Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits

Abstract

Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical -value calculation without any computationally expensive resampling procedures. By collapsing information in both traits and genetic variants using kernels, the proposed DKAT is shown to have a correct type-I error rate and higher power than other existing methods in both simulation studies and application to a study of genetic regulation of pathway gene expressions.

Powerful rare variant association testing in a copula‐based joint analysis of multiple phenotypes

“…Many previously proposed approaches are based on multivariate generalized linear or linear mixed models such as MURAT (Sun et al, 2016), aSPU, aSPUset, aSPUset-Score (Kim, Zhang, (Kaakinen et al, 2017;Lippert, Casale, Rakitsch, & Stegle, 2014;Maity, Sullivan, & Tzeng, 2012;Schifano, Li, Christiani, & Lin, 2013;Wang, Wang, Sha, & Zhang, 2016), or use dimension reduction methods (Aschard et al, 2014;Yang & Wang, 2012), structural equation modeling methods (Momen et al, 2018;Song, Morris, & Stein, 2016;Verhulst, Maes, & Neale, 2017), methods combining results from univariate analyses (Liang, Wang, & Zhang, 2016;Liu & Lin, 2018;O'Brien, 1984;van der Sluis, Posthuma, & Dolan, 2013), or others (Aschard et al, 2017;Jiang et al, 2015), see the review by Yang and Wang (2012) for more details. There exist more recent kernel-based approaches including GAMuT (Broadaway, Cutler, & Duncan, 2016), MSKAT (B. Wu & Pankow, 2016), DKAT (Zhan et al, 2017), and Multi-SKAT (Dutta, Scott, Boehnke, & Lee, 2019) that allow a more flexible modeling of the multivariate dependence structure. There exist more recent kernel-based approaches including GAMuT (Broadaway, Cutler, & Duncan, 2016), MSKAT (B. Wu & Pankow, 2016), DKAT (Zhan et al, 2017), and Multi-SKAT (Dutta, Scott, Boehnke, & Lee, 2019) that allow a more flexible modeling of the multivariate dependence structure.…”

“…There exist more recent kernel-based approaches including GAMuT (Broadaway, Cutler, & Duncan, 2016), MSKAT (B. Wu & Pankow, 2016), DKAT (Zhan et al, 2017), and Multi-SKAT (Dutta, Scott, Boehnke, & Lee, 2019) that allow a more flexible modeling of the multivariate dependence structure. Wu & Pankow, 2016;Zhan et al, 2017), however, most comparisons have been limited to a few selected methods of similar type. There exist some empirical comparisons of multivariate tests for the analysis of common (Kim et al, 2016;Liang et al, 2016;Zhu, Zhang, & Sha, 2015) and rare genetic variants (Broadaway et al, 2016;Dutta et al, 2019;B.…”

Meta‐MultiSKAT: Multiple phenotype meta‐analysis for region‐based association test

“…For example, under the assumption that the genetic effects of a variant on each phenotype are independent, we can use = I K × K , which is numerically equivalent to Het‐MAAUSS (Selyeong Lee et al, ). If the covariance structure in phenotypes is assumed to follow genetic effect residual covariance, we can use as , which results in the test equivalent to GAMuT (Broadaway et al, ), MSKAT (B. Wu & Pankow, ), and DKAT (Zhan et al, ).…”

A review of kernel methods for genetic association studies

“…Permutation methods, which permute indices of subjects to randomly distribute their multivariate traits, can overcome this limitation, but can be computationally time consuming. An alternative is to use the kernel matrix product approach of Broadaway et al (), or similar approaches (Zhan, Zhao, et al, ), to calculate the association between these two kernel matrices, but compute P ‐values as follows. Instead of actually permuting subjects, one can compute the finite sample moments of the statistic under the null hypothesis of no association.…”

“…The finite sample moments are the moments of the distribution if all permutations where enumerated, yet the moments can be calculated without actually enumerating all permutations. P ‐values (Zhan, Zhao, et al, ) can then be efficiently computed by matching the first three moments with a Pearson Type III density (Zhan, Zhao, et al, ). This approach can be especially useful for high‐dimensional traits.…”