title: False discovery rates for rare variants from sequenced data.
abstract: The detection of rare deleterious variants is the preeminent current technical challenge in statistical genetics. Sorting the deleterious from neutral variants at a disease locus is challenging because of the sparseness of the evidence for each individual variant. Hierarchical modeling and Bayesian model uncertainty are two techniques that have been shown to be promising in pinpointing individual rare variants that may be driving the association. Interpreting the results from these techniques from the perspective of multiple testing is a challenge and the goal of this article is to better understand their false discovery properties. Using simulations, we conclude that accurate false discovery control cannot be achieved in this framework unless the magnitude of the variants' risk is large and the hierarchical characteristics have high accuracy in distinguishing deleterious from neutral variants.
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States of America.
authors.forename: Marinela
authors.initials: M
authors.lastname: Capanu
authors.forename: Venkatraman E
authors.initials: VE
authors.lastname: Seshan
mesh_terms: D001483:Base Sequence; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology; D004252:DNA Mutational Analysis; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D018570:Risk Assessment; D035501:Uncertainty
keywords: false discovery rate; hierarchical model; local false discovery rate; rare variants