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A W‐test collapsing method for rare‐variant association testing in exome sequencing data
Author(s) -
Sun Rui,
Weng Haoyi,
Hu Inchi,
Guo Junfeng,
Wu William K. K.,
Zee Benny ChungYing,
Wang Maggie Haitian
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22000
Subject(s) - exome sequencing , exome , computational biology , dna sequencing , genetic association , association test , computer science , genetics , test statistic , biology , mutation , statistics , statistical hypothesis testing , single nucleotide polymorphism , mathematics , genotype , gene
Advancement in sequencing technology enables the study of association between complex disorder phenotypes and single‐nucleotide polymorphisms with rare mutations. However, the rare genetic variant has extremely small variance and impairs testing power of traditional statistical methods. We introduce a W‐test collapsing method to evaluate rare‐variant association by measuring the distributional differences between cases and controls through combined log of odds ratio within a genomic region. The method is model‐free and inherits chi‐squared distribution with degrees of freedom estimated from bootstrapped samples of the data, and allows for fast and accurate P ‐value calculation without the need of permutations. The proposed method is compared with the Weighted‐Sum Statistic and Sequence Kernel Association Test on simulation datasets, and showed good performances and significantly faster computing speed. In the application of real next‐generation sequencing dataset of hypertensive disorder, it identified genes of interesting biological functions associated to metabolism disorder and inflammation, including the MACROD1 , NLRP7 , AGK , PAK6 , and APBB1 . The proposed method offers an efficient and effective way for testing rare genetic variants in whole exome sequencing datasets.