Implementation of genetic association tests for continuous outcomes utilizing the rank-based inverse normal transformation (INT). For outcomes whose residual distribution is heavily skewed or enriched for outliers, INT-based tests provided valid inference and improved power. The primary contribution is a rank normal omnibus test (RNOmni), which synthesizes two complementary INT-based approaches. In simulations against non-normal phenotypes, the omnibus test controlled the type I error in the absence of genetic associations, and improved power in the presence of genetic associations. Under the same settings, standard linear regression variously failed to control the type I error in the absence of associations, and was underpowered in the presence of associations.
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