title: A bayesian method for evaluating and discovering disease loci associations.
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.
  
  
  RESULTS
  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 been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found.
  
  
  CONCLUSIONS
  We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America. xij6@pitt.edu
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
authors.forename: M Michael
authors.initials: MM
authors.lastname: Barmada
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Becich
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics; D003198:Computer Simulation; D030541:Databases, Genetic; D003657:Decision Making; D005260:Female; D005786:Gene Expression Regulation; D056726:Genetic Association Studies / Q000379:methods*; D056426:Genetic Loci / Q000235:genetics*; D020022:Genetic Predisposition to Disease; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012372:ROC Curve