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Abstract:
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National and international genetic compendiums such as the UK Biobank have been an invaluable resource for identifying genetic variants that are associated with complex diseases. A common practice of these biobanks is to collect interval-censored data. However, there is a current lack of methodology to perform rare variant association testing with these types of outcomes. Some tests exist for the association of variant sets and a single outcome, but these tests may lack power when signals are very weak. Combining multiple outcomes in genetic association tests can increase statistical power while identifying key biomarkers that are associated with multiple traits. In this work, we develop a set-based inference method for jointly testing the association between multiple interval-censored outcomes and a group of genetic mutations, such as those in a gene or pathway. This variance components score test only requires fitting the null model once, so it is well-suited for genome-wide application. Our work shows that combining multiple interval-censored outcomes can detect causal variants with increased power over using a test that only considers single outcomes.
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