title: Identifying rare variant associations in population-based and family-based designs.
abstract: For almost all complex traits studied in humans, the identified genetic variants discovered to date have accounted for only a small portion of the estimated trait heritability. Consequently, several methods have been developed to identify rare single-nucleotide variants associated with complex traits for population-based designs. Because rare disease variants tend to be enriched in families containing multiple affected individuals, family-based designs can play an important role in the identification of rare causal variants. In this study, we utilize Genetic Analysis Workshop 18 simulated data to examine the performance of some existing rare variant identification methods for unrelated individuals, including our recent method (rPLS). The simulated data is used to investigate whether there is an advantage to using family data compared to case-control data. The results indicate that population-based methods suffer from power loss, especially when the sample size is small. The family-based method employed in this paper results in higher power but fails to control type I error. Our study also highlights the importance of the phenotype choice, which can affect the power of detecting causal genes substantially.
authors.affiliation: Department of Statistics, The Ohio State University, 1179 University Drive, Newark, OH 43055, USA.
authors.forename: Asuman S
authors.initials: AS
authors.lastname: Turkmen
authors.affiliation: Department of Statistics, The Ohio State University, 1958 Neil Avenue, Columbus, OH 43210, USA.
authors.forename: Shili
authors.initials: S
authors.lastname: Lin