A Powerful Variant-Set Association Test Based on Chi-Square Distribution. Issue 3 (13th September 2017)

• Record Type: Journal Article
• Authors: Chen, Zhongxue; Lin, Tong; Wang, Kai
• Is Part Of: Genetics. Volume 207:Issue 3(2017)
• Page Start: 903
• Page End: 910
• Publication Date: 2017-09-13
• Subjects: chi-square distribution -- gene-set analysis -- principal component analysis; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.117.300287
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: Detecting the association between a set of variants and a given phenotype has attracted a large amount of attention in the scientific community, although it is a difficult task. Recently, several related statistical approaches have been proposed in the literature; powerful statistical tests are still highly desired and yet to be developed in this area. In this paper, we propose a powerful test that combines information from each individual single nucleotide polymorphism (SNP) based on principal component analysis without relying on the eigenvalues associated with the principal components. We compare the proposed approach with some popular tests through a simulation study and real data applications. Our results show that, in general, the new test is more powerful than its competitors considered in this study; the gain in detecting power can be substantial in many situations.