Supporting Information is available in the online issue at wileyonlinelibrary.com.
A Unified Mixed-Effects Model for Rare-Variant Association in Sequencing Studies
Article first published online: 9 MAR 2013
© 2013 Wiley Periodicals, Inc.
Volume 37, Issue 4, pages 334–344, May 2013
How to Cite
Sun, J., Zheng, Y. and Hsu, L. (2013), A Unified Mixed-Effects Model for Rare-Variant Association in Sequencing Studies. Genet. Epidemiol., 37: 334–344. doi: 10.1002/gepi.21717
- Issue published online: 15 APR 2013
- Article first published online: 9 MAR 2013
- Manuscript Accepted: 5 FEB 2013
- Manuscript Revised: 17 DEC 2012
- Manuscript Received: 20 SEP 2012
- NIH. Grant Numbers: P01AG014358, UC2HL102924, R01AG014358, R01GM085047, R01CA059045
- set-based association;
- rare variants;
- variant characteristics;
- score statistics;
- Fisher's procedure
For rare-variant association analysis, due to extreme low frequencies of these variants, it is necessary to aggregate them by a prior set (e.g., genes and pathways) in order to achieve adequate power. In this paper, we consider hierarchical models to relate a set of rare variants to phenotype by modeling the effects of variants as a function of variant characteristics while allowing for variant-specific effect (heterogeneity). We derive a set of two score statistics, testing the group effect by variant characteristics and the heterogeneity effect. We make a novel modification to these score statistics so that they are independent under the null hypothesis and their asymptotic distributions can be derived. As a result, the computational burden is greatly reduced compared with permutation-based tests. Our approach provides a general testing framework for rare variants association, which includes many commonly used tests, such as the burden test [Li and Leal, 2008] and the sequence kernel association test [Wu et al., 2011], as special cases. Furthermore, in contrast to these tests, our proposed test has an added capacity to identify which components of variant characteristics and heterogeneity contribute to the association. Simulations under a wide range of scenarios show that the proposed test is valid, robust, and powerful. An application to the Dallas Heart Study illustrates that apart from identifying genes with significant associations, the new method also provides additional information regarding the source of the association. Such information may be useful for generating hypothesis in future studies.