bolt - Efficient large cohorts genome-wide Bayesian mixed-model association testing

Copyright

       Copyright © 2014-2018 Harvard University.  Distributed under the GNU GPLv3+ open source license.

2.3.2                                               May 2018                                             BOLT(1)

Description

       The BOLT-LMM software package currently consists of two main algorithms, the BOLT-LMM algorithm for mixed
       model  association  testing,  and  the  BOLT-REML  algorithm  for  variance  components  analysis  (i.e.,
       partitioning of SNP-heritability and estimation of genetic correlations).

       The BOLT-LMM algorithm computes statistics for testing association between phenotype and genotypes  using
       a  linear  mixed  model.  By default, BOLT-LMM assumes a Bayesian mixture-of-normals prior for the random
       effect attributed to SNPs  other  than  the  one  being  tested.  This  model  generalizes  the  standard
       infinitesimal  mixed model used by previous mixed model association methods, providing an opportunity for
       increased power to detect associations while controlling false positives. Additionally, BOLT-LMM  applies
       algorithmic  advances  to compute mixed model association statistics much faster than eigendecomposition-
       based methods, both when using the Bayesian mixture model and when specialized to  standard  mixed  model
       association.

       The BOLT-REML algorithm estimates heritability explained by genotyped SNPs and genetic correlations among
       multiple  traits  measured on the same set of individuals. BOLT-REML applies variance components analysis
       to perform these tasks, supporting both multi-component modeling to partition SNP-heritability and multi-
       trait modeling to estimate correlations. BOLT-REML applies a Monte Carlo algorithm that  is  much  faster
       than eigendecomposition-based methods for variance components analysis at large sample sizes.

Name

       bolt - Efficient large cohorts genome-wide Bayesian mixed-model association testing

Options

-h [ --help ] print help message with typical options

       --helpFull
              print help message with full option list

       --bfile arg
              prefix of PLINK .fam, .bim, .bed files

       --bfilegz arg
              prefix of PLINK .fam.gz, .bim.gz, .bed.gz files

       --fam arg
              PLINK .fam file (note: file names ending in .gz are auto-[de]compressed)

       --bim arg
              PLINK .bim file(s); for >1, use multiple --bim and/or {i:j}, e.g., data.chr{1:22}.bim

       --bed arg
              PLINK .bed file(s); for >1, use multiple --bim and/or {i:j} expansion

       --geneticMapFile arg
              Oxford-format file for interpolating genetic distances: tables/genetic_map_hg##.txt.gz

       --remove arg
              file(s) listing individuals to ignore (no header; FID IID must be first two columns)

       --exclude arg
              file(s) listing SNPs to ignore (no header; SNP ID must be first column)

       --maxMissingPerSnp arg (=0.1)
              QC filter: max missing rate per SNP

       --maxMissingPerIndiv arg (=0.1) QC filter: max missing rate per person

       --phenoFile arg
              phenotype file (header required; FID IID must be first two columns)

       --phenoCol arg
              phenotype column header

       --phenoUseFam
              use last (6th) column of .fam file as phenotype

       --covarFile arg
              covariate file (header required; FID IID must be first two columns)

       --covarCol arg
              categorical covariate column(s); for >1, use multiple --covarCol and/or {i:j} expansion

       --qCovarCol arg
              quantitative covariate column(s); for >1, use multiple --qCovarCol and/or {i:j} expansion

       --covarUseMissingIndic
              include  samples with missing covariates in analysis via missing indicator method (default: ignore
              such samples)

       --reml run variance components analysis to precisely estimate heritability (but not compute assoc stats)

       --lmm  compute assoc stats under the inf model and with Bayesian non-inf prior (VB approx), if power gain
              expected

       --lmmInfOnly
              compute mixed model assoc stats under the infinitesimal model

       --lmmForceNonInf
              compute non-inf assoc stats even if BOLT-LMM expects no power gain

       --modelSnps arg
              file(s) listing SNPs to use in model (i.e., GRM) (default: use all non-excluded SNPs)

       --LDscoresFile arg
              LD Scores for calibration of Bayesian assoc stats: tables/LDSCORE.1000G_EUR.tab.gz

       --numThreads arg (=1)
              number of computational threads

       --statsFile arg
              output file for assoc stats at PLINK genotypes

       --dosageFile arg
              file(s) containing imputed SNP dosages to test for association (see manual for format)

       --dosageFidIidFile arg
              file listing FIDs and IIDs of samples in dosageFile(s), one line per sample

       --statsFileDosageSnps arg
              output file for assoc stats at dosage format genotypes

       --impute2FileList arg
              list of [chr file] pairs containing IMPUTE2 SNP probabilities to test for association

       --impute2FidIidFile arg
              file listing FIDs and IIDs of samples in IMPUTE2 files, one line per sample

       --impute2MinMAF arg (=0)
              MAF threshold on IMPUTE2 genotypes; lower-MAF SNPs will be ignored

       --bgenFile arg
              file(s) containing Oxford BGEN-format genotypes to test for association

       --sampleFile arg
              file containing Oxford sample file corresponding to BGEN file(s)

       --bgenSampleFileList arg
              list of [bgen sample] file pairs containing BGEN imputed variants to test for association

       --bgenMinMAF arg (=0)
              MAF threshold on Oxford BGEN-format genotypes; lower-MAF SNPs will be ignored

       --bgenMinINFO arg (=0)
              INFO threshold on Oxford BGEN-format genotypes; lower-INFO SNPs will be ignored

       --statsFileBgenSnps arg
              output file for assoc stats at BGEN-format genotypes

       --statsFileImpute2Snps arg
              output file for assoc stats at IMPUTE2 format genotypes

       --dosage2FileList arg
              list of [map  dosage]  file  pairs  with  2-dosage  SNP  probabilities  (Ricopili/plink2  --dosage
              format=2) to test for association

       --statsFileDosage2Snps arg
              output file for assoc stats at 2-dosage format genotypes