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• W2103551323 abstract "Abstract Background The 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. Results V-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. Conclusions V-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." @default.
• W2103551323 created "2016-06-24" @default.
• W2103551323 creator A5004096202 @default.
• W2103551323 creator A5042205466 @default.
• W2103551323 creator A5046862823 @default.
• W2103551323 date "2010-01-27" @default.
• W2103551323 modified "2023-10-16" @default.
• W2103551323 title "A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis" @default.
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• W2103551323 doi "https://doi.org/10.1186/1471-2105-11-58" @default.
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