a bayesian method for evaluating and discovering disease loci associations贝叶斯方法评估和发现疾病位点关联.pdf

A Bayesian Method for Evaluating and Discovering Disease Loci Associations Xia Jiang1*, M. Michael Barmada2, Gregory F. Cooper 1,3, Michael J. Becich 1 1 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 2 Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 3 Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America Abstract Background: A genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed Bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi- locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. Methodology/Findings: We introduce the Bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a Bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously bee