A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis

By Benjamin A. Logsdon, Gabriel E. Hoffman, Jason G. Mezey. 2010

129 citations recorded by the source corpus.

BackgroundThe success achieved by genome-wide association (GWA) studies in the identification of candidate loci for complex diseases has been accompanied by an inability to explain the bulk of heritability. Here, we describe the algorithm V-Bay, a variational Bayes algorithm for multiple locus GWA analysis, which is designed to identify weaker associations that may contribute to this missing heritability.ResultsV-Bay provides a novel solution to the computational scaling constraints of most multiple locus methods and can complete a simultaneous analysis of a million genetic markers in a few hours, when using a desktop. Using a range of simulated genetic and GWA experimental scenarios, we demonstrate that V-Bay is highly accurate, and reliably identifies associations that are too weak to be discovered by single-marker testing approaches. V-Bay can also outperform a multiple locus analysis method based on the lasso, which has similar scaling properties for large numbers of genetic markers. For demonstration purposes, we also use V-Bay to confirm associations with gene expression in cell lines derived from the Phase II individuals of HapMap.ConclusionsV-Bay is a versatile, fast, and accurate multiple locus GWA analysis tool for the practitioner interested in identifying weaker associations without high false positive rates.

Subfields: Genetics

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Citations in the corpus

Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior (2016, background): “Implementation instead can proceed instead by applying approximation methods, such as stochastic search variable selection (George and McCulloch 1993), shotgun stochastic search (Hans, Dobra, and West 2007), variational Bayes (Logsdon, Hoffman, and Mezey 2010; Carbonetto and Stephens 2012; Ray and S”
Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, methodology): “Our algorithm fits a Gaussian mixture model of SNP effects 19 , using a fast variational approximation 20–22 to compute approximate phenotypic residuals, and tests the residuals for association with candidate markers via a retrospective score statistic 23 that provides a bridge between Bayesian…”
A simple new approach to variable selection in regression, with application to genetic fine mapping (2018, unknown): “Although variational approaches to BVSR already exist (Logsdon et al., 2010; Carbonetto and Stephens, 2012), our new approach introduces a different family of approximating distributions that provides much more accurate inferences in settings with highly correlated variables.”
Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, methodology): “An alternative assumption, which has also been widely used in polygenic modeling applications [18,19,34], and more generally in statistics for sparse high-dimensional regression with large numbers of covariates [50,51], is that the effects come from a mixture of a normal distribution and a point mas”
Genomic Selection in Plant Breeding: A Comparison of Models (2012, unknown): “Similar fi ndings were reported in the use of VBay, which is an EM algorithm equivalent to BayesCπ developed for genome-wide association studies approaches in humans (Logsdon et al., 2010).”
Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies (2012, methodology): “Our method builds on a variational approximation recently developed in the same context (Logsdon et al. 2010).”
Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies (2014, unknown): “Logsdon, Hoffman and Mezey [21]; Carbonetto and Stephens [8]; Wand and Ormerod [32]).”
Computationally efficient whole-genome regression for quantitative and binary traits (2020, background): “5 and Supplementary Tables 8,9).”
fastSTRUCTURE: Variational Inference of Population Structure in Large SNP Data Sets (2014, methodology): “…originally used for approximating intractable integrals, have been used for a wide variety of applications in complex networks (Hofman and Wiggins 2008), machine learning (Jordan et al. 1998; Blei et al. 2003), and Bayesian variable selection (Logsdon et al. 2010; Carbonetto and Stephens 2012).”
ASYMPTOTIC NORMALITY AND VALID INFERENCE FOR GAUSSIAN VARIATIONAL APPROXIMATION (2011, methodology): “Examples of such applications are cluster analysis of gene-expression data [17], fitting spatial models to neuroimage data [6], image segmentation [4] and genome-wide association analysis [8].”