Adaptive-weight burden test for associations between quantitative traits and genotype data with complex correlations
Xiaowei Wu, Ting Guan, Dajiang J. Liu, Luis G. León Novelo, Dipankar Bandyopadhyay
The Annals of Applied Statistics · 2018 · Volume 12 · Issue 3
Abstract
High throughput sequencing has often been used to screen samples from pedigrees or with population structure, producing genotype data with complex correlations caused by both familial relation and linkage disequilibrium. With such data it is critical to account for these genotypic correlations when assessing the contribution of multiple variants by gene or pathway. Recognizing the limitations of existing association testing methods, we propose Adaptive-weight Burden Test (ABT), a retrospective, mixed model test for genetic association of quantitative traits on genotype data with complex correlations. This method makes full use of genotypic correlations across both samples and variants and adopts “data driven” weights to improve power. We derive the ABT statistic and its explicit distribution under the null hypothesis and demonstrate through simulation studies that it is generally more powerful than the fixed-weight burden test and family-based SKAT in various scenarios, controlling for the type I error rate. Further investigation reveals the connection of ABT with kernel tests, as well as the adaptability of its weights to the direction of genetic effects. The application of ABT is illustrated by a gene-based association analysis of fasting glucose using data from the NHLBI “Grand Opportunity” Exome Sequencing Project.
Keywords
adaptive weight, burden test, complex genotypic correlation, Genetic association test, kernel test
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