+-----------------------------+ | ___ | | BOLT-LMM, v2.3.1 /_ / | | December 19, 2017 /_/ | | Po-Ru Loh // | | / | +-----------------------------+ Copyright (C) 2014-2017 Harvard University. Distributed under the GNU GPLv3 open source license. Compiled with USE_SSE: fast aligned memory access Compiled with USE_MKL: Intel Math Kernel Library linear algebra Boost version: 1_58 Command line options: /n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3.1/bolt \ --bed=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bed \ --bim=/n/groups/price/UKBiobank/download_500K/../qc0_500K/chr{1:23}.bim \ --fam=/n/groups/price/UKBiobank/download_500K/ukb1404_cal_chr1_v2_CURRENT.fixCol6.fam \ --allowX \ --remove=/n/groups/price/UKBiobank/download_500K/bolt.in_plink_but_not_imputed.FID_IID.976.txt \ --remove=/n/groups/price/UKBiobank/download_500K/../sampleQC/remove.nonWhite.FID_IID.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr.no_phasing.keep_rare.hwe200.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_maf_lt_1e-5.txt \ --exclude=/n/groups/price/UKBiobank/snpQC/allchr_missing_gt_0.09.txt \ --phenoFile=/n/groups/price/steven/RareVariants/Final/UKB_new_sumstats/UKB_v2.092517.tab \ --phenoCol=blood_MEAN_PLATELET_VOL \ --covarFile=/n/groups/price/UKBiobank/app10438assoc/ukb4777.processed_and_post.plinkPCs.tab.gz \ --covarCol=cov_ASSESS_CENTER \ --covarCol=cov_GENO_ARRAY \ --covarCol=cov_SEX \ --covarMaxLevels=30 \ --qCovarCol=cov_AGE \ --qCovarCol=cov_AGE_SQ \ --qCovarCol=PC{1:20} \ --LDscoresFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/LDSCORE.1000G_EUR.tab.gz \ --geneticMapFile=/n/groups/price/poru/HSPH_SVN/software/BOLT-LMM_v2.3/tables/genetic_map_hg19_withX.txt.gz \ --lmmForceNonInf \ --numThreads=8 \ --predBetasFile=bolt_460K_selfRepWhite.blood_MEAN_PLATELET_VOL.predbetas.txt.gz \ --statsFile=bolt_460K_selfRepWhite.blood_MEAN_PLATELET_VOL.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 = 4737.1 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: 445163 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 = 445163 Singular values of covariate matrix: S[0] = 2.27224e+06 S[1] = 4767.23 S[2] = 470.065 S[3] = 293.757 S[4] = 199.996 S[5] = 194.514 S[6] = 183.71 S[7] = 175.203 S[8] = 170.906 S[9] = 165.575 S[10] = 162.83 S[11] = 153.856 S[12] = 145.863 S[13] = 143.792 S[14] = 141.231 S[15] = 135.929 S[16] = 132.533 S[17] = 130.257 S[18] = 126.239 S[19] = 116.333 S[20] = 112.166 S[21] = 99.8508 S[22] = 44.8845 S[23] = 23.9816 S[24] = 19.3254 S[25] = 0.984518 S[26] = 0.984238 S[27] = 0.98422 S[28] = 0.984107 S[29] = 0.983887 S[30] = 0.983789 S[31] = 0.983707 S[32] = 0.983623 S[33] = 0.983553 S[34] = 0.98337 S[35] = 0.98329 S[36] = 0.9832 S[37] = 0.983047 S[38] = 0.982994 S[39] = 0.982684 S[40] = 0.982502 S[41] = 0.982297 S[42] = 0.982186 S[43] = 0.964987 S[44] = 0.88877 S[45] = 6.79729e-12 S[46] = 7.7068e-13 S[47] = 2.08517e-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: 442569.962253 Dimension of all-1s proj space (Nused-1): 445162 Time for covariate data setup + Bolt initialization = 4175.14 sec Phenotype 1: N = 445163 mean = -0.00550905 std = 0.997039 === Computing linear regression (LINREG) stats === Time for computing LINREG stats = 448.321 sec === Estimating variance parameters === Using CGtol of 0.005 for this step Using default number of random trials: 3 (for Nused = 445163) 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=322.62 rNorms/orig: (0.6,0.7) res2s: 721723..107299 iter 2: time=317.24 rNorms/orig: (0.5,0.7) res2s: 850925..146084 iter 3: time=301.46 rNorms/orig: (0.3,0.3) res2s: 988326..181804 iter 4: time=307.22 rNorms/orig: (0.2,0.3) res2s: 1.03993e+06..197108 iter 5: time=313.04 rNorms/orig: (0.1,0.2) res2s: 1.06588e+06..204489 iter 6: time=312.48 rNorms/orig: (0.09,0.1) res2s: 1.07855e+06..208789 iter 7: time=305.98 rNorms/orig: (0.06,0.07) res2s: 1.08497e+06..210694 iter 8: time=307.83 rNorms/orig: (0.04,0.05) res2s: 1.08791e+06..211570 iter 9: time=308.20 rNorms/orig: (0.02,0.03) res2s: 1.0894e+06..212027 iter 10: time=307.46 rNorms/orig: (0.02,0.02) res2s: 1.09011e+06..212236 iter 11: time=309.37 rNorms/orig: (0.01,0.01) res2s: 1.09043e+06..212332 iter 12: time=311.03 rNorms/orig: (0.007,0.007) res2s: 1.09057e+06..212370 iter 13: time=311.32 rNorms/orig: (0.004,0.005) res2s: 1.09063e+06..212387 Converged at iter 13: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.3%, memory/overhead = 49.7% MCscaling: logDelta = 1.09, h2 = 0.250, f = 0.401465 Estimating MC scaling f_REML at log(delta) = -0.00574086, h2 = 0.5... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=310.20 rNorms/orig: (1,1) res2s: 76235.1..26472.4 iter 2: time=311.86 rNorms/orig: (1,1) res2s: 108828..44221.3 iter 3: time=307.93 rNorms/orig: (0.8,0.9) res2s: 164637..71126.2 iter 4: time=308.54 rNorms/orig: (0.6,0.7) res2s: 203337..89926.5 iter 5: time=305.58 rNorms/orig: (0.5,0.5) res2s: 231616..103331 iter 6: time=303.15 rNorms/orig: (0.4,0.4) res2s: 251995..114729 iter 7: time=303.89 rNorms/orig: (0.3,0.3) res2s: 266817..122110 iter 8: time=302.99 rNorms/orig: (0.2,0.3) res2s: 276772..126945 iter 9: time=303.36 rNorms/orig: (0.2,0.2) res2s: 283802..130556 iter 10: time=299.26 rNorms/orig: (0.1,0.2) res2s: 288767..132942 iter 11: time=306.67 rNorms/orig: (0.1,0.1) res2s: 291975..134538 iter 12: time=304.66 rNorms/orig: (0.09,0.09) res2s: 294072..135477 iter 13: time=305.54 rNorms/orig: (0.07,0.07) res2s: 295308..136066 iter 14: time=305.36 rNorms/orig: (0.05,0.06) res2s: 296192..136512 iter 15: time=299.60 rNorms/orig: (0.04,0.04) res2s: 296750..136750 iter 16: time=295.94 rNorms/orig: (0.03,0.03) res2s: 297112..136916 iter 17: time=297.23 rNorms/orig: (0.02,0.03) res2s: 297304..137014 iter 18: time=297.44 rNorms/orig: (0.02,0.02) res2s: 297430..137080 iter 19: time=302.78 rNorms/orig: (0.01,0.02) res2s: 297520..137117 iter 20: time=305.20 rNorms/orig: (0.01,0.01) res2s: 297574..137143 iter 21: time=317.70 rNorms/orig: (0.008,0.009) res2s: 297605..137160 iter 22: time=325.17 rNorms/orig: (0.007,0.007) res2s: 297627..137171 iter 23: time=308.34 rNorms/orig: (0.005,0.006) res2s: 297640..137177 iter 24: time=319.23 rNorms/orig: (0.004,0.004) res2s: 297648..137182 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 50.3%, memory/overhead = 49.7% MCscaling: logDelta = -0.01, h2 = 0.500, f = 0.0105682 Estimating MC scaling f_REML at log(delta) = -0.0354426, h2 = 0.507425... Batch-solving 4 systems of equations using conjugate gradient iteration iter 1: time=312.85 rNorms/orig: (1,1) res2s: 71131.1..25307.3 iter 2: time=320.06 rNorms/orig: (1,1) res2s: 101970..42476.6 iter 3: time=311.35 rNorms/orig: (0.8,0.9) res2s: 155306..68791.2 iter 4: time=313.11 rNorms/orig: (0.6,0.7) res2s: 192838..87388.3 iter 5: time=328.73 rNorms/orig: (0.5,0.5) res2s: 220537..100770 iter 6: time=315.46 rNorms/orig: (0.4,0.4) res2s: 240689..112248 iter 7: time=331.14 rNorms/orig: (0.3,0.3) res2s: 255473..119744 iter 8: time=316.09 rNorms/orig: (0.2,0.3) res2s: 265492..124692 iter 9: time=310.13 rNorms/orig: (0.2,0.2) res2s: 272619..128417 iter 10: time=310.19 rNorms/orig: (0.1,0.2) res2s: 277696..130896 iter 11: time=320.69 rNorms/orig: (0.1,0.1) res2s: 281003..132569 iter 12: time=312.81 rNorms/orig: (0.09,0.1) res2s: 283181..133560 iter 13: time=334.38 rNorms/orig: (0.07,0.08) res2s: 284474..134186 iter 14: time=320.03 rNorms/orig: (0.05,0.06) res2s: 285406..134664 iter 15: time=315.52 rNorms/orig: (0.04,0.04) res2s: 285999..134921 iter 16: time=388.29 rNorms/orig: (0.03,0.04) res2s: 286387..135101 iter 17: time=334.36 rNorms/orig: (0.02,0.03) res2s: 286594..135209 iter 18: time=330.86 rNorms/orig: (0.02,0.02) res2s: 286731..135281 iter 19: time=316.88 rNorms/orig: (0.01,0.02) res2s: 286830..135323 iter 20: time=308.17 rNorms/orig: (0.01,0.01) res2s: 286890..135352 iter 21: time=306.43 rNorms/orig: (0.009,0.01) res2s: 286924..135371 iter 22: time=305.46 rNorms/orig: (0.007,0.008) res2s: 286948..135383 iter 23: time=303.88 rNorms/orig: (0.005,0.006) res2s: 286963..135390 iter 24: time=309.58 rNorms/orig: (0.004,0.005) res2s: 286972..135395 Converged at iter 24: rNorms/orig all < CGtol=0.005 Time breakdown: dgemm = 49.9%, memory/overhead = 50.1% MCscaling: logDelta = -0.04, h2 = 0.507, f = -0.000309796 Secant iteration for h2 estimation converged in 1 steps Estimated (pseudo-)heritability: h2g = 0.507 To more precisely estimate variance parameters and s.e., use --reml Variance params: sigma^2_K = 0.473111, logDelta = -0.035443, f = -0.000309796 Time for fitting variance components = 19658.5 sec === Computing mixed model assoc stats (inf. model) === Selected 30 SNPs for computation of prospective stat Tried 30; threw out 0 with GRAMMAR chisq > 5 Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Batch-solving 53 systems of equations using conjugate gradient iteration iter 1: time=653.92 rNorms/orig: (0.9,1) res2s: 25971..103509 iter 2: time=648.34 rNorms/orig: (1,1) res2s: 43682.5..142828 iter 3: time=658.09 rNorms/orig: (0.6,0.9) res2s: 71632.6..169996 iter 4: time=647.20 rNorms/orig: (0.5,0.8) res2s: 92474.5..185672 iter 5: time=659.15 rNorms/orig: (0.4,0.6) res2s: 107125..198965 iter 6: time=648.91 rNorms/orig: (0.3,0.5) res2s: 119521..207417 iter 7: time=655.65 rNorms/orig: (0.3,0.4) res2s: 128136..213430 iter 8: time=646.78 rNorms/orig: (0.2,0.3) res2s: 133858..217825 iter 9: time=638.28 rNorms/orig: (0.1,0.2) res2s: 138169..220512 iter 10: time=644.77 rNorms/orig: (0.1,0.2) res2s: 141157..222413 iter 11: time=649.55 rNorms/orig: (0.08,0.1) res2s: 143188..223657 iter 12: time=637.50 rNorms/orig: (0.07,0.1) res2s: 144385..224485 iter 13: time=627.18 rNorms/orig: (0.05,0.09) res2s: 145162..224998 iter 14: time=632.54 rNorms/orig: (0.04,0.07) res2s: 145784..225369 iter 15: time=631.81 rNorms/orig: (0.03,0.05) res2s: 146119..225574 iter 16: time=622.18 rNorms/orig: (0.02,0.04) res2s: 146351..225718 iter 17: time=626.42 rNorms/orig: (0.02,0.03) res2s: 146502..225813 iter 18: time=640.87 rNorms/orig: (0.01,0.02) res2s: 146602..225879 iter 19: time=636.00 rNorms/orig: (0.008,0.02) res2s: 146659..225916 iter 20: time=632.16 rNorms/orig: (0.007,0.02) res2s: 146698..225941 iter 21: time=639.53 rNorms/orig: (0.005,0.01) res2s: 146726..225954 iter 22: time=637.87 rNorms/orig: (0.004,0.01) res2s: 146743..225965 iter 23: time=627.71 rNorms/orig: (0.003,0.008) res2s: 146753..225972 iter 24: time=624.84 rNorms/orig: (0.002,0.006) res2s: 146761..225975 iter 25: time=623.68 rNorms/orig: (0.002,0.004) res2s: 146766..225978 iter 26: time=635.34 rNorms/orig: (0.001,0.004) res2s: 146769..225979 iter 27: time=623.54 rNorms/orig: (0.0008,0.003) res2s: 146771..225980 iter 28: time=620.29 rNorms/orig: (0.0006,0.002) res2s: 146772..225980 iter 29: time=642.10 rNorms/orig: (0.0004,0.002) res2s: 146773..225981 iter 30: time=652.33 rNorms/orig: (0.0003,0.001) res2s: 146773..225981 iter 31: time=658.44 rNorms/orig: (0.0002,0.001) res2s: 146773..225981 iter 32: time=648.70 rNorms/orig: (0.0002,0.0008) res2s: 146774..225981 iter 33: time=651.50 rNorms/orig: (0.0001,0.0006) res2s: 146774..225981 iter 34: time=659.87 rNorms/orig: (9e-05,0.0005) res2s: 146774..225981 Converged at iter 34: rNorms/orig all < CGtol=0.0005 Time breakdown: dgemm = 74.2%, memory/overhead = 25.8% AvgPro: 2.767 AvgRetro: 2.695 Calibration: 1.027 (0.002) (30 SNPs) Ratio of medians: 1.022 Median of ratios: 1.023 Time for computing infinitesimal model assoc stats = 22195.1 sec === Estimating chip LD Scores using 400 indivs === Time for estimating chip LD Scores = 14.5855 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 > 445.2) # of SNPs remaining after outlier window removal: 555710/579570 Intercept of LD Score regression for ref stats: 1.222 (0.024) Estimated attenuation: 0.159 (0.017) Intercept of LD Score regression for cur stats: 1.172 (0.018) Calibration factor (ref/cur) to multiply by: 1.042 (0.006) LINREG intercept inflation = 0.95952 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 = 356130 Singular values of covariate matrix: S[0] = 2.03198e+06 S[1] = 4263.61 S[2] = 420.424 S[3] = 262.747 S[4] = 178.943 S[5] = 173.968 S[6] = 164.117 S[7] = 156.683 S[8] = 152.818 S[9] = 148.089 S[10] = 145.71 S[11] = 137.659 S[12] = 130.529 S[13] = 128.709 S[14] = 126.369 S[15] = 121.504 S[16] = 118.471 S[17] = 116.491 S[18] = 113.093 S[19] = 104.011 S[20] = 100.331 S[21] = 89.2065 S[22] = 40.2031 S[23] = 21.4628 S[24] = 17.2877 S[25] = 0.882387 S[26] = 0.882034 S[27] = 0.881423 S[28] = 0.880986 S[29] = 0.880819 S[30] = 0.880728 S[31] = 0.880552 S[32] = 0.880047 S[33] = 0.879977 S[34] = 0.879349 S[35] = 0.879227 S[36] = 0.878996 S[37] = 0.878596 S[38] = 0.8784 S[39] = 0.877809 S[40] = 0.877407 S[41] = 0.877172 S[42] = 0.876736 S[43] = 0.8624 S[44] = 0.794962 S[45] = 3.84414e-12 S[46] = 4.71835e-13 S[47] = 2.94658e-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: 354046.941210 Dimension of all-1s proj space (Nused-1): 356129 Beginning variational Bayes iter 1: time=621.36 for 18 active reps iter 2: time=421.32 for 18 active reps approxLL diffs: (41574.18,47844.08) iter 3: time=419.44 for 18 active reps approxLL diffs: (6861.14,10231.55) iter 4: time=418.82 for 18 active reps approxLL diffs: (1887.37,3985.84) iter 5: time=418.57 for 18 active reps approxLL diffs: (708.04,2043.02) iter 6: time=421.26 for 18 active reps approxLL diffs: (317.83,1233.94) iter 7: time=424.80 for 18 active reps approxLL diffs: (160.55,771.50) iter 8: time=419.59 for 18 active reps approxLL diffs: (88.50,535.56) iter 9: time=419.08 for 18 active reps approxLL diffs: (52.38,358.71) iter 10: time=421.28 for 18 active reps approxLL diffs: (32.97,253.58) iter 11: time=418.59 for 18 active reps approxLL diffs: (21.84,204.93) iter 12: time=410.94 for 18 active reps approxLL diffs: (15.04,174.57) iter 13: time=409.96 for 18 active reps approxLL diffs: (10.71,133.40) iter 14: time=421.49 for 18 active reps approxLL diffs: (7.90,96.07) iter 15: time=436.66 for 18 active reps approxLL diffs: (6.02,94.45) iter 16: time=431.67 for 18 active reps approxLL diffs: (4.70,78.61) iter 17: time=432.75 for 18 active reps approxLL diffs: (3.71,62.64) iter 18: time=428.76 for 18 active reps approxLL diffs: (2.97,52.47) iter 19: time=434.72 for 18 active reps approxLL diffs: (2.43,44.72) iter 20: time=439.04 for 18 active reps approxLL diffs: (2.03,40.91) iter 21: time=426.25 for 18 active reps approxLL diffs: (1.74,30.39) iter 22: time=417.43 for 18 active reps approxLL diffs: (1.50,30.06) iter 23: time=423.21 for 18 active reps approxLL diffs: (1.30,35.52) iter 24: time=420.03 for 18 active reps approxLL diffs: (1.13,28.35) iter 25: time=423.76 for 18 active reps approxLL diffs: (1.00,22.92) iter 26: time=422.77 for 18 active reps approxLL diffs: (0.89,16.66) iter 27: time=421.59 for 18 active reps approxLL diffs: (0.80,15.73) iter 28: time=434.09 for 18 active reps approxLL diffs: (0.72,15.57) iter 29: time=437.25 for 18 active reps approxLL diffs: (0.65,15.58) iter 30: time=440.18 for 18 active reps approxLL diffs: (0.59,12.48) iter 31: time=447.44 for 18 active reps approxLL diffs: (0.54,11.35) iter 32: time=461.04 for 18 active reps approxLL diffs: (0.50,9.26) iter 33: time=466.32 for 18 active reps approxLL diffs: (0.46,10.95) iter 34: time=468.42 for 18 active reps approxLL diffs: (0.43,12.30) iter 35: time=469.20 for 18 active reps approxLL diffs: (0.39,11.75) iter 36: time=472.49 for 18 active reps approxLL diffs: (0.35,10.19) iter 37: time=476.41 for 18 active reps approxLL diffs: (0.32,10.29) iter 38: time=475.33 for 18 active reps approxLL diffs: (0.29,9.80) iter 39: time=480.64 for 18 active reps approxLL diffs: (0.27,6.39) iter 40: time=470.46 for 18 active reps approxLL diffs: (0.26,4.98) iter 41: time=469.81 for 18 active reps approxLL diffs: (0.24,5.28) iter 42: time=473.04 for 18 active reps approxLL diffs: (0.22,6.03) iter 43: time=476.26 for 18 active reps approxLL diffs: (0.20,8.54) iter 44: time=475.52 for 18 active reps approxLL diffs: (0.19,4.21) iter 45: time=476.48 for 18 active reps approxLL diffs: (0.17,3.66) iter 46: time=477.57 for 18 active reps approxLL diffs: (0.13,4.78) iter 47: time=483.93 for 18 active reps approxLL diffs: (0.11,5.72) iter 48: time=425.98 for 18 active reps approxLL diffs: (0.10,8.84) iter 49: time=416.20 for 18 active reps approxLL diffs: (0.10,2.98) iter 50: time=412.55 for 18 active reps approxLL diffs: (0.11,7.07) iter 51: time=416.57 for 18 active reps approxLL diffs: (0.10,5.37) iter 52: time=416.18 for 18 active reps approxLL diffs: (0.08,2.31) iter 53: time=420.83 for 18 active reps approxLL diffs: (0.06,5.21) iter 54: time=416.60 for 18 active reps approxLL diffs: (0.06,7.29) iter 55: time=436.08 for 18 active reps approxLL diffs: (0.06,6.77) iter 56: time=431.49 for 18 active reps approxLL diffs: (0.06,3.82) iter 57: time=420.29 for 18 active reps approxLL diffs: (0.05,1.12) iter 58: time=438.64 for 18 active reps approxLL diffs: (0.05,0.78) iter 59: time=431.41 for 18 active reps approxLL diffs: (0.04,1.36) iter 60: time=427.52 for 18 active reps approxLL diffs: (0.03,1.45) iter 61: time=420.07 for 18 active reps approxLL diffs: (0.03,1.04) iter 62: time=410.90 for 18 active reps approxLL diffs: (0.02,1.00) iter 63: time=423.29 for 18 active reps approxLL diffs: (0.02,1.05) iter 64: time=434.08 for 18 active reps approxLL diffs: (0.02,1.31) iter 65: time=427.73 for 18 active reps approxLL diffs: (0.01,5.92) iter 66: time=425.74 for 18 active reps approxLL diffs: (0.01,0.66) iter 67: time=423.00 for 18 active reps approxLL diffs: (0.01,1.35) iter 68: time=412.52 for 17 active reps approxLL diffs: (0.01,1.28) iter 69: time=413.86 for 17 active reps approxLL diffs: (0.01,0.68) iter 70: time=404.34 for 17 active reps approxLL diffs: (0.01,4.46) iter 71: time=399.07 for 17 active reps approxLL diffs: (0.01,1.69) iter 72: time=409.61 for 17 active reps approxLL diffs: (0.01,0.50) iter 73: time=410.78 for 17 active reps approxLL diffs: (0.01,0.29) iter 74: time=395.68 for 17 active reps approxLL diffs: (0.01,0.17) iter 75: time=371.93 for 16 active reps approxLL diffs: (0.01,0.31) iter 76: time=366.35 for 14 active reps approxLL diffs: (0.01,0.66) iter 77: time=363.58 for 13 active reps approxLL diffs: (0.01,0.65) iter 78: time=332.56 for 12 active reps approxLL diffs: (0.01,0.63) iter 79: time=328.42 for 12 active reps approxLL diffs: (0.01,0.87) iter 80: time=330.74 for 12 active reps approxLL diffs: (0.01,0.78) iter 81: time=332.74 for 12 active reps approxLL diffs: (0.01,0.25) iter 82: time=332.95 for 12 active reps approxLL diffs: (0.01,0.59) iter 83: time=337.15 for 12 active reps approxLL diffs: (0.01,2.44) iter 84: time=340.42 for 11 active reps approxLL diffs: (0.01,1.31) iter 85: time=341.59 for 11 active reps approxLL diffs: (0.01,1.31) iter 86: time=338.69 for 11 active reps approxLL diffs: (0.01,0.69) iter 87: time=338.16 for 11 active reps approxLL diffs: (0.01,1.25) iter 88: time=355.31 for 11 active reps approxLL diffs: (0.01,1.49) iter 89: time=331.02 for 10 active reps approxLL diffs: (0.01,1.96) iter 90: time=323.69 for 10 active reps approxLL diffs: (0.01,1.08) iter 91: time=262.41 for 8 active reps approxLL diffs: (0.01,0.90) iter 92: time=285.17 for 7 active reps approxLL diffs: (0.01,1.37) iter 93: time=284.81 for 7 active reps approxLL diffs: (0.01,1.43) iter 94: time=286.13 for 7 active reps approxLL diffs: (0.01,1.00) iter 95: time=293.49 for 7 active reps approxLL diffs: (0.01,0.33) iter 96: time=292.55 for 7 active reps approxLL diffs: (0.01,0.11) iter 97: time=293.33 for 7 active reps approxLL diffs: (0.01,0.05) iter 98: time=275.79 for 6 active reps approxLL diffs: (0.01,0.05) iter 99: time=259.41 for 5 active reps approxLL diffs: (0.01,0.08) iter 100: time=244.06 for 5 active reps approxLL diffs: (0.01,0.14) iter 101: time=247.26 for 5 active reps approxLL diffs: (0.01,0.20) iter 102: time=234.63 for 5 active reps approxLL diffs: (0.01,0.17) iter 103: time=241.57 for 5 active reps approxLL diffs: (0.01,0.11) iter 104: time=249.39 for 5 active reps approxLL diffs: (0.01,0.10) iter 105: time=268.31 for 5 active reps approxLL diffs: (0.01,0.12) iter 106: time=261.64 for 5 active reps approxLL diffs: (0.01,0.29) iter 107: time=234.20 for 4 active reps approxLL diffs: (0.01,1.57) iter 108: time=230.24 for 4 active reps approxLL diffs: (0.01,1.08) iter 109: time=233.25 for 4 active reps approxLL diffs: (0.01,0.55) iter 110: time=233.03 for 4 active reps approxLL diffs: (0.01,0.86) iter 111: time=230.43 for 4 active reps approxLL diffs: (0.01,0.48) iter 112: time=230.35 for 4 active reps approxLL diffs: (0.01,0.27) iter 113: time=232.68 for 4 active reps approxLL diffs: (0.01,0.27) iter 114: time=235.40 for 4 active reps approxLL diffs: (0.01,0.37) iter 115: time=246.19 for 4 active reps approxLL diffs: (0.01,0.49) iter 116: time=238.65 for 2 active reps approxLL diffs: (0.01,0.55) iter 117: time=237.31 for 2 active reps approxLL diffs: (0.01,0.34) iter 118: time=241.14 for 2 active reps approxLL diffs: (0.01,0.12) iter 119: time=249.18 for 2 active reps approxLL diffs: (0.01,0.05) iter 120: time=260.67 for 2 active reps approxLL diffs: (0.01,0.03) iter 121: time=310.12 for 1 active reps approxLL diffs: (0.02,0.02) iter 122: time=329.34 for 1 active reps approxLL diffs: (0.02,0.02) iter 123: time=323.15 for 1 active reps approxLL diffs: (0.01,0.01) iter 124: time=271.84 for 1 active reps approxLL diffs: (0.01,0.01) iter 125: time=322.58 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 125: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 77.4%, memory/overhead = 22.6% Computing predictions on left-out cross-validation fold Time for computing predictions = 8534.53 sec Average PVEs obtained by param pairs tested (high to low): f2=0.3, p=0.02: 0.398564 f2=0.3, p=0.01: 0.398255 f2=0.1, p=0.01: 0.397384 ... f2=0.5, p=0.5: 0.285807 Detailed CV fold results: Absolute prediction MSE baseline (covariates only): 0.984574 Absolute prediction MSE using standard LMM: 0.703176 Absolute prediction MSE, fold-best f2=0.3, p=0.02: 0.592158 Absolute pred MSE using f2=0.5, p=0.5: 0.703176 Absolute pred MSE using f2=0.5, p=0.2: 0.653390 Absolute pred MSE using f2=0.5, p=0.1: 0.623689 Absolute pred MSE using f2=0.5, p=0.05: 0.607370 Absolute pred MSE using f2=0.5, p=0.02: 0.600234 Absolute pred MSE using f2=0.5, p=0.01: 0.599947 Absolute pred MSE using f2=0.3, p=0.5: 0.683237 Absolute pred MSE using f2=0.3, p=0.2: 0.629472 Absolute pred MSE using f2=0.3, p=0.1: 0.605730 Absolute pred MSE using f2=0.3, p=0.05: 0.595810 Absolute pred MSE using f2=0.3, p=0.02: 0.592158 Absolute pred MSE using f2=0.3, p=0.01: 0.592463 Absolute pred MSE using f2=0.1, p=0.5: 0.660115 Absolute pred MSE using f2=0.1, p=0.2: 0.612430 Absolute pred MSE using f2=0.1, p=0.1: 0.598146 Absolute pred MSE using f2=0.1, p=0.05: 0.594787 Absolute pred MSE using f2=0.1, p=0.02: 0.593666 Absolute pred MSE using f2=0.1, p=0.01: 0.593320 ====> End CV fold 1: 18 remaining param pair(s) <==== Estimated proportion of variance explained using inf model: 0.286 Relative improvement in prediction MSE using non-inf model: 0.158 Optimal mixture parameters according to CV: f2 = 0.3, p = 0.02 Time for estimating mixture parameters = 60852.5 sec === Computing Bayesian mixed model assoc stats with mixture prior === Assigning SNPs to 23 chunks for leave-out analysis Each chunk is excluded when testing SNPs belonging to the chunk Beginning variational Bayes iter 1: time=667.74 for 23 active reps iter 2: time=461.77 for 23 active reps approxLL diffs: (49857.76,57413.13) iter 3: time=465.11 for 23 active reps approxLL diffs: (10955.76,12836.23) iter 4: time=468.34 for 23 active reps approxLL diffs: (4309.34,5022.31) iter 5: time=463.87 for 23 active reps approxLL diffs: (2118.22,2527.89) iter 6: time=462.42 for 23 active reps approxLL diffs: (1231.54,1512.57) iter 7: time=465.97 for 23 active reps approxLL diffs: (809.45,959.01) iter 8: time=460.77 for 23 active reps approxLL diffs: (534.82,636.27) iter 9: time=464.45 for 23 active reps approxLL diffs: (388.95,469.25) iter 10: time=464.02 for 23 active reps approxLL diffs: (277.26,352.58) iter 11: time=469.68 for 23 active reps approxLL diffs: (212.72,260.02) iter 12: time=464.57 for 23 active reps approxLL diffs: (161.00,196.64) iter 13: time=465.39 for 23 active reps approxLL diffs: (120.03,149.39) iter 14: time=466.85 for 23 active reps approxLL diffs: (93.46,134.04) iter 15: time=467.70 for 23 active reps approxLL diffs: (85.69,110.45) iter 16: time=468.95 for 23 active reps approxLL diffs: (67.60,94.77) iter 17: time=465.20 for 23 active reps approxLL diffs: (56.17,80.96) iter 18: time=457.96 for 23 active reps approxLL diffs: (43.47,61.67) iter 19: time=455.16 for 23 active reps approxLL diffs: (33.04,51.88) iter 20: time=454.98 for 23 active reps approxLL diffs: (28.28,51.99) iter 21: time=456.67 for 23 active reps approxLL diffs: (22.88,43.30) iter 22: time=458.95 for 23 active reps approxLL diffs: (20.58,28.93) iter 23: time=458.54 for 23 active reps approxLL diffs: (14.88,25.79) iter 24: time=458.88 for 23 active reps approxLL diffs: (12.56,21.05) iter 25: time=459.81 for 23 active reps approxLL diffs: (9.02,19.43) iter 26: time=460.84 for 23 active reps approxLL diffs: (6.61,15.99) iter 27: time=458.23 for 23 active reps approxLL diffs: (5.41,13.54) iter 28: time=460.71 for 23 active reps approxLL diffs: (4.56,14.00) iter 29: time=455.01 for 23 active reps approxLL diffs: (4.89,13.59) iter 30: time=444.70 for 23 active reps approxLL diffs: (3.84,11.51) iter 31: time=442.63 for 23 active reps approxLL diffs: (2.85,9.81) iter 32: time=443.36 for 23 active reps approxLL diffs: (2.61,11.31) iter 33: time=439.74 for 23 active reps approxLL diffs: (1.75,8.01) iter 34: time=443.11 for 23 active reps approxLL diffs: (1.62,6.82) iter 35: time=444.06 for 23 active reps approxLL diffs: (1.12,9.32) iter 36: time=444.03 for 23 active reps approxLL diffs: (1.02,7.78) iter 37: time=444.20 for 23 active reps approxLL diffs: (1.11,10.61) iter 38: time=441.65 for 23 active reps approxLL diffs: (1.30,9.46) iter 39: time=438.46 for 23 active reps approxLL diffs: (1.43,14.62) iter 40: time=438.47 for 23 active reps approxLL diffs: (1.18,9.65) iter 41: time=438.82 for 23 active reps approxLL diffs: (1.23,7.94) iter 42: time=441.84 for 23 active reps approxLL diffs: (0.90,7.16) iter 43: time=440.42 for 23 active reps approxLL diffs: (0.95,6.34) iter 44: time=439.33 for 23 active reps approxLL diffs: (1.35,5.23) iter 45: time=451.15 for 23 active reps approxLL diffs: (1.18,5.91) iter 46: time=475.03 for 23 active reps approxLL diffs: (0.73,6.00) iter 47: time=462.45 for 23 active reps approxLL diffs: (0.62,6.52) iter 48: time=479.58 for 23 active reps approxLL diffs: (1.05,5.61) iter 49: time=459.30 for 23 active reps approxLL diffs: (0.93,7.39) iter 50: time=455.23 for 23 active reps approxLL diffs: (0.96,5.87) iter 51: time=456.92 for 23 active reps approxLL diffs: (1.07,3.90) iter 52: time=461.59 for 23 active reps approxLL diffs: (0.94,5.22) iter 53: time=448.28 for 23 active reps approxLL diffs: (0.67,6.43) iter 54: time=465.65 for 23 active reps approxLL diffs: (0.51,4.26) iter 55: time=466.97 for 23 active reps approxLL diffs: (0.42,3.93) iter 56: time=491.06 for 23 active reps approxLL diffs: (0.37,3.83) iter 57: time=476.13 for 23 active reps approxLL diffs: (0.33,5.14) iter 58: time=486.55 for 23 active reps approxLL diffs: (0.29,4.56) iter 59: time=542.88 for 23 active reps approxLL diffs: (0.28,4.33) iter 60: time=522.09 for 23 active reps approxLL diffs: (0.31,4.90) iter 61: time=530.59 for 23 active reps approxLL diffs: (0.27,6.05) iter 62: time=540.43 for 23 active reps approxLL diffs: (0.23,3.43) iter 63: time=547.13 for 23 active reps approxLL diffs: (0.21,4.94) iter 64: time=584.52 for 23 active reps approxLL diffs: (0.18,4.31) iter 65: time=568.36 for 23 active reps approxLL diffs: (0.12,4.09) iter 66: time=555.05 for 23 active reps approxLL diffs: (0.10,3.75) iter 67: time=552.45 for 23 active reps approxLL diffs: (0.08,2.87) iter 68: time=602.52 for 23 active reps approxLL diffs: (0.07,4.61) iter 69: time=606.04 for 23 active reps approxLL diffs: (0.06,3.16) iter 70: time=589.43 for 23 active reps approxLL diffs: (0.05,2.61) iter 71: time=649.41 for 23 active reps approxLL diffs: (0.04,2.88) iter 72: time=635.17 for 23 active reps approxLL diffs: (0.04,3.59) iter 73: time=610.13 for 23 active reps approxLL diffs: (0.03,3.43) iter 74: time=603.13 for 23 active reps approxLL diffs: (0.03,3.85) iter 75: time=590.10 for 23 active reps approxLL diffs: (0.02,4.96) iter 76: time=578.47 for 23 active reps approxLL diffs: (0.02,2.78) iter 77: time=551.59 for 23 active reps approxLL diffs: (0.02,2.47) iter 78: time=520.62 for 23 active reps approxLL diffs: (0.01,3.22) iter 79: time=513.58 for 23 active reps approxLL diffs: (0.01,2.59) iter 80: time=507.34 for 23 active reps approxLL diffs: (0.01,2.35) iter 81: time=483.72 for 22 active reps approxLL diffs: (0.02,2.52) iter 82: time=474.46 for 22 active reps approxLL diffs: (0.02,2.70) iter 83: time=470.06 for 22 active reps approxLL diffs: (0.01,2.68) iter 84: time=448.63 for 21 active reps approxLL diffs: (0.02,3.36) iter 85: time=430.72 for 21 active reps approxLL diffs: (0.02,2.49) iter 86: time=423.33 for 21 active reps approxLL diffs: (0.01,2.60) iter 87: time=437.24 for 21 active reps approxLL diffs: (0.01,3.26) iter 88: time=428.11 for 21 active reps approxLL diffs: (0.01,2.82) iter 89: time=409.04 for 20 active reps approxLL diffs: (0.01,2.29) iter 90: time=414.11 for 18 active reps approxLL diffs: (0.02,2.21) iter 91: time=428.24 for 18 active reps approxLL diffs: (0.01,1.72) iter 92: time=425.95 for 18 active reps approxLL diffs: (0.01,1.40) iter 93: time=432.87 for 18 active reps approxLL diffs: (0.01,2.48) iter 94: time=411.35 for 17 active reps approxLL diffs: (0.01,4.15) iter 95: time=395.27 for 16 active reps approxLL diffs: (0.01,3.10) iter 96: time=411.95 for 16 active reps approxLL diffs: (0.01,2.29) iter 97: time=386.88 for 16 active reps approxLL diffs: (0.01,3.73) iter 98: time=387.80 for 16 active reps approxLL diffs: (0.01,2.59) iter 99: time=395.24 for 15 active reps approxLL diffs: (0.01,2.60) iter 100: time=388.40 for 15 active reps approxLL diffs: (0.01,2.42) iter 101: time=368.02 for 14 active reps approxLL diffs: (0.01,2.13) iter 102: time=352.49 for 13 active reps approxLL diffs: (0.00,1.85) iter 103: time=344.15 for 11 active reps approxLL diffs: (0.01,1.68) iter 104: time=308.66 for 9 active reps approxLL diffs: (0.02,2.04) iter 105: time=315.02 for 9 active reps approxLL diffs: (0.02,2.98) iter 106: time=308.43 for 9 active reps approxLL diffs: (0.02,3.83) iter 107: time=303.35 for 9 active reps approxLL diffs: (0.01,1.60) iter 108: time=263.66 for 8 active reps approxLL diffs: (0.01,0.96) iter 109: time=283.14 for 7 active reps approxLL diffs: (0.00,0.79) iter 110: time=245.51 for 5 active reps approxLL diffs: (0.04,0.69) iter 111: time=243.58 for 5 active reps approxLL diffs: (0.04,0.63) iter 112: time=246.52 for 5 active reps approxLL diffs: (0.03,0.61) iter 113: time=243.75 for 5 active reps approxLL diffs: (0.03,0.66) iter 114: time=245.39 for 5 active reps approxLL diffs: (0.02,0.74) iter 115: time=248.71 for 5 active reps approxLL diffs: (0.02,0.77) iter 116: time=246.30 for 5 active reps approxLL diffs: (0.02,0.66) iter 117: time=242.89 for 5 active reps approxLL diffs: (0.02,0.39) iter 118: time=247.53 for 5 active reps approxLL diffs: (0.01,0.27) iter 119: time=247.21 for 5 active reps approxLL diffs: (0.01,0.22) iter 120: time=254.34 for 5 active reps approxLL diffs: (0.01,0.19) iter 121: time=231.71 for 4 active reps approxLL diffs: (0.02,0.16) iter 122: time=225.53 for 4 active reps approxLL diffs: (0.01,0.14) iter 123: time=220.80 for 4 active reps approxLL diffs: (0.01,0.12) iter 124: time=219.80 for 4 active reps approxLL diffs: (0.01,0.11) iter 125: time=224.73 for 4 active reps approxLL diffs: (0.01,0.09) iter 126: time=250.60 for 3 active reps approxLL diffs: (0.01,0.08) iter 127: time=243.21 for 3 active reps approxLL diffs: (0.01,0.07) iter 128: time=245.56 for 3 active reps approxLL diffs: (0.01,0.06) iter 129: time=230.31 for 3 active reps approxLL diffs: (0.01,0.07) iter 130: time=227.75 for 3 active reps approxLL diffs: (0.01,0.12) iter 131: time=213.02 for 2 active reps approxLL diffs: (0.05,0.11) iter 132: time=224.86 for 2 active reps approxLL diffs: (0.05,0.05) iter 133: time=214.71 for 2 active reps approxLL diffs: (0.01,0.05) iter 134: time=211.91 for 2 active reps approxLL diffs: (0.01,0.08) iter 135: time=186.80 for 1 active reps approxLL diffs: (0.14,0.14) iter 136: time=185.73 for 1 active reps approxLL diffs: (0.25,0.25) iter 137: time=182.98 for 1 active reps approxLL diffs: (0.35,0.35) iter 138: time=182.88 for 1 active reps approxLL diffs: (0.43,0.43) iter 139: time=187.38 for 1 active reps approxLL diffs: (0.30,0.30) iter 140: time=187.07 for 1 active reps approxLL diffs: (0.10,0.10) iter 141: time=191.47 for 1 active reps approxLL diffs: (0.02,0.02) iter 142: time=197.99 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 142: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 80.2%, memory/overhead = 19.8% 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 > 445.2) # of SNPs remaining after outlier window removal: 555710/579570 Intercept of LD Score regression for ref stats: 1.222 (0.024) Estimated attenuation: 0.159 (0.017) Intercept of LD Score regression for cur stats: 1.230 (0.027) Calibration factor (ref/cur) to multiply by: 0.993 (0.003) Time for computing Bayesian mixed model assoc stats = 58410.3 sec === Computing and writing betas for risk prediction === Beginning variational Bayes iter 1: time=428.42 for 1 active reps iter 2: time=205.44 for 1 active reps approxLL diffs: (57495.04,57495.04) iter 3: time=212.53 for 1 active reps approxLL diffs: (12832.76,12832.76) iter 4: time=222.35 for 1 active reps approxLL diffs: (5022.59,5022.59) iter 5: time=223.04 for 1 active reps approxLL diffs: (2524.33,2524.33) iter 6: time=215.81 for 1 active reps approxLL diffs: (1508.98,1508.98) iter 7: time=213.38 for 1 active reps approxLL diffs: (954.62,954.62) iter 8: time=203.55 for 1 active reps approxLL diffs: (626.21,626.21) iter 9: time=205.08 for 1 active reps approxLL diffs: (450.00,450.00) iter 10: time=205.55 for 1 active reps approxLL diffs: (340.88,340.88) iter 11: time=217.43 for 1 active reps approxLL diffs: (250.58,250.58) iter 12: time=216.30 for 1 active reps approxLL diffs: (186.11,186.11) iter 13: time=212.51 for 1 active reps approxLL diffs: (129.81,129.81) iter 14: time=248.77 for 1 active reps approxLL diffs: (106.25,106.25) iter 15: time=281.88 for 1 active reps approxLL diffs: (93.07,93.07) iter 16: time=276.08 for 1 active reps approxLL diffs: (81.54,81.54) iter 17: time=286.10 for 1 active reps approxLL diffs: (71.07,71.07) iter 18: time=270.82 for 1 active reps approxLL diffs: (52.88,52.88) iter 19: time=279.59 for 1 active reps approxLL diffs: (43.90,43.90) iter 20: time=273.71 for 1 active reps approxLL diffs: (33.52,33.52) iter 21: time=293.08 for 1 active reps approxLL diffs: (24.43,24.43) iter 22: time=300.56 for 1 active reps approxLL diffs: (21.60,21.60) iter 23: time=291.99 for 1 active reps approxLL diffs: (22.91,22.91) iter 24: time=287.11 for 1 active reps approxLL diffs: (17.76,17.76) iter 25: time=290.54 for 1 active reps approxLL diffs: (14.33,14.33) iter 26: time=297.34 for 1 active reps approxLL diffs: (10.43,10.43) iter 27: time=295.10 for 1 active reps approxLL diffs: (10.14,10.14) iter 28: time=294.67 for 1 active reps approxLL diffs: (8.68,8.68) iter 29: time=305.82 for 1 active reps approxLL diffs: (6.31,6.31) iter 30: time=294.71 for 1 active reps approxLL diffs: (5.27,5.27) iter 31: time=297.24 for 1 active reps approxLL diffs: (5.13,5.13) iter 32: time=298.41 for 1 active reps approxLL diffs: (5.47,5.47) iter 33: time=283.45 for 1 active reps approxLL diffs: (3.27,3.27) iter 34: time=292.12 for 1 active reps approxLL diffs: (4.41,4.41) iter 35: time=286.94 for 1 active reps approxLL diffs: (4.60,4.60) iter 36: time=302.52 for 1 active reps approxLL diffs: (7.18,7.18) iter 37: time=294.80 for 1 active reps approxLL diffs: (7.82,7.82) iter 38: time=292.39 for 1 active reps approxLL diffs: (9.15,9.15) iter 39: time=305.22 for 1 active reps approxLL diffs: (4.75,4.75) iter 40: time=295.52 for 1 active reps approxLL diffs: (3.10,3.10) iter 41: time=285.10 for 1 active reps approxLL diffs: (3.13,3.13) iter 42: time=280.72 for 1 active reps approxLL diffs: (4.31,4.31) iter 43: time=291.12 for 1 active reps approxLL diffs: (5.76,5.76) iter 44: time=295.26 for 1 active reps approxLL diffs: (5.39,5.39) iter 45: time=306.89 for 1 active reps approxLL diffs: (4.89,4.89) iter 46: time=305.18 for 1 active reps approxLL diffs: (5.06,5.06) iter 47: time=299.07 for 1 active reps approxLL diffs: (5.25,5.25) iter 48: time=311.28 for 1 active reps approxLL diffs: (3.80,3.80) iter 49: time=304.89 for 1 active reps approxLL diffs: (3.15,3.15) iter 50: time=305.10 for 1 active reps approxLL diffs: (2.22,2.22) iter 51: time=320.62 for 1 active reps approxLL diffs: (1.77,1.77) iter 52: time=315.17 for 1 active reps approxLL diffs: (1.57,1.57) iter 53: time=307.28 for 1 active reps approxLL diffs: (1.57,1.57) iter 54: time=291.80 for 1 active reps approxLL diffs: (2.14,2.14) iter 55: time=303.55 for 1 active reps approxLL diffs: (3.39,3.39) iter 56: time=303.09 for 1 active reps approxLL diffs: (3.57,3.57) iter 57: time=299.64 for 1 active reps approxLL diffs: (3.42,3.42) iter 58: time=293.27 for 1 active reps approxLL diffs: (3.24,3.24) iter 59: time=300.27 for 1 active reps approxLL diffs: (2.98,2.98) iter 60: time=290.31 for 1 active reps approxLL diffs: (2.70,2.70) iter 61: time=288.24 for 1 active reps approxLL diffs: (2.27,2.27) iter 62: time=289.14 for 1 active reps approxLL diffs: (1.92,1.92) iter 63: time=299.59 for 1 active reps approxLL diffs: (2.28,2.28) iter 64: time=299.97 for 1 active reps approxLL diffs: (4.43,4.43) iter 65: time=295.60 for 1 active reps approxLL diffs: (3.04,3.04) iter 66: time=299.92 for 1 active reps approxLL diffs: (1.29,1.29) iter 67: time=298.11 for 1 active reps approxLL diffs: (0.98,0.98) iter 68: time=299.16 for 1 active reps approxLL diffs: (0.83,0.83) iter 69: time=297.48 for 1 active reps approxLL diffs: (0.73,0.73) iter 70: time=296.41 for 1 active reps approxLL diffs: (0.65,0.65) iter 71: time=302.98 for 1 active reps approxLL diffs: (0.58,0.58) iter 72: time=293.94 for 1 active reps approxLL diffs: (0.51,0.51) iter 73: time=298.14 for 1 active reps approxLL diffs: (0.45,0.45) iter 74: time=295.35 for 1 active reps approxLL diffs: (0.39,0.39) iter 75: time=289.74 for 1 active reps approxLL diffs: (0.34,0.34) iter 76: time=288.75 for 1 active reps approxLL diffs: (0.30,0.30) iter 77: time=291.73 for 1 active reps approxLL diffs: (0.26,0.26) iter 78: time=296.50 for 1 active reps approxLL diffs: (0.22,0.22) iter 79: time=290.29 for 1 active reps approxLL diffs: (0.19,0.19) iter 80: time=290.91 for 1 active reps approxLL diffs: (0.16,0.16) iter 81: time=292.19 for 1 active reps approxLL diffs: (0.14,0.14) iter 82: time=289.66 for 1 active reps approxLL diffs: (0.12,0.12) iter 83: time=281.78 for 1 active reps approxLL diffs: (0.10,0.10) iter 84: time=284.35 for 1 active reps approxLL diffs: (0.09,0.09) iter 85: time=282.71 for 1 active reps approxLL diffs: (0.08,0.08) iter 86: time=281.99 for 1 active reps approxLL diffs: (0.07,0.07) iter 87: time=281.25 for 1 active reps approxLL diffs: (0.06,0.06) iter 88: time=288.02 for 1 active reps approxLL diffs: (0.05,0.05) iter 89: time=285.42 for 1 active reps approxLL diffs: (0.04,0.04) iter 90: time=286.90 for 1 active reps approxLL diffs: (0.04,0.04) iter 91: time=285.13 for 1 active reps approxLL diffs: (0.03,0.03) iter 92: time=286.12 for 1 active reps approxLL diffs: (0.03,0.03) iter 93: time=288.78 for 1 active reps approxLL diffs: (0.02,0.02) iter 94: time=300.60 for 1 active reps approxLL diffs: (0.02,0.02) iter 95: time=311.38 for 1 active reps approxLL diffs: (0.02,0.02) iter 96: time=307.75 for 1 active reps approxLL diffs: (0.01,0.01) iter 97: time=309.69 for 1 active reps approxLL diffs: (0.01,0.01) iter 98: time=319.34 for 1 active reps approxLL diffs: (0.01,0.01) Converged at iter 98: approxLL diffs each have been < LLtol=0.01 Time breakdown: dgemm = 57.4%, memory/overhead = 42.6% Time for computing and writing betas = 27978 sec Calibration stats: mean and lambdaGC (over SNPs used in GRM) (note that both should be >1 because of polygenicity) Mean LINREG: 3.13686 (705851 good SNPs) lambdaGC: 1.46464 Mean BOLT_LMM_INF: 3.93413 (705851 good SNPs) lambdaGC: 1.56355 Mean BOLT_LMM: 4.28726 (705851 good SNPs) lambdaGC: 1.58201 === Streaming genotypes to compute and write assoc stats at all SNPs === Time for streaming genotypes and writing output = 7624.56 sec Total elapsed time for analysis = 206094 sec