title: Efficient unified rare variant association test by modeling the population genetic distribution in case-control studies.
abstract: Recent advancements in next-generation DNA sequencing technologies have made it plausible to study the association of rare variants with complex diseases. Due to the low frequency, rare variants need to be aggregated in association tests to achieve adequate power with reasonable sample sizes. Hierarchical modeling/kernel machine methods have gained popularity among many available methods for testing a set of rare variants collectively. Here, we propose a new score statistic based on a hierarchical model by additionally modeling the distribution of rare variants under the case-control study design. Results from extensive simulation studies show that the proposed method strikes a balance between robustness and power and outperforms several popular rare-variant association tests. We demonstrate the performance of our method using the Dallas Heart Study.
authors.affiliation: Department of Population Health, New York University School of Medicine, New York, New York, United States of America. huilin.li@nyumc.org.
authors.forename: Huilin
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, United States of America.
authors.forename: Jinbo
authors.initials: J
authors.lastname: Chen
mesh_terms: D016022:Case-Control Studies; D014644:Genetic Variation; D005828:Genetics, Population; D055106:Genome-Wide Association Study; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D017422:Sequence Analysis, DNA
keywords: case-control study; gene set; population genetic distribution; rare variant