title: A weighted U-statistic for genetic association analyses of sequencing data.
abstract: With advancements in next-generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high-dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU-SEQ, for the high-dimensional association analysis of sequencing data. Based on a nonparametric U-statistic, WU-SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU-SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy-tailed distribution). Even when the assumptions were satisfied, WU-SEQ still attained comparable performance to SKAT. Finally, we applied WU-SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol.
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, United States of America; Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, Texas, United States of America.
authors.forename: Changshuai
authors.initials: C
authors.lastname: Wei
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.forename: Zihuai
authors.initials: Z
authors.lastname: He
authors.forename: Olga
authors.initials: O
authors.lastname: Vsevolozhskaya
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Schaid
authors.forename: Qing
authors.initials: Q
authors.lastname: Lu
mesh_terms: D000075803:Angiopoietin-Like Protein 4; D042682:Angiopoietins / Q000235:genetics; D056726:Genetic Association Studies; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008077:Lipoproteins, LDL / Q000235:genetics; D008957:Models, Genetic; D010641:Phenotype; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data; D012984:Software
chemical_list: C474772:ANGPTL4 protein, human; D000075803:Angiopoietin-Like Protein 4; D042682:Angiopoietins; D008077:Lipoproteins, LDL
keywords: next-generation sequencing; rare variants; weighted U-statistic