BOLT(1)

💖 BOLT(1) — the girlies' guide to genome-wide association testing 💖

okay bestie, sit down, hydrate, because we're about to talk about bolt, from the General Commands Manual, and honestly? it's giving icon behavior. this is the BOLT-LMM software package, and it does efficient large cohorts genome-wide Bayesian mixed-model association testing. yes that's a mouthful. yes it's also slay. let's go 🧬✨

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✨ NAME ✨

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

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🌸 SYNOPSIS 🌸

bolt [options]

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🦋 DESCRIPTION 🦋

so the BOLT-LMM software package currently has two main algorithms, and they're literally the main characters:

• the BOLT-LMM algorithm for mixed model association testing 💅
• the BOLT-REML algorithm for variance components analysis — like partitioning of SNP-heritability and estimation of genetic correlations 🎀

BOLT-LMM, the moment

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. lowkey this model generalizes the standard infinitesimal mixed model that previous mixed model association methods used, and it gives you an opportunity for increased power to detect associations while still controlling false positives. no cap.

and like, additionally? BOLT-LMM applies algorithmic advances to compute mixed model association statistics much faster than eigendecomposition-based methods — both when it's using the Bayesian mixture model and when it's specialized to standard mixed model association. speed AND accuracy. she ate. 🔥

BOLT-REML, the other moment

The BOLT-REML algorithm estimates heritability explained by genotyped SNPs, plus genetic correlations among multiple traits measured on the same set of individuals. she applies variance components analysis to do all that, supporting:

• multi-component modeling to partition SNP-heritability
• 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. periodt. 💜

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👑 OPTIONS 👑

one bullet per flag, bestie, and I am NOT skipping a single one. this is the whole menu.

• -h, --help — print help message with typical options 🙈
• --helpFull — print help message with the FULL option list. she's thorough. 💅
• --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

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🌈 SEE ALSO 🌈

• https://data.broadinstitute.org/alkesgroup/BOLT-LMM/ — go read it, iykyk 🦋

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💜 COPYRIGHT 💜

Copyright © 2014-2018 Harvard University. Distributed under the GNU GPLv3+ open source license. respect the license bestie, it's literally the law 🫶

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ok that's the whole page, no notes, she's iconic 💅✨

and that's a wrap on BOLT(1) — this was version 2.3.2, dated May 2018, mwah, go run some associations 🎀🧚💖