title: Test of rare variant association based on affected sib-pairs.
abstract: With the development of sequencing techniques, there is increasing interest to detect associations between rare variants and complex traits. Quite a few statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. Statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals. Recent studies show that rare disease variants will be enriched in family data and thus family-based designs may improve power to detect rare variant associations. In this article, we propose a novel test to test association between the optimally weighted combination of variants and trait of interests for affected sib-pairs. The optimal weights are analytically derived and can be calculated from sampled genotypes and phenotypes. Based on the optimal weights, the proposed method is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing methods are. Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on unrelated individuals and existing methods based on affected sib-pairs.
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
authors.forename: Qiuying
authors.initials: Q
authors.lastname: Sha
authors.affiliation: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
authors.forename: Shuanglin
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D056726:Genetic Association Studies / Q000379:methods*; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D035583:Rare Diseases / Q000235:genetics*