Polygenic Modeling with Bayesian Sparse Linear Mixed Models

By Xiaoping Zhou, Peter Carbonetto, Matthew Stephens. PLoS Genetics, 2012

810 citations recorded by the source corpus.

Both linear mixed models (LMMs) and sparse regression models are widely used in genetics applications, including, recently, polygenic modeling in genome-wide association studies. These two approaches make very different assumptions, so are expected to perform well in different situations. However, in practice, for a given dataset one typically does not know which assumptions will be more accurate. Motivated by this, we consider a hybrid of the two, which we refer to as a “Bayesian sparse linear mixed model” (BSLMM) that includes both these models as special cases. We address several key computational and statistical issues that arise when applying BSLMM, including appropriate prior specification for the hyper-parameters and a novel Markov chain Monte Carlo algorithm for posterior inference. We apply BSLMM and compare it with other methods for two polygenic modeling applications: estimating the proportion of variance in phenotypes explained (PVE) by available genotypes, and phenotype (or breeding value) prediction. For PVE estimation, we demonstrate that BSLMM combines the advantages of both standard LMMs and sparse regression modeling. For phenotype prediction it considerably outperforms either of the other two methods, as well as several other large-scale regression methods previously suggested for this problem. Software implementing our method is freely available from http://stephenslab.uchicago.edu/software.html.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/1e9876f0b167e465ab0b5dfb271e97eadac1f028

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Citations in the corpus

Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, neutral): “43,44 .”
Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology (2016, positive): “Zhou et al. 5 developed a Bayesian sparse linear mixed model and Moser et al. 17 proposed a Bayesian mixture model.”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “…et al., 2001; Verbyla et al., 2009, 2010; Yi et al., 2003], BayesR [Erbe et al., 2012; Moser et al., 2015], BVSR [Guan and Stephens, 2011], BSLMM [Zhou et al., 2013], and DPR [Zeng and Zhou, 2017], have used discrete mixture priors on SNP effect sizes, we expect that PRS-CS can provide…”
The genomics of local adaptation in trees: are we out of the woods yet? (2017, methodology): “While the approach remains promising considering its performance in the context of genomic prediction and inference of PVE (e.g., Zhou et al. 2013, Speed & Balding 2014), there has been no attempts, to our knowledge, to assess the approach under various demographic histories, genetic architectures,…”
Genetic mapping of quantitative trait loci in crops (2017, methodology): “Also, the Bayesian sparse linear mixed model (BSLMM) sets an appropriate prior for hyper-parameters and uses the Markov chain Monte Carlo algorithm for posterior inference, yielding reliable results for large datasets [65].”
The critical needs and challenges for genetic architecture studies in Africa (2018, neutral): “further increase power in structured populations [74-77].”
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores (2015, result): “, a mixture of normal, were used by other studies.(23,26,28) One recent study proposed an elegant approach for handling case-control ascertainment while including genome-wide-significant associations as fixed effects;(56) however, the correlations between distal causal SNPs induced by case-control a”
Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, methodology): “As written in equation (14), this mixture has three parameters: σ β ,12 and σ β ,22 , the variances of the two Gaussians, and p , the probability of drawing from the first Gaussian.”