State Key Laboratory of Integrated Services Networks, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
Applied Science College, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China.
Sci Rep. 2021 Jan 28;11(1):2590. doi: 10.1038/s41598-021-82336-8.
Meta-analysis is a popular method used in genome-wide association studies, by which the results of multiple studies are combined to identify associations. This process generates heterogeneity. Recently, we proposed a random effect model meta-regression method (MR) to study the effect of single nucleotide polymorphism (SNP)-environment interactions. This method takes heterogeneity into account and produces high power. We also proposed a fixed effect model overlapping MR in which the overlapping data is taken into account. In the present study, a random effect model overlapping MR that simultaneously considers heterogeneity and overlapping data is proposed. This method is based on the random effect model MR and the fixed effect model overlapping MR. A new way of solving the logarithm of the determinant of covariance matrices in likelihood functions is also provided. Tests for the likelihood ratio statistic of the SNP-environment interaction effect and the SNP and SNP-environment joint effects are given. In our simulations, null distributions and type I error rates were proposed to verify the suitability of our method, and powers were applied to evaluate the superiority of our method. Our findings indicate that this method is effective in cases of overlapping data with a high heterogeneity.
元分析是一种广泛应用于全基因组关联研究的方法，通过该方法可以合并多个研究的结果以确定关联。这一过程会产生异质性。最近，我们提出了一种随机效应模型荟萃回归方法（MR）来研究单核苷酸多态性（SNP）-环境相互作用的影响。该方法考虑了异质性，具有较高的功效。我们还提出了一种固定效应模型重叠 MR，其中考虑了重叠数据。在本研究中，提出了一种同时考虑异质性和重叠数据的随机效应模型重叠 MR。该方法基于随机效应模型 MR 和固定效应模型重叠 MR。还提供了一种解决似然函数中协方差矩阵对数的新方法。提出了用于检验 SNP-环境相互作用效应以及 SNP 和 SNP-环境联合效应的似然比统计量的检验方法。在我们的模拟中，提出了零分布和Ⅰ型错误率来验证我们方法的适用性，并应用功效来评估我们方法的优越性。我们的研究结果表明，在存在高度异质性的重叠数据的情况下，该方法是有效的。
