TY - JOUR
T1 - A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis
AU - Logsdon, Benjamin A.
AU - Hoffman, Gabriel E.
AU - Mezey, Jason G.
N1 - Funding Information:
We thank Cornell University for funding for JGM, the Cornell Center for Vertebrate Genomics and the Cornell Provost Fund for support of BAL. We would like to thank Kirk Lohmueller, Adam Siepel, Brian White, Keyan Zhao, and Nadia Singh for insightful comments on the manuscript. We would also like to thank two anonymous reviewers for suggestions which strengthened the overall quality of the manuscript. This research was conducted using the resources of the Cornell University Center for Advanced Computing.
PY - 2010/1/27
Y1 - 2010/1/27
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/77349109776
U2 - 10.1186/1471-2105-11-58
DO - 10.1186/1471-2105-11-58
M3 - Article
C2 - 20105321
AN - SCOPUS:77349109776
SN - 1471-2105
VL - 11
JO - BMC Bioinformatics
JF - BMC Bioinformatics
M1 - 58
ER -