Two adaptive weighting methods to test for rare variant associations in family-based designs.
Although next-generation DNA sequencing technologies have made rare variant association studies feasible and affordable, the development of powerful statistical methods for rare variant association studies is still under way. Most of the existing methods for rare variant association studies compare the number of rare mutations in a group of rare variants (in a gene or a pathway) between cases and controls. However, these methods assume that all causal variants are risk to diseases. Recently, several methods that are robust to the direction and magnitude of effects of causal variants have been proposed. However, they are applicable to unrelated individuals only, whereas family data have been shown to improve power to detect rare variants. In this article, we propose two adaptive weighting methods for rare variant association studies based on family data for quantitative traits. Using extensive simulation studies, we evaluate and compare our proposed methods with two methods based on the weights proposed by Madsen and Browning. Our results show that both proposed methods are robust to population stratification, robust to the direction and magnitude of the effects of causal variants, and more powerful than the methods using weights suggested by Madsen and Browning, especially when both risk and protective variants are present.
Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan 49931, USA.
Shurong
S
Fang
Qiuying
Q
Sha
Shuanglin
S
Zhang
D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D056726:Genetic Association Studies; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D023281:Genomics; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D012306:Risk; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software