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Gene‐based sequential burden association test
Author(s) -
Chen Zhongxue,
Wang Kai
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8111
Subject(s) - computer science , association test , statistical power , genetic association , statistical hypothesis testing , association (psychology) , data mining , multiple comparisons problem , set (abstract data type) , machine learning , single nucleotide polymorphism , artificial intelligence , statistics , gene , genetics , mathematics , biology , genotype , epistemology , programming language , philosophy
Detecting the association between a set of variants and a phenotype of interest is the first and important step in genetic and genomic studies. Although it attracted a large amount of attention in the scientific community and several related statistical approaches have been proposed in the literature, powerful and robust statistical tests are still highly desired and yet to be developed in this area. In this paper, we propose a powerful and robust association test, which combines information from each individual single‐nucleotide polymorphisms based on sequential independent burden tests. We compare the proposed approach with some popular tests through a comprehensive simulation study and real data application. Our results show that, in general, the new test is more powerful; the gain in detecting power can be substantial in many situations, compared to other methods.