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

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 ...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Benjamin A. Logsdon, Gabriel E. Hoffman, Jason G. Mezey
• Keywords: BMCBI 2010, GWA Analysis, Locus Gwa, Multiple Locus