title: A novel statistical method for rare-variant association studies in general pedigrees.
abstract: Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with ascertainment based on phenotype, may enrich the sample for causal rare variants and thus can be more powerful than population-based designs. Therefore, it is important to develop family-based statistical methods that can account for ascertainment. In this paper, we develop a novel statistical method for rare-variant association studies in general pedigrees for quantitative traits. This method uses a retrospective view that treats the traits as fixed and the genotypes as random, which allows us to account for complex and undefined ascertainment of families. We then apply the newly developed method to the Genetic Analysis Workshop 19 data set and compare the power of the new method with two other methods for general pedigrees. The results show that the newly proposed method increases power in most of the cases we consider, more than the other two methods.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 USA.
authors.forename: Huanhuan
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 USA.
authors.forename: Zhenchuan
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Mathematics, University of North Texas, 1155 Union Circle #311430, Denton, TX 76203-5017 USA.
authors.forename: Xuexia
authors.initials: X
authors.lastname: Wang
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 USA.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha