A two-stage testing strategy for detecting genes×environment interactions in association studies

Author:

Zhou Jiabin1,Li Shitao2,Zhou Ying1ORCID,Sheng Xiaona3

Affiliation:

1. Department of Statistics, School of Mathematical Sciences, Heilongjiang University, Harbin 150080, China

2. Department of Basic Course, Shenyang University of Technology, Liaoyang 111000, China

3. School of Information Engineering, Harbin University, Harbin 150086, China

Abstract

Abstract Identifying gene×environment (G×E) interactions, especially when rare variants are included in genome-wide association studies, is a major challenge in statistical genetics. However, the detection of G×E interactions is very important for understanding the etiology of complex diseases. Although currently some statistical methods have been developed to detect the interactions between genes and environment, the detection of the interactions for the case of rare variants is still limited. Therefore, it is particularly important to develop a new method to detect the interactions between genes and environment for rare variants. In this study, we extend an existing method of adaptive combination of P-values (ADA) and design a novel strategy (called iSADA) for testing the effects of G×E interactions for rare variants. We propose a new two-stage test to detect the interactions between genes and environment in a certain region of a chromosome or even for the whole genome. First, the score statistic is used to test the associations between trait value and the interaction terms of genes and environment and obtain the original P-values. Then, based on the idea of the ADA method, we further construct a full test statistic via the P-values of the preliminary tests in the first stage, so that we can comprehensively test the interactions between genes and environment in the considered genome region. Simulation studies are conducted to compare our proposed method with other existing methods. The results show that the iSADA has higher power than other methods in each case. A GAW17 data set is also applied to illustrate the applicability of the new method.

Funder

The National Natural Science Foundation of China

Natural Science Foundation of Heilongjiang Province of China

Publisher

Oxford University Press (OUP)

Subject

Genetics (clinical),Genetics,Molecular Biology

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