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 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.
Logsdon
Benjamin A
BA
Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.
Hoffman
Gabriel E
GE
Mezey
Jason G
JG
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D030541:Databases, Genetic; D056426:Genetic Loci; D055106:Genome-Wide Association Study