A Powerful Retrospective Multiple Variant Association Test for Quantitative Traits by Borrowing Strength from Complex Genotypic Correlations
High-throughput sequencing has often been used in pedigree-based studies to identify genetic risk factors associated with complex traits. The genotype data in such studies exhibit complex correlations attributed to both familial relation and linkage disequilibrium. Accounting for these genotypic correlations can improve power for assessing the contribution of multiple genomic loci. However, due to model restrictions, existing multiple variant association testing methods cannot make efficient use of the correlation information appropriately. Recognizing this limitation, we develop PC-ABT, a novel principal-component-based adaptive-weight burden test for gene-based association mapping of quantitative traits. This method uses a retrospective score test to incorporate genotypic correlations, and employs “data-driven” weights to obtain maximized test statistic. In addition, by adjusting the number of principal components that essentially reveals the effective number of tests in the target gene region, PC-ABT is able to reduce the degree of freedom of the null distribution to improve power. Simulation studies show that PC-ABT is generally more powerful than other multiple variant tests that allow related individuals. We illustrate the application of PC-ABT by a gene-based association analysis of systolic blood pressure using data from the NHLBI “Grand Opportunity” Exome Sequencing Project.
This research was funded by 4-VA, a collaborative partnership for advancing the Commonwealth of Virginia.
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