NAME

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

SYNOPSIS

       bolt [options]

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.

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

SEE ALSO

       https://data.broadinstitute.org/alkesgroup/BOLT-LMM/

COPYRIGHT

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