Efficient Bayesian mixed model analysis increases association power in large cohorts
By Po-Ru Loh, George Tucker, Brendan K. Bulik-Sullivan, Bjarni J. Vilhjálmsson, Hilary K Finucane, Rany M. Salem, Daniel Chasman, P. Ridker, Benjamin M. Neale, Bonnie Berger and 2 more. 2014
1,384 citations recorded by the source corpus.
Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts and may not optimize power. All existing methods require time cost O(MN2) (where N is the number of samples and M is the number of SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here we present a far more efficient mixed-model association method, BOLT-LMM, which requires only a small number of O(MN) time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to 9 quantitative traits in 23,294 samples from the Women's Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for genome-wide association studies in large cohorts.
Subfields: Genetics
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In Genetics
- Alkes L Price
- Zhiwu Zhang
- Jian Yang
- Peter M. Visscher
- Edward S. Buckler
- Gonçalo R. Abecasis
- E. Sanderson
- Bogdan Pasaniuc
Citations in the corpus
- Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies (2016, methodology): “In addition, we tested several methods (BOLT-LMM v2.”
- Genetic mapping of quantitative trait loci in crops (2017, methodology): “[59] proposed BOLT-LMM, an efficient Bayesian mixed model analysis, which dramatically reduces running time and increases power by using a mixture of two Gaussian distributions as a prior on marker effect sizes.”
- Geographic variation and bias in polygenic scores of complex diseases and traits in Finland (2018, methodology): “In addition, we ran a linear mixed model for the second data set with the same covariates as with the standard linear model using BOLT-LMM v.2.3 (Loh et al. 2015).”
- Leveraging polygenic functional enrichment to improve GWAS power (2017, methodology): “consisted of 145K individuals from the interim UK Biobank release.(30,41) This served as our ‘‘discovery’’ dataset and had mean sample size of z130K across 27 independent traits (see below).”
- The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, neutral): “BOLT-LMM (Loh et al., 2015), EPACTS https://github.”
- Allelic Landscape of Human Blood Cell Trait Variation and Links (2016, neutral): “…(O’Connell et al., 2016) https://jmarchini.org/software/ PBWT (Durbin, 2014) https://imputation.sanger.ac.uk/ BOLT-LMM (Loh et al., 2015) https://data.broadinstitute.org/alkesgroup/BOLT-LMM/ METAL (Willer et al., 2010) http://csg.sph.umich.edu//abecasis/Metal/ SMR…”