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Abstract:
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High-throughput sequencing has often been used to screen samples from pedigrees or with population structure, producing genotype data with bi-directional correlations rendered from both familial relation and LD. With such data, it is critical to account for the complex genotypic correlations when assessing the contribution of variants by gene or pathway. Recognizing the limitations of existing association testing methods, we propose Optimal Burden Test (OBT), a retrospective, mixed-model test for genetic association of quantitative traits on genotype data with bi-directional correlations. This method makes full use of genotypic correlations from both directions and adopts data-driven weights to improve power. We derive OBT statistic and its explicit null distribution, and demonstrate through simulations that, while keeping type I error well controlled, OBT is generally more powerful than fixed-weight burden test and family-based SKAT. Our further investigation reveals the connection of OBT with kernel tests, as well as the adaptivity of optimal weights. The application of OBT is illustrated by an analysis of fasting glucose associated genes from the Framingham Heart study.
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