Optimal tests for rare variant effects in sequencing association studies

Abstract

With development of massively parallel sequencing technologies, there is a substantial need for developing powerful rare variant association tests. Common approaches include burden and non-burden tests. Burden tests assume all rare variants in the target region have effects on the phenotype in the same direction and of similar magnitude. The recently proposed sequence kernel association test (SKAT) (Wu, M. C., and others, 2011. Rare-variant association testing for sequencing data with the SKAT. The American Journal of Human Genetics 89, 82-93], an extension of the C-alpha test (Neale, B. M., and others, 2011. Testing for an unusual distribution of rare variants. PLoS Genetics 7, 161-165], provides a robust test that is particularly powerful in the presence of protective and deleterious variants and null variants, but is less powerful than burden tests when a large number of variants in a region are causal and in the same direction. As the underlying biological mechanisms are unknown in practice and vary from one gene to another across the genome, it is of substantial practical interest to develop a test that is optimal for both scenarios. In this paper, we propose a class of tests that include burden tests and SKAT as special cases, and derive an optimal test within this class that maximizes power. We show that this optimal test outperforms burden tests and SKAT in a wide range of scenarios. The results are illustrated using simulation studies and triglyceride data from the Dallas Heart Study. In addition, we have derived sample size/power calculation formula for SKAT with a new family of kernels to facilitate designing new sequence association studies.

Phenotypic extremes in rare variant study designs

“…8,9 The power to detect association with the various proposed gene-based methods is dependent on the underlying genetic architecture of the gene. [10][11][12] The relative power of different study designs for CVASs has been well established. 13 For all genetic studies, selecting the extremes of the phenotype distribution improves power; a concept in genetics that can be traced back to seminal work by Lander and Botstein.…”

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

“…Prior work [5][6][7] has shown that combined tests can be considered 'optimal;' however, these approaches have been limited to combining L(1) and J(2) tests. In this paper we have shown that combining other disparate tests can be advantageous (e.g., combining SKAT-O, itself a combination of L(1) and J(2), with J(N)).…”

“…1,2 Recent papers have proposed combining test statistics across both the length and joint classes to yield more powerful test statistics. 1,[5][6][7][8] Results from these papers demonstrate how to combine a single version of a length test with a single version of a joint test, 5 how to use a weighting strategy to find the optimal weighted combination of two particular length and joint test statistics, 6 and that different weighted combinations of particular length and joint tests can be more powerful than single tests for different genetic architectures. 1 Overall, these combined testing approaches show improved power against a wider range of genetic architectures when compared to using either statistic separately.…”

“…1 Overall, these combined testing approaches show improved power against a wider range of genetic architectures when compared to using either statistic separately. 1,[5][6][7] In general, any approach that combines a single-length test and a single-joint test will have a limited range of situations in which it is powerful. In particular, the combined test can only be powerful in cases where either of the two individual tests being combined is powerful.…”

SEQSpark: A Complete Analysis Tool for Large-Scale Rare Variant Association Studies Using Whole-Genome and Exome Sequence Data

“…SEQSpark implements both single variant association tests and rare variant aggregate association methods, e.g., combined multivariate collapsing (CMC), 1 burden of rare variants (BRV), 2,12 variable threshold (VT), 4 sequence kernel association test (SKAT), 5 and SKAToptimal (SKAT-O). 13 All methods are implemented in a regression framework so that important covariates can be included in the analysis and gene 3 gene and gene 3 environment interactions can be investigated. Conditional regression can also be performed to tease apart, for example, associations with susceptibility variants from those due to linkage disequilibrium.…”