Incorporating Gene-Environment Interaction in Testing for Association with Rare Genetic Variants

被引:29
作者
Chen, Han [1 ,2 ]
Meigs, James B. [3 ,4 ]
Dupuis, Josee [1 ,5 ]
机构
[1] Boston Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02118 USA
[2] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA
[3] Harvard Univ, Massachusetts Gen Hosp, Sch Med, Div Gen Med, Boston, MA USA
[4] Harvard Univ, Sch Med, Dept Med, Boston, MA USA
[5] NHLBI, Framingham Heart Study, Framingham, MA USA
关键词
Rare variant analysis; Gene-environment interaction; Sequence kernel association test; Joint test; Generalized linear mixed model; GENOME-WIDE; QUADRATIC-FORMS; COMMON DISEASES; REGRESSION; METAANALYSIS;
D O I
10.1159/000363347
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Objectives: The incorporation of gene-environment interactions could improve the ability to detect genetic associations with complex traits. For common genetic variants, single-marker interaction tests and joint tests of genetic main effects and gene-environment interaction have been well-established and used to identify novel association loci for complex diseases and continuous traits. For rare genetic variants, however, single-marker tests are severely underpowered due to the low minor allele frequency, and only a few gene-environment interaction tests have been developed. We aimed at developing powerful and computationally efficient tests for gene-environment interaction with rare variants. Methods: In this paper, we propose interaction and joint tests for testing gene-environment interaction of rare genetic variants. Our approach is a generalization of existing gene-environment interaction tests for multiple genetic variants under certain conditions. Results: We show in our simulation studies that our interaction and joint tests have correct type I errors, and that the joint test is a powerful approach for testing genetic association, allowing for gene-environment interaction. We also illustrate our approach in a real data example from the Framingham Heart Study. Conclusion: Our approach can be applied to both binary and continuous traits, it is powerful and computationally efficient. (C) 2014 S. Karger AG, Basel
引用
收藏
页码:81 / 90
页数:10
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