Adaptive Set-Based Methods for Association Testing
Year: 2015
Type: article
Abstract: 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.
Source: Genetic Epidemiology
DOI: 10.1002/gepi.21950
Authors: Yu-Chen Su, William James Gauderman, Kiros Berhane, Juan Pablo Lewinger
Institution: University of Southern California
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: Association pour la Recherche sur les Tumeurs de la Prostate, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Environmental Health Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Transdisciplinary Studies of Genetic Variation in Colorectal Cancer, FLEXIBLE MULTI-LEVEL MODELS FOR LONGITUDINAL ANALYSIS OF CHILDHOOD OBESITY, Methods for Pathway Modeling with Application to Folate, Detecting GxE Interactions in Genome-wide Association Studies, Environmental Exposures, Host Factors and Human Disease, Statistical Methods for Integrative Genomics in Cancer, University of Southern California Norris Comprehensive Cancer Center Support Grant (CCSG)
OpenAlex ID: W2234802500