A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures.
The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture. Recently, tests have been proposed which combine two particular individual tests (one burden and one variance components) to minimize power loss while improving robustness to a wider range of genetic architectures. In our analysis we propose an expansion of these approaches, yielding a general method that works for combining any number of individual tests. We demonstrate that running multiple different tests on the same data set and using a Bonferroni correction for multiple testing is never better than combining tests using our general method. We also find that using a test statistic that is highly robust to the inclusion of non-causal variants (joint-infinity) together with a previously published combined test (sequence kernel adaptive test-optimal) provides improved robustness to a wide range of genetic architectures and should be considered for use in practice. Software for this approach is supplied. We support the increased use of combined tests in practice - as well as further exploration of novel combined testing approaches using the general framework provided here - to maximize robustness of rare variant testing strategies against a wide range of genetic architectures.
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Brian
B
Greco
Department of Biostatistics, Vanderbilt University, Nashville, TN, USA.
Allison
A
Hainline
Department of Statistics, Winona State University, Winona, MN, USA.|Department of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
Jaron
J
Arbet
Department of Mathematics, Statistics and Computer Science, St Olaf College, Northfield, MN, USA.|Department of Biostatistics, University of Washington, Seattle, WA, USA.
Kelsey
K
Grinde
Department of Biostatistics, UC Berkeley, Berkeley, CA, USA.
Alejandra
A
Benitez
Department of Mathematics, Statistics and Computer Science, Dordt College, Sioux Center, IA, USA.
Nathan
N
Tintle
D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards; D005838:Genotype; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D012680:Sensitivity and Specificity; D012984:Software