A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis. Academic Article uri icon

Overview

MeSH

  • Bayes Theorem
  • Computational Biology
  • Databases, Genetic
  • Genetic Loci

MeSH Major

  • Algorithms
  • Genome-Wide Association Study

abstract

  • 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. 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. 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.

publication date

  • 2010

has subject area

  • Algorithms
  • Bayes Theorem
  • Computational Biology
  • Databases, Genetic
  • Genetic Loci
  • Genome-Wide Association Study

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC2824680

Digital Object Identifier (DOI)

  • 10.1186/1471-2105-11-58

PubMed ID

  • 20105321

Additional Document Info

start page

  • 58

volume

  • 11