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Abstract:
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In genetic association studies, joint testing of related phenotypes can provide novel insights into the genetic architecture of complex diseases. Although several methods exist for multi-phenotype tests with common variants, only a few exist for rare variants. To address this, we present several strategies to combine multi-phenotypes into gene-based tests, specifically a PC-based approach, regression-model-based approach, and omnibus test approaches that combine the two. The PC-based approach modifies the SKAT-O test based on the principal components of the phenotypes and aggregates signals for multiple PCs. The regression-based approach models the effect-sizes of the variants through correlations in mixed models and conducts a variance component test. From extensive simulation studies, we show that these tests can improve power over standard single-phenotype tests, while maintaining type1 error. Their relative performance depends on the number of associated phenotypes and correlation patterns. The omnibus test generally has robust power regardless of the genetic model. We applied our methods to data on nine amino-acid phenotypes from METSIM studies to identify associated variants.
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