Mathematics, Statistics, and Computer Science
genetic analysis, genomic variation, case-control studies
In the wake of the widespread availability of genome sequencing data made possible by way of nextgeneration technologies, a flood of gene‐based rare variant tests have been proposed. Most methods claim superior power against particular genetic architectures. However, an important practical issue remains for the applied researcher—namely, which test should be used for a particular association study which may consider multiple genes and/or multiple phenotypes. Recently, tests have been proposed which combine individual tests to minimize power loss while improving the robustness to a wide range of genetic architectures. In our analysis, we propose an expansion of these approaches, by providing a general method that works for combining an arbitrarily large number of any gene‐based rare variant test—a flexibility typically not available in other combined testing methods. We provide a theoretical framework for evaluating our combined test to provide direct insights into the relationship between test‐test correlation, test power and the combined test power relative to individual testing approaches and other combined testing approaches. We demonstrate that our flexible combined testing method can provide improved power and robustness against a wide range of genetic architectures. We further demonstrate the performance of our combined test on simulated genotypes, as well as on a dataset of real genotypes with simulated phenotypes. We support the increased use of flexible combined tests in practice to maximize robustness of rare‐variant testing strategies against a wide‐range of genetic architectures.
Tintle, Nathan L.; Greco, Brian; Hainline, Allison; Liu, Keli; Arbet, Jaron; Benitez, Alejandra; and Grinde, Kelsey, "General Approaches for Combining Multiple Rare Variant Associate Tests Provide Improved Power Across a Wider Range of Genetic Architecture" (2014). Faculty Work Comprehensive List. 63.