A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures

Abstract

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.

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20

“…Huisman et al used test (2) as a data preprocessing step to get beta coefficients statistics or p values (for SNP and CpG sites) to aggregate in their multimarker methods. For each gene, they created 4 scores by aggregating beta coefficients or p values for SNPs or DNA methylation probes (sum of absolute values, sum of squares, maximum of absolute values, and median of absolute values in the spirit of other papers exploring aggregation methods [ 35 – 37 ]). Xu et al used a multimarker approach to test the combined SNPs/CpG sites or candidate genes from the iterative regression and the extreme values strategy (a score test developed by Chapman et al [ 6 ]).…”

Gene‐based sequential burden association test

“…Recently, some robust association tests have been proposed in the literature, such as the joint‐infinity test and the adaptive sum of powered score test . Both of the tests try to combine information from different but related test statistics to make them more powerful under some situations where a single test might have low or no power.…”

Testing an Optimally Weighted Combination of Common and/or Rare Variants with Multiple Traits

“…information from multiple variants. These methods include burden tests (Morgenthaler and Thilly 2007;Li and Leal 2008;Madsen and Browning 2009;Price et al 2010;Zawistowski et al 2010), quadratic tests (Neale et al 2011;Wu et al 2011;Sha et al 2012;Yang et al 2017), and combined tests (Derkach et al 2013;Lee et al 2013;Sha and Zhang 2014;Greco et al 2015).…”