.007) res2s: 297627..157142 iter 23: time=310.10 rNorms/orig: (0.005,0.006) res2s: 297640..157149 iter 24: time=307.46 rNorms/orig: (0.004,0.004) res2s: 297648..157153 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.5%, memory/overhead = 48.5% MCscaling: logDelta = -0.01, h2 = 0.500, f = -0.125812 Estimating MC scaling f_REML at log(delta) = 0.374555, h2 = 0.406056... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=317.86 rNorms/orig: (0.9,1) res2s: 179754..55709.6 iter 2: time=312.91 rNorms/orig: (0.9,1) res2s: 241521..81828.8 iter 3: time=340.68 rNorms/orig: (0.7,0.7) res2s: 332385..122021 iter 4: time=324.82 rNorms/orig: (0.4,0.5) res2s: 383718..142822 iter 5: time=362.05 rNorms/orig: (0.4,0.4) res2s: 417155..157365 iter 6: time=320.82 rNorms/orig: (0.3,0.3) res2s: 438199..167469 iter 7: time=312.00 rNorms/orig: (0.2,0.2) res2s: 451297..173630 iter 8: time=313.18 rNorms/orig: (0.1,0.2) res2s: 459375..176973 iter 9: time=319.72 rNorms/orig: (0.1,0.1) res2s: 464249..179341 iter 10: time=330.30 rNorms/orig: (0.08,0.08) res2s: 467457..180676 iter 11: time=315.88 rNorms/orig: (0.06,0.06) res2s: 469299..181463 iter 12: time=334.99 rNorms/orig: (0.04,0.04) res2s: 470423..181923 iter 13: time=314.68 rNorms/orig: (0.03,0.03) res2s: 470993..182195 iter 14: time=309.48 rNorms/orig: (0.02,0.03) res2s: 471377..182351 iter 15: time=335.53 rNorms/orig: (0.02,0.02) res2s: 471584..182440 iter 16: time=338.69 rNorms/orig: (0.01,0.01) res2s: 471709..182496 iter 17: time=375.22 rNorms/orig: (0.009,0.01) res2s: 471769..182526 iter 18: time=331.23 rNorms/orig: (0.006,0.007) res2s: 471803..182545 iter 19: time=300.72 rNorms/orig: (0.005,0.005) res2s: 471825..182554 iter 20: time=321.87 rNorms/orig: (0.003,0.004) res2s: 471837..182559 Converged at iter 20: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 51.4%, memory/overhead = 48.6% MCscaling: logDelta = 0.37, h2 = 0.406, f = 0.00172112 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.406 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.388960, logDelta = 0.374555, f = 0.00172112 Time for fitting variance components = 18756.3 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=633.80 rNorms/orig: (0.7,1) res2s: 56050.9..151441 iter 2: time=635.46 rNorms/orig: (0.8,1) res2s: 82636..193668 iter 3: time=620.46 rNorms/orig: (0.4,0.8) res2s: 124580..219229 iter 4: time=644.53 rNorms/orig: (0.3,0.6) res2s: 147051..232358 iter 5: time=688.52 rNorms/orig: (0.3,0.4) res2s: 162673..242363 iter 6: time=683.29 rNorms/orig: (0.2,0.3) res2s: 173766..248002 iter 7: time=714.23 rNorms/orig: (0.1,0.2) res2s: 180679..251602 iter 8: time=640.69 rNorms/orig: (0.1,0.2) res2s: 184553..253989 iter 9: time=630.43 rNorms/orig: (0.07,0.1) res2s: 187347..255295 iter 10: time=640.47 rNorms/orig: (0.05,0.1) res2s: 188939..256130 iter 11: time=644.73 rNorms/orig: (0.04,0.08) res2s: 189915..256622 iter 12: time=636.65 rNorms/orig: (0.03,0.05) res2s: 190497..256915 iter 13: time=648.25 rNorms/orig: (0.02,0.04) res2s: 190842..257077 iter 14: time=636.54 rNorms/orig: (0.01,0.03) res2s: 191051..257182 iter 15: time=647.82 rNorms/orig: (0.01,0.02) res2s: 191172..257234 iter 16: time=659.23 rNorms/orig: (0.007,0.02) res2s: 191249..257267 iter 17: time=732.88 rNorms/orig: (0.005,0.01) res2s: 191292..257287 iter 18: time=778.05 rNorms/orig: (0.003,0.009) res2s: 191318..257299 iter 19: time=777.60 rNorms/orig: (0.002,0.007) res2s: 191332..257305 iter 20: time=761.59 rNorms/orig: (0.002,0.005) res2s: 191340..257308 iter 21: time=726.01 rNorms/orig: (0.001,0.004) res2s: 191345..257310 iter 22: time=725.56 rNorms/orig: (0.0008,0.003) res2s: 191348..257311 iter 23: time=638.28 rNorms/orig: (0.0005,0.002) res2s: 191349..257312 iter 24: time=621.20 rNorms/orig: (0.0004,0.002) res2s: 191350..257312 iter 25: time=665.49 rNorms/orig: (0.0003,0.001) res2s: 191351..257313 iter 26: time=632.69 rNorms/orig: (0.0002,0.0008) res2s: 191351..257313 iter 27: time=634.91 rNorms/orig: (0.0001,0.0006) res2s: 191351..257313 iter 28: time=620.05 rNorms/orig: (8e-05,0.0005) res2s: 191351..257313 Converged at iter 28: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 72.7%, memory/overhead = 27.3% AvgPro: 1.524 AvgRetro: 1.494 Calibration: 1.020 (0.003) (30 SNPs) Ratio of medians: 1.015 Median of ratios: 1.018 Time for computing infinitesimal model assoc stats = 19118.2 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 14.1363 sec === Reading LD Scores for calibration of Bayesian assoc stats === Looking up LD Scores... Looking for column header 'SNP': column number = 1 Looking for column header 'LDSCORE': column number = 5 Found LD Scores for 601289/705881 SNPs Estimating inflation of LINREG chisq stats using MLMe as reference... Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 444.4) # of SNPs remaining after outlier window removal: 564359/579570 Intercept of LD Score regression for ref stats: 1.206 (0.020) Estimated attenuation: 0.137 (0.015) Intercept of LD Score regression for cur stats: 1.188 (0.018) Calibration factor (ref/cur) to multiply by: 1.015 (0.003) LINREG intercept inflation = 0.984976 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 = 355505 Singular values of covariate matrix: S[0] = 2.03052e+06 S[1] = 4262.23 S[2] = 420.093 S[3] = 262.52 S[4] = 178.715 S[5] = 173.88 S[6] = 164.415 S[7] = 156.574 S[8] = 152.738 S[9] = 147.962 S[10] = 145.355 S[11] = 137.441 S[12] = 130.321 S[13] = 128.624 S[14] = 126.212 S[15] = 121.365 S[16] = 118.273 S[17] = 116.414 S[18] = 112.803 S[19] = 103.718 S[20] = 100.172 S[21] = 89.4917 S[22] = 40.2288 S[23] = 21.4405 S[24] = 17.2845 S[25] = 0.881756 S[26] = 0.88105 S[27] = 0.880653 S[28] = 0.880181 S[29] = 0.879927 S[30] = 0.879651 S[31] = 0.879296 S[32] = 0.87919 S[33] = 0.879059 S[34] = 0.878859 S[35] = 0.87828 S[36] = 0.87804 S[37] = 0.877686 S[38] = 0.877548 S[39] = 0.877332 S[40] = 0.876718 S[41] = 0.876536 S[42] = 0.87588 S[43] = 0.864214 S[44] = 0.794486 S[45] = 4.28166e-12 S[46] = 6.82365e-13 S[47] = 2.28117e-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: 353425.121325 Dimension of all-1s proj space (Nused-1): 355504 Beginning variational Bayes iter 1: time=675.73 for 18 active reps iter 2: time=421.59 for 18 active reps approxLL diffs: (23670.85,27771.47) iter 3: time=416.57 for 18 active reps approxLL diffs: (3068.43,5593.47) iter 4: time=417.48 for 18 active reps approxLL diffs: (711.01,2058.22) iter 5: time=411.95 for 18 active reps approxLL diffs: (235.62,1043.86) iter 6: time=420.01 for 18 active reps approxLL diffs: (96.13,600.84) iter 7: time=412.56 for 18 active reps approxLL diffs: (45.76,352.27) iter 8: time=411.11 for 18 active reps approxLL diffs: (24.74,244.42) iter 9: time=414.66 for 18 active reps approxLL diffs: (14.66,174.48) iter 10: time=411.31 for 18 active reps approxLL diffs: (9.31,120.10) iter 11: time=414.70 for 18 active reps approxLL diffs: (6.34,93.49) iter 12: time=413.90 for 18 active reps approxLL diffs: (4.61,76.66) iter 13: time=413.30 for 18 active reps approxLL diffs: (3.50,67.01) iter 14: time=413.70 for 18 active reps approxLL diffs: (2.71,53.78) iter 15: time=413.30 for 18 active reps approxLL diffs: (2.14,34.95) iter 16: time=417.67 for 18 active reps approxLL diffs: (1.71,30.01) iter 17: time=412.20 for 18 active reps approxLL diffs: (1.39,25.25) iter 18: time=412.21 for 18 active reps approxLL diffs: (1.16,18.97) iter 19: time=416.66 for 18 active reps approxLL diffs: (0.98,18.58) iter 20: time=413.60 for 18 active reps approxLL diffs: (0.83,12.90) iter 21: time=413.07 for 18 active reps approxLL diffs: (0.70,16.33) iter 22: time=413.98 for 18 active reps approxLL diffs: (0.59,13.52) iter 23: time=412.31 for 18 active reps approxLL diffs: (0.50,9.79) iter 24: time=416.94 for 18 active reps approxLL diffs: (0.43,7.86) iter 25: time=415.75 for 18 active reps approxLL diffs: (0.37,7.88) iter 26: time=414.60 for 18 active reps approxLL diffs: (0.32,6.82) iter 27: time=418.36 for 18 active reps approxLL diffs: (0.28,7.62) iter 28: time=413.32 for 18 active reps approxLL diffs: (0.25,6.65) iter 29: time=421.44 for 18 active reps approxLL diffs: (0.22,6.60) iter 30: time=416.12 for 18 active reps approxLL diffs: (0.18,5.59) iter 31: time=415.51 for 18 active reps approxLL diffs: (0.15,6.51) iter 32: time=411.60 for 18 active reps approxLL diffs: (0.13,6.92) iter 33: time=412.39 for 18 active reps approxLL diffs: (0.11,4.18) iter 34: time=413.98 for 18 active reps approxLL diffs: (0.10,5.16) iter 35: time=413.19 for 18 active reps approxLL diffs: (0.09,5.91) iter 36: time=415.73 for 18 active reps approxLL diffs: (0.08,3.30) iter 37: time=411.29 for 18 active reps approxLL diffs: (0.07,3.58) iter 38: time=410.15 for 18 active reps approxLL diffs: (0.06,4.18) iter 39: time=415.79 for 18 active reps approxLL diffs: (0.06,2.30) iter 40: time=421.95 for 18 active reps approxLL diffs: (0.05,2.46) iter 41: time=414.36 for 18 active reps approxLL diffs: (0.05,2.14) iter 42: time=404.16 for 18 active reps approxLL diffs: (0.04,1.83) iter 43: time=403.57 for 18 active reps approxLL diffs: (0.04,1.66) iter 44: time=415.53 for 18 active reps approxLL diffs: (0.04,1.50) iter 45: time=411.51 for 18 active reps approxLL diffs: (0.03,3.37) iter 46: time=419.57 for 18 active reps approxLL diffs: (0.03,2.62) iter 47: time=413.27 for 18 active reps approxLL diffs: (0.03,1.87) iter 48: time=419.87 for 18 active reps approxLL diffs: (0.03,1.38) iter 49: time=412.00 for 18 active reps approxLL diffs: (0.02,2.51) iter 50: time=407.18 for 18 active reps approxLL diffs: (0.02,1.89) iter 51: time=410.41 for 18 active reps approxLL diffs: (0.02,0.78) iter 52: time=410.21 for 18 active reps approxLL diffs: (0.02,2.30) iter 53: time=411.29 for 18 active reps approxLL diffs: (0.02,1.14) iter 54: time=411.35 for 18 active reps approxLL diffs: (0.01,2.83) iter 55: time=411.08 for 18 active reps approxLL diffs: (0.01,1.89) iter 56: time=394.14 for 17 active reps approxLL diffs: (0.01,1.05) iter 57: time=392.71 for 17 active reps approxLL diffs: (0.01,0.24) iter 58: time=394.91 for 17 active reps approxLL diffs: (0.01,0.29) iter 59: time=370.80 for 16 active reps approxLL diffs: (0.01,0.69) iter 60: time=371.26 for 16 active reps approxLL diffs: (0.01,0.77) iter 61: time=375.21 for 16 active reps approxLL diffs: (0.01,0.55) iter 62: time=385.31 for 15 active reps approxLL diffs: (0.01,1.92) iter 63: time=387.30 for 15 active reps approxLL diffs: (0.01,4.16) iter 64: time=353.24 for 13 active reps approxLL diffs: (0.01,3.57) iter 65: time=351.71 for 13 active reps approxLL diffs: (0.01,2.45) iter 66: time=338.64 for 12 active reps approxLL diffs: (0.01,2.56) iter 67: time=342.39 for 12 active reps approxLL diffs: (0.01,3.57) iter 68: time=345.96 for 11 active reps approxLL diffs: (0.01,1.12) iter 69: time=307.88 for 9 active reps approxLL diffs: (0.01,0.16) iter 70: time=308.56 for 9 active reps approxLL diffs: (0.01,0.22) iter 71: time=310.40 for 9 active reps approxLL diffs: (0.01,0.31) iter 72: time=314.25 for 9 active reps approxLL diffs: (0.01,0.48) iter 73: time=310.42 for 9 active reps approxLL diffs: (0.01,1.46) iter 74: time=277.94 for 8 active reps approxLL diffs: (0.01,1.42) iter 75: time=284.99 for 7 active reps approxLL diffs: (0.01,2.06) iter 76: time=283.01 for 7 active reps approxLL diffs: (0.01,0.39) iter 77: time=267.08 for 6 active reps approxLL diffs: (0.01,0.42) iter 78: time=226.65 for 4 active reps approxLL diffs: (0.01,0.41) iter 79: time=238.70 for 3 active reps approxLL diffs: (0.04,0.18) iter 80: time=237.47 for 3 active reps approxLL diffs: (0.04,0.07) iter 81: time=236.80 for 3 active reps approxLL diffs: (0.04,0.17) iter 82: time=243.33 for 3 active reps approxLL diffs: (0.04,0.35) iter 83: time=250.16 for 3 active reps approxLL diffs: (0.03,0.36) iter 84: time=237.59 for 3 active reps approxLL diffs: (0.02,0.17) iter 85: time=237.94 for 3 active reps approxLL diffs: (0.01,0.07) iter 86: time=244.27 for 3 active reps approxLL diffs: (0.01,0.03) iter 87: time=222.33 for 2 active reps approxLL diffs: (0.02,0.02) iter 88: time=220.29 for 2 active reps approxLL diffs: (0.02,0.03) iter 89: time=220.05 for 2 active reps approxLL diffs: (0.02,0.10) iter 90: time=220.85 for 2 active reps approxLL diffs: (0.03,0.39) iter 91: time=222.51 for 2 active reps approxLL diffs: (0.06,0.90) iter 92: time=220.92 for 2 active reps approxLL diffs: (0.21,1.56) iter 93: time=220.08 for 2 active reps approxLL diffs: (1.07,2.97) iter 94: time=219.33 for 2 active reps approxLL diffs: (0.45,1.56) iter 95: time=219.14 for 2 active reps approxLL diffs: (0.10,0.33) iter 96: time=217.87 for 2 active reps approxLL diffs: (0.04,0.15) iter 97: time=218.04 for 2 active reps approxLL diffs: (0.01,0.26) iter 98: time=218.46 for 2 active reps approxLL diffs: (0.01,0.29) iter 99: time=196.01 for 1 active reps approxLL diffs: (0.13,0.13) iter 100: time=196.31 for 1 active reps approxLL diffs: (0.04,0.04) iter 101: time=196.68 for 1 active reps approxLL diffs: (0.02,0.02) iter 102: time=196.41 for 1 active reps approxLL diffs: (0.01,0.01) iter 103: time=196.13 for 1 active reps approxLL diffs: (0.01,0.01) iter 104: time=196.43 for 1 active reps approxLL diffs: (0.01,0.01) iter 105: time=196.87 for 1 active reps approxLL diffs: (0.01,0.01) iter 106: time=196.76 for 1 active reps approxLL diffs: (0.02,0.02) iter 107: time=195.91 for 1 active reps approxLL diffs: (0.02,0.02) iter 108: time=211.14 for 1 active reps approxLL diffs: (0.05,0.05) iter 109: time=237.51 for 1 active reps approxLL diffs: (0.12,0.12) iter 110: time=229.42 for 1 active reps approxLL diffs: (0.37,0.37) iter 111: time=226.32 for 1 active reps approxLL diffs: (0.65,0.65) iter 112: time=230.60 for 1 active reps approxLL diffs: (0.62,0.62) iter 113: time=229.51 for 1 active reps approxLL diffs: (0.43,0.43) iter 114: time=227.23 for 1 active reps approxLL diffs: (0.21,0.21) iter 115: time=226.78 for 1 active reps approxLL diffs: (0.10,0.10) iter 116: time=226.16 for 1 active reps approxLL diffs: (0.04,0.04) iter 117: time=216.54 for 1 active reps approxLL diffs: (0.02,0.02) iter 118: time=222.57 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 118: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 76.9%, memory/overhead = 23.1% Computing predictions on left-out cross-validation fold Time for computing predictions = 8490.96 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.01: 0.277238 f2=0.3, p=0.02: 0.276797 f2=0.3, p=0.05: 0.275081 ... f2=0.5, p=0.5: 0.199671 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.995698 Absolute prediction MSE using standard LMM: 0.796886 Absolute prediction MSE, fold-best f2=0.3, p=0.01: 0.719652 Absolute pred MSE using f2=0.5, p=0.5: 0.796886 Absolute pred MSE using f2=0.5, p=0.2: 0.766542 Absolute pred MSE using f2=0.5, p=0.1: 0.743899 Absolute pred MSE using f2=0.5, p=0.05: 0.729253 Absolute pred MSE using f2=0.5, p=0.02: 0.722540 Absolute pred MSE using f2=0.5, p=0.01: 0.722413 Absolute pred MSE using f2=0.3, p=0.5: 0.785715 Absolute pred MSE using f2=0.3, p=0.2: 0.749669 Absolute pred MSE using f2=0.3, p=0.1: 0.730121 Absolute pred MSE using f2=0.3, p=0.05: 0.721800 Absolute pred MSE using f2=0.3, p=0.02: 0.720092 Absolute pred MSE using f2=0.3, p=0.01: 0.719652 Absolute pred MSE using f2=0.1, p=0.5: 0.771551 Absolute pred MSE using f2=0.1, p=0.2: 0.737269 Absolute pred MSE using f2=0.1, p=0.1: 0.724948 Absolute pred MSE using f2=0.1, p=0.05: 0.724118 Absolute pred MSE using f2=0.1, p=0.02: 0.727948 Absolute pred MSE using f2=0.1, p=0.01: 0.728785 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.200 Relative improvement in prediction MSE using non-inf model: 0.097 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.01 Time for estimating mixture parameters = 53005.2 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=731.51 for 23 active reps iter 2: time=506.85 for 23 active reps approxLL diffs: (28914.19,31763.83) iter 3: time=506.44 for 23 active reps approxLL diffs: (6227.94,7016.07) iter 4: time=486.01 for 23 active reps approxLL diffs: (2298.49,2658.72) iter 5: time=487.11 for 23 active reps approxLL diffs: (1062.87,1242.26) iter 6: time=489.08 for 23 active reps approxLL diffs: (597.19,724.19) iter 7: time=485.76 for 23 active reps approxLL diffs: (359.40,453.59) iter 8: time=486.84 for 23 active reps approxLL diffs: (234.84,301.11) iter 9: time=488.32 for 23 active reps approxLL diffs: (186.33,228.15) iter 10: time=488.32 for 23 active reps approxLL diffs: (134.48,182.73) iter 11: time=488.96 for 23 active reps approxLL diffs: (96.56,145.05) iter 12: time=501.39 for 23 active reps approxLL diffs: (74.15,112.24) iter 13: time=493.57 for 23 active reps approxLL diffs: (57.91,83.69) iter 14: time=489.21 for 23 active reps approxLL diffs: (41.41,67.73) iter 15: time=490.63 for 23 active reps approxLL diffs: (27.25,57.27) iter 16: time=492.17 for 23 active reps approxLL diffs: (18.50,43.48) iter 17: time=490.56 for 23 active reps approxLL diffs: (20.15,44.61) iter 18: time=486.01 for 23 active reps approxLL diffs: (17.08,46.40) iter 19: time=486.09 for 23 active reps approxLL diffs: (17.36,34.51) iter 20: time=483.89 for 23 active reps approxLL diffs: (11.43,31.43) iter 21: time=478.16 for 23 active reps approxLL diffs: (9.07,24.61) iter 22: time=491.56 for 23 active reps approxLL diffs: (7.50,21.46) iter 23: time=495.89 for 23 active reps approxLL diffs: (6.26,15.97) iter 24: time=497.32 for 23 active reps approxLL diffs: (6.31,18.41) iter 25: time=494.06 for 23 active reps approxLL diffs: (4.96,20.84) iter 26: time=484.01 for 23 active reps approxLL diffs: (5.88,19.74) iter 27: time=476.20 for 23 active reps approxLL diffs: (4.92,15.34) iter 28: time=481.21 for 23 active reps approxLL diffs: (3.79,16.07) iter 29: time=473.84 for 23 active reps approxLL diffs: (3.59,11.10) iter 30: time=462.57 for 23 active reps approxLL diffs: (2.39,10.08) iter 31: time=465.73 for 23 active reps approxLL diffs: (1.52,11.76) iter 32: time=463.59 for 23 active reps approxLL diffs: (0.90,8.04) iter 33: time=466.00 for 23 active reps approxLL diffs: (1.19,7.28) iter 34: time=465.25 for 23 active reps approxLL diffs: (0.62,10.57) iter 35: time=465.00 for 23 active reps approxLL diffs: (0.53,5.89) iter 36: time=463.13 for 23 active reps approxLL diffs: (0.33,7.18) iter 37: time=474.59 for 23 active reps approxLL diffs: (0.22,6.81) iter 38: time=476.16 for 23 active reps approxLL diffs: (0.18,3.93) iter 39: time=498.59 for 23 active reps approxLL diffs: (0.19,3.77) iter 40: time=491.01 for 23 active reps approxLL diffs: (0.27,6.61) iter 41: time=495.93 for 23 active reps approxLL diffs: (0.28,8.22) iter 42: time=467.61 for 23 active reps approxLL diffs: (0.43,7.73) iter 43: time=507.47 for 23 active reps approxLL diffs: (0.35,5.52) iter 44: time=508.83 for 23 active reps approxLL diffs: (0.27,6.98) iter 45: time=501.16 for 23 active reps approxLL diffs: (0.21,8.38) iter 46: time=498.56 for 23 active reps approxLL diffs: (0.19,4.79) iter 47: time=521.11 for 23 active reps approxLL diffs: (0.11,7.01) iter 48: time=505.07 for 23 active reps approxLL diffs: (0.11,5.37) iter 49: time=495.12 for 23 active reps approxLL diffs: (0.09,2.90) iter 50: time=497.01 for 23 active reps approxLL diffs: (0.06,2.51) iter 51: time=494.14 for 23 active reps approxLL diffs: (0.05,3.00) iter 52: time=489.82 for 23 active reps approxLL diffs: (0.04,4.18) iter 53: time=486.94 for 23 active reps approxLL diffs: (0.03,2.12) iter 54: time=460.80 for 23 active reps approxLL diffs: (0.02,2.07) iter 55: time=472.78 for 23 active reps approxLL diffs: (0.02,6.00) iter 56: time=473.33 for 23 active reps approxLL diffs: (0.01,3.73) iter 57: time=467.30 for 23 active reps approxLL diffs: (0.01,3.40) iter 58: time=461.05 for 23 active reps approxLL diffs: (0.01,3.41) iter 59: time=461.37 for 23 active reps approxLL diffs: (0.01,3.54) iter 60: time=457.61 for 22 active reps approxLL diffs: (0.02,2.98) iter 61: time=482.39 for 22 active reps approxLL diffs: (0.02,3.51) iter 62: time=478.22 for 22 active reps approxLL diffs: (0.02,3.65) iter 63: time=482.55 for 22 active reps approxLL diffs: (0.01,4.05) iter 64: time=473.20 for 22 active reps approxLL diffs: (0.01,3.21) iter 65: time=476.34 for 22 active reps approxLL diffs: (0.01,3.13) iter 66: time=457.67 for 21 active reps approxLL diffs: (0.01,1.84) iter 67: time=433.88 for 20 active reps approxLL diffs: (0.02,1.71) iter 68: time=431.79 for 20 active reps approxLL diffs: (0.02,1.90) iter 69: time=418.46 for 20 active reps approxLL diffs: (0.01,4.02) iter 70: time=442.33 for 19 active reps approxLL diffs: (0.01,4.36) iter 71: time=437.01 for 18 active reps approxLL diffs: (0.02,1.36) iter 72: time=409.05 for 18 active reps approxLL diffs: (0.01,1.64) iter 73: time=384.35 for 17 active reps approxLL diffs: (0.01,2.30) iter 74: time=401.97 for 17 active reps approxLL diffs: (0.00,2.48) iter 75: time=370.66 for 16 active reps approxLL diffs: (0.01,2.81) iter 76: time=361.44 for 14 active reps approxLL diffs: (0.01,2.67) iter 77: time=342.12 for 13 active reps approxLL diffs: (0.01,3.31) iter 78: time=343.67 for 13 active reps approxLL diffs: (0.01,1.24) iter 79: time=331.70 for 12 active reps approxLL diffs: (0.01,2.02) iter 80: time=310.16 for 10 active reps approxLL diffs: (0.02,1.82) iter 81: time=318.98 for 10 active reps approxLL diffs: (0.01,2.48) iter 82: time=343.08 for 10 active reps approxLL diffs: (0.01,1.78) iter 83: time=319.63 for 9 active reps approxLL diffs: (0.03,1.10) iter 84: time=310.23 for 9 active reps approxLL diffs: (0.03,1.66) iter 85: time=320.64 for 9 active reps approxLL diffs: (0.03,0.30) iter 86: time=318.98 for 9 active reps approxLL diffs: (0.01,0.43) iter 87: time=325.37 for 7 active reps approxLL diffs: (0.01,0.34) iter 88: time=320.43 for 7 active reps approxLL diffs: (0.01,0.89) iter 89: time=295.08 for 6 active reps approxLL diffs: (0.01,1.41) iter 90: time=294.96 for 6 active reps approxLL diffs: (0.00,0.87) iter 91: time=276.40 for 5 active reps approxLL diffs: (0.08,1.93) iter 92: time=263.84 for 5 active reps approxLL diffs: (0.05,2.17) iter 93: time=267.32 for 5 active reps approxLL diffs: (0.06,2.36) iter 94: time=274.33 for 5 active reps approxLL diffs: (0.10,1.76) iter 95: time=274.61 for 5 active reps approxLL diffs: (0.28,0.93) iter 96: time=253.36 for 5 active reps approxLL diffs: (0.09,1.02) iter 97: time=263.12 for 5 active reps approxLL diffs: (0.02,1.63) iter 98: time=269.67 for 5 active reps approxLL diffs: (0.01,1.07) iter 99: time=252.17 for 4 active reps approxLL diffs: (0.01,1.18) iter 100: time=281.05 for 3 active reps approxLL diffs: (0.01,1.22) iter 101: time=293.22 for 3 active reps approxLL diffs: (0.01,0.17) iter 102: time=253.49 for 1 active reps approxLL diffs: (0.03,0.03) iter 103: time=239.78 for 1 active reps approxLL diffs: (0.02,0.02) iter 104: time=252.43 for 1 active reps approxLL diffs: (0.02,0.02) iter 105: time=243.97 for 1 active reps approxLL diffs: (0.03,0.03) iter 106: time=238.95 for 1 active reps approxLL diffs: (0.04,0.04) iter 107: time=258.05 for 1 active reps approxLL diffs: (0.07,0.07) iter 108: time=261.28 for 1 active reps approxLL diffs: (0.11,0.11) iter 109: time=257.34 for 1 active reps approxLL diffs: (0.12,0.12) iter 110: time=247.35 for 1 active reps approxLL diffs: (0.10,0.10) iter 111: time=261.37 for 1 active reps approxLL diffs: (0.07,0.07) iter 112: time=231.62 for 1 active reps approxLL diffs: (0.08,0.08) iter 113: time=224.42 for 1 active reps approxLL diffs: (0.13,0.13) iter 114: time=236.45 for 1 active reps approxLL diffs: (0.17,0.17) iter 115: time=254.67 for 1 active reps approxLL diffs: (0.11,0.11) iter 116: time=259.25 for 1 active reps approxLL diffs: (0.04,0.04) iter 117: time=275.86 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 117: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.7%, memory/overhead = 22.3% Filtering to SNPs with chisq stats, LD Scores, and MAF > 0.01 # of SNPs passing filters before outlier removal: 579570/705881 Masking windows around outlier snps (chisq > 444.4) # of SNPs remaining after outlier window removal: 564359/579570 Intercept of LD Score regression for ref stats: 1.206 (0.020) Estimated attenuation: 0.137 (0.015) Intercept of LD Score regression for cur stats: 1.211 (0.022) Calibration factor (ref/cur) to multiply by: 0.996 (0.003) Time for computing Bayesian mixed model assoc stats = 48142.8 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=501.99 for 1 active reps iter 2: time=281.39 for 1 active reps approxLL diffs: (31951.44,31951.44) iter 3: time=281.36 for 1 active reps approxLL diffs: (7031.59,7031.59) iter 4: time=265.15 for 1 active reps approxLL diffs: (2654.64,2654.64) iter 5: time=258.12 for 1 active reps approxLL diffs: (1229.06,1229.06) iter 6: time=269.83 for 1 active reps approxLL diffs: (734.06,734.06) iter 7: time=279.54 for 1 active reps approxLL diffs: (442.61,442.61) iter 8: time=278.37 for 1 active reps approxLL diffs: (283.27,283.27) iter 9: time=270.60 for 1 active reps approxLL diffs: (205.54,205.54) iter 10: time=268.73 for 1 active reps approxLL diffs: (172.92,172.92) iter 11: time=240.89 for 1 active reps approxLL diffs: (133.69,133.69) iter 12: time=238.97 for 1 active reps approxLL diffs: (95.20,95.20) iter 13: time=270.63 for 1 active reps approxLL diffs: (67.22,67.22) iter 14: time=267.96 for 1 active reps approxLL diffs: (59.73,59.73) iter 15: time=245.26 for 1 active reps approxLL diffs: (45.92,45.92) iter 16: time=260.39 for 1 active reps approxLL diffs: (34.87,34.87) iter 17: time=278.76 for 1 active reps approxLL diffs: (30.35,30.35) iter 18: time=267.99 for 1 active reps approxLL diffs: (24.79,24.79) iter 19: time=279.58 for 1 active reps approxLL diffs: (24.39,24.39) iter 20: time=271.10 for 1 active reps approxLL diffs: (18.06,18.06) iter 21: time=291.27 for 1 active reps approxLL diffs: (17.48,17.48) iter 22: time=258.41 for 1 active reps approxLL diffs: (16.77,16.77) iter 23: time=249.71 for 1 active reps approxLL diffs: (17.61,17.61) iter 24: time=272.46 for 1 active reps approxLL diffs: (17.74,17.74) iter 25: time=260.31 for 1 active reps approxLL diffs: (17.11,17.11) iter 26: time=280.92 for 1 active reps approxLL diffs: (14.80,14.80) iter 27: time=285.77 for 1 active reps approxLL diffs: (10.00,10.00) iter 28: time=267.53 for 1 active reps approxLL diffs: (7.60,7.60) iter 29: time=266.12 for 1 active reps approxLL diffs: (11.71,11.71) iter 30: time=239.88 for 1 active reps approxLL diffs: (6.94,6.94) iter 31: time=272.02 for 1 active reps approxLL diffs: (4.67,4.67) iter 32: time=290.34 for 1 active reps approxLL diffs: (4.91,4.91) iter 33: time=283.81 for 1 active reps approxLL diffs: (3.96,3.96) iter 34: time=287.42 for 1 active reps approxLL diffs: (2.30,2.30) iter 35: time=261.81 for 1 active reps approxLL diffs: (1.20,1.20) iter 36: time=264.56 for 1 active reps approxLL diffs: (0.81,0.81) iter 37: time=274.00 for 1 active reps approxLL diffs: (1.12,1.12) iter 38: time=288.93 for 1 active reps approxLL diffs: (1.83,1.83) iter 39: time=265.34 for 1 active reps approxLL diffs: (1.10,1.10) iter 40: time=285.95 for 1 active reps approxLL diffs: (0.69,0.69) iter 41: time=272.47 for 1 active reps approxLL diffs: (0.63,0.63) iter 42: time=255.71 for 1 active reps approxLL diffs: (0.68,0.68) iter 43: time=253.85 for 1 active reps approxLL diffs: (1.18,1.18) iter 44: time=274.18 for 1 active reps approxLL diffs: (1.07,1.07) iter 45: time=248.19 for 1 active reps approxLL diffs: (0.86,0.86) iter 46: time=244.39 for 1 active reps approxLL diffs: (1.54,1.54) iter 47: time=267.11 for 1 active reps approxLL diffs: (1.78,1.78) iter 48: time=261.40 for 1 active reps approxLL diffs: (1.13,1.13) iter 49: time=258.68 for 1 active reps approxLL diffs: (1.34,1.34) iter 50: time=262.29 for 1 active reps approxLL diffs: (1.34,1.34) iter 51: time=257.22 for 1 active reps approxLL diffs: (3.29,3.29) iter 52: time=258.97 for 1 active reps approxLL diffs: (2.85,2.85) iter 53: time=269.81 for 1 active reps approxLL diffs: (0.84,0.84) iter 54: time=269.13 for 1 active reps approxLL diffs: (0.87,0.87) iter 55: time=271.61 for 1 active reps approxLL diffs: (0.38,0.38) iter 56: time=268.16 for 1 active reps approxLL diffs: (0.45,0.45) iter 57: time=239.73 for 1 active reps approxLL diffs: (1.54,1.54) iter 58: time=266.29 for 1 active reps approxLL diffs: (2.26,2.26) iter 59: time=254.48 for 1 active reps approxLL diffs: (1.61,1.61) iter 60: time=240.94 for 1 active reps approxLL diffs: (0.95,0.95) iter 61: time=237.65 for 1 active reps approxLL diffs: (0.61,0.61) iter 62: time=243.76 for 1 active reps approxLL diffs: (0.43,0.43) iter 63: time=237.41 for 1 active reps approxLL diffs: (0.34,0.34) iter 64: time=244.34 for 1 active reps approxLL diffs: (0.29,0.29) iter 65: time=267.61 for 1 active reps approxLL diffs: (0.21,0.21) iter 66: time=254.39 for 1 active reps approxLL diffs: (0.14,0.14) iter 67: time=246.91 for 1 active reps approxLL diffs: (0.08,0.08) iter 68: time=256.63 for 1 active reps approxLL diffs: (0.09,0.09) iter 69: time=269.94 for 1 active reps approxLL diffs: (0.50,0.50) iter 70: time=259.15 for 1 active reps approxLL diffs: (2.45,2.45) iter 71: time=265.56 for 1 active reps approxLL diffs: (4.56,4.56) iter 72: time=245.77 for 1 active reps approxLL diffs: (6.62,6.62) iter 73: time=253.84 for 1 active reps approxLL diffs: (1.21,1.21) iter 74: time=258.74 for 1 active reps approxLL diffs: (0.12,0.12) iter 75: time=264.69 for 1 active reps approxLL diffs: (0.08,0.08) iter 76: time=265.44 for 1 active reps approxLL diffs: (0.16,0.16) iter 77: time=270.88 for 1 active reps approxLL diffs: (0.52,0.52) iter 78: time=263.52 for 1 active reps approxLL diffs: (1.73,1.73) iter 79: time=231.59 for 1 active reps approxLL diffs: (4.71,4.71) iter 80: time=250.99 for 1 active reps approxLL diffs: (1.63,1.63) iter 81: time=239.97 for 1 active reps approxLL diffs: (0.07,0.07) iter 82: time=247.25 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 82: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 55.2%, memory/overhead = 44.8% Time for computing and writing betas = 21797.9 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 2.71871 (705851 good SNPs) lambdaGC: 1.53001 Mean BOLT_LMM_INF: 3.08238 (705851 good SNPs) lambdaGC: 1.59638 Mean BOLT_LMM: 3.23151 (705851 good SNPs) lambdaGC: 1.60793 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 7923.78 sec Total elapsed time for analysis = 178844 sec