Mixed model association for biobank-scale data sets

By Po-Ru Loh, Gleb Kichaev, Steven Gazal, Armin P. Schoech, Alkes L Price. Nature Genetics, 2018

737 citations recorded by the source corpus.

Biobank-based genome-wide association studies are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing statistical power and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method— capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals). When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al. bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase. We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/f8489c1bfdeea2a210fdc27455266dcfc4156874

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Citations in the corpus

Leveraging polygenic functional enrichment to improve GWAS power (2017, methodology): “While we computed summary statistics at 20 million SNPs which passed filtering and QC thresholds (see ref.(27)), to ensure compatibility with simulations, we ran association analyses restricting to the same set of well-imputed ≈ 9.”
Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies (2018, positive): “We also used an independent GWAS that included all UK Biobank individuals irrespective of ancestry and relatedness (“UKB Loh”, N=459,327)[24].”
Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry (2018, background): “Following the recent study by Loh et al. (2018), which pointed out a number of caveats relative to the interpretation of the univariate LDSC intercept as an indicator of confounding due to population stratification or other artefacts, we have shown here that caution must also be applied when…”
Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis (2020, methodology): “This does not require the use of principal components in GWAS to account for population structure because this is taken into account using an LMM as performed by the software [35].”
A resource-efficient tool for mixed model association analysis of large-scale data (2019, methodology): “Last but not least, this study is focused on common variants, and fastGWA likely suffers from the same weakness as the other MLM-based methods in rare variant association test 47 especially for skewedly distributed phenotypes or binary traits with very low prevalence rate 42 .”
Computationally efficient whole-genome regression for quantitative and binary traits (2020, background): “To assess the computational resources needed to analyze a larger number of traits, we again ran REGENIE using Firth/SPA correction and SAIGE on a set of 50 binary traits from the UK Biobank with a range of different case–control ratios and distinct missing data patterns (Supplementary Table 6).”
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, methodology): “Quantitative traits were inverse rank transformed and associations were estimated using BOLT-LMM 50 .”
Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, positive): “Mixed models may yield greater statistical power, both through increased sample size and by controlling for the variance explained by the genetic relatedness between individuals (i.e., a random effects component; Loh et al., 2018).”
A generalized linear mixed model association tool for biobank-scale data (2021, methodology): “7 ) and BGENIE 1 , and linear mixed model (LMM)-based tools such as DISSECT 8 , BOLT-LMM 9 and fastGWA 10 .”