Jin Q.a,b· Shi G.a

Author affiliations

aState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China
bApplied Science College, Taiyuan University of Science and Technology, Taiyuan, China

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Article / Publication Details

First-Page Preview

Received: September 05, 2020
Accepted: July 29, 2021
Published online: October 26, 2021

Number of Print Pages: 9
Number of Figures: 4
Number of Tables: 0

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

Abstract

Many complex diseases are caused by single nucleotide polymorphisms (SNPs), environmental factors, and the interaction between SNPs and environment. Joint tests of the SNP and SNP-environment interaction effects (JMA) and meta-regression (MR) are commonly used to evaluate these SNP-environment interactions. However, these two methods do not consider genetic heterogeneity. We previously presented a random-effect MR, which provided higher power than the MR in datasets with high heterogeneity. However, this method requires group-level data, which sometimes are not available. Given this, we designed this study to evaluate the introduction of the random effects of SNP and SNP-environment interaction into the JMA, and then extended this to the random effect model. Likelihood ratio statistic is applied to test the JMA and the new method we proposed in this paper. We evaluated the null distributions of these tests, and the powers for this method. This method was verified by simulation and was shown to provide similar powers to the random effect meta-regression method (RMR). However, this method only requires study-level data which relaxed the condition of the RMR. Our study suggests that this method is more suitable for finding the association between SNP and diseases in the absence of group-level data.

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First-Page Preview

Received: September 05, 2020
Accepted: July 29, 2021
Published online: October 26, 2021

Number of Print Pages: 9
Number of Figures: 4
Number of Tables: 0

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: https://www.karger.com/HHE

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