title: Genetic Variant Selection: Learning Across Traits and Sites.
abstract: We consider resequencing studies of associated loci and the problem of prioritizing sequence variants for functional follow-up. Working within the multivariate linear regression framework helps us to account for the joint effects of multiple genes; and adopting a Bayesian approach leads to posterior probabilities that coherently incorporate all information about the variants' function. We describe two novel prior distributions that facilitate learning the role of each variable site by borrowing evidence across phenotypes and across mutations in the same gene. We illustrate their potential advantages with simulations and reanalyzing a data set of sequencing variants.
authors.affiliation: Department of Health Research and Policy, Stanford University, Stanford, California 94305 lstell@stanford.edu.
authors.forename: Laurel
authors.initials: L
authors.lastname: Stell
authors.affiliation: Department of Health Research and Policy, Stanford University, Stanford, California 94305 Department of Statistics, Stanford University, Stanford, California 94305.
authors.forename: Chiara
authors.initials: C
authors.lastname: Sabatti
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping; D003198:Computer Simulation; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results; D012641:Selection, Genetic
keywords: Bayesian variant selection; fine mapping; pleiotropy; rare variants