title: A novel test for testing the optimally weighted combination of rare and common variants based on data of parents and affected children.
abstract: With the development of sequencing technologies, the direct testing of rare variant associations has become possible. Many statistical methods for detecting associations between rare variants and complex diseases have recently been developed, most of which are population-based methods for unrelated individuals. A limitation of population-based methods is that spurious associations can occur when there is a population structure. For rare variants, this problem can be more serious, because the spectrum of rare variation can be very different in diverse populations, as well as the current nonexistence of methods to control for population stratification in population-based rare variant associations. A solution to the problem of population stratification is to use family-based association tests, which use family members to control for population stratification. In this article, we propose a novel test for Testing the Optimally Weighted combination of variants based on data of Parents and Affected Children (TOW-PAC). TOW-PAC is a family-based association test that tests the combined effect of rare and common variants in a genomic region, and is robust to the directions of the effects of causal variants. Simulation studies confirm that, for rare variant associations, family-based association tests are robust to population stratification although population-based association tests can be seriously confounded by population stratification. The results of power comparisons show that the power of TOW-PAC increases with an increase of the number of affected children in each family and TOW-PAC based on multiple affected children per family is more powerful than TOW based on unrelated individuals.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan, United States of America.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha
authors.forename: Shuanglin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D002648:Child; D003198:Computer Simulation; D005260:Female; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005828:Genetics, Population; D015894:Genome, Human; D006801:Humans; D008297:Male; D008957:Models, Genetic; D010290:Parents; D010375:Pedigree
keywords: association studies; family-based design; optimal weights; rare variants