期刊
GENETIC EPIDEMIOLOGY
卷 39, 期 6, 页码 399-405出版社
WILEY-BLACKWELL
DOI: 10.1002/gepi.21913
关键词
GWAS; SKAT; score statistic; sequencing data
资金
- NIH [GM083345, CA134848]
- National Heart, Lung, and Blood Institute (NHLBI) [HHSN26820 1100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN2682 011000010C, HHSN2682011000011C, HHSN268201 1000012C]
- National Institutes of Health (NIH) [5RC2HL102419]
Recent sequencing efforts have focused on exploring the influence of rare variants on the complex diseases. Gene level based tests by aggregating information across rare variants within a gene have become attractive to enrich the rare variant association signal. Among them, the sequence kernel association test (SKAT) has proved to be a very powerful method for jointly testing multiple rare variants within a gene. In this article, we explore an alternative SKAT. We propose to use the univariate likelihood ratio statistics from the marginal model for individual variants as input into the kernel association test. We show how to compute its significance P-value efficiently based on the asymptotic chi-square mixture distribution. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to associations between rare exonic variants and type 2 diabetes (T2D) in the Atherosclerosis Risk in Communities (ARIC) study. We identified an exome-wide significant rare variant set in the gene ZZZ3 worthy of further investigations.
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