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Adaptive Set‐Based Methods for Association Testing
Author(s) -
Su YuChen,
Gauderman William James,
Berhane Kiros,
Lewinger Juan Pablo
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.21950
Subject(s) - single nucleotide polymorphism , set (abstract data type) , snp , genome wide association study , genetic association , a priori and a posteriori , rank (graph theory) , computer science , sample size determination , association test , computational biology , biology , statistics , genetics , mathematics , genotype , gene , philosophy , epistemology , combinatorics , programming language
With a typical sample size of a few thousand subjects, a single genome‐wide association study (GWAS) using traditional one single nucleotide polymorphism (SNP)‐at‐a‐time methods can only detect genetic variants conferring a sizable effect on disease risk. Set‐based methods, which analyze sets of SNPs jointly, can detect variants with smaller effects acting within a gene, a pathway, or other biologically relevant sets. Although self‐contained set‐based methods (those that test sets of variants without regard to variants not in the set) are generally more powerful than competitive set‐based approaches (those that rely on comparison of variants in the set of interest with variants not in the set), there is no consensus as to which self‐contained methods are best. In particular, several self‐contained set tests have been proposed to directly or indirectly “adapt” to the a priori unknown proportion and distribution of effects of the truly associated SNPs in the set, which is a major determinant of their power. A popular adaptive set‐based test is the adaptive rank truncated product (ARTP), which seeks the set of SNPs that yields the best‐combined evidence of association. We compared the standard ARTP, several ARTP variations we introduced, and other adaptive methods in a comprehensive simulation study to evaluate their performance. We used permutations to assess significance for all the methods and thus provide a level playing field for comparison. We found the standard ARTP test to have the highest power across our simulations followed closely by the global model of random effects (GMRE) and a least absolute shrinkage and selection operator (LASSO)‐based test.