Meta-Analysis Of SNP-Environment Interaction With Heterogeneity And Overlapping Data In GWAS

Genome-wide association study is a genome-wide method to identify genetic variations associated with complex traits and diseases.Genetic variations here mainly refer to single nucleotide polymorphisms(SNPs),it accounts for more than 90% of known polymorphisms.Meta-analysis is widely used method in genome-wide association studies.They synthesize the analysis results of multiple studies to achieve a large effective sample size and improve the probability of discovering new associations.Fixed effect model methods and random effect model methods are two commonly used methods in meta-analysis.The fixed effects model methods assume that the effects among studies are same.Under the fixed effects model,there are joint tests of SNP and SNP-environment interaction effects method and meta-regression method for SNP-environment interaction.In practice,genetic heterogeneity occurs when the same genetic disease or phenotype or similar genetic diseases or phenotypes are produced by different genetic mechanisms,which requires heterogeneity to be considered in the meta-analysis.The variants identified in GWASs have been shown to have different effect sizes and even different directions of associations in populations with different demographic histories.Recently,many large trans-ancestry meta-analyses have been performed,which routinely include genetic heterogeneity.Therefore,genetic heterogeneity and corresponding random effect models need to be considered in the meta-analysis.The classical random effects approach treats genetic heterogeneity as a random effect and as a part of the variance of fixed effect.Recent work suggests performing hypothesis testing under the null hypothesis that neither fixed nor random effects exist for a variant.This method has been shown to perform better than classical random effects method.However,this method only focuses on SNP main effect model and there is no research on the SNP-environment interaction model at present.In practice,overlapping data between studies may occur when using meta-analysis.This may be caused inadvertently for saving research cost or intentionally by researchers.Spurious association may be achieved if overlapping data exist and are ignored in the meta-analysis Recent studies have proposed methods to handle the issue of overlapping data when testing the genetic main effect of SNP.However,there is still no meta-analysis method for testing SNP-environment interaction when overlapping data exist.Based on these fixed effect model methods and random effects model methods,works was done as follows:first,we proposed a meta-analysis of testing SNP-environment interaction in the presence of genetic heterogeneity.We introduced the random effects of the SNP and SNP-environment interaction under test into a meta-regression model to account for heterogeneity.A test for the SNP-environment interaction was formulated to test for fixed and random effects of the interaction simultaneously.Similarly,a test for total genetic effects was formulated to test for fixed effects of the SNP and the SNP-environment interaction together with their random effects.We performed simulations to study the null distribution and statistical power of the proposed tests.We show that the new methods have higher power than classical random effects and fixed effects meta-regression methods when heterogeneity effects are large.This is a preferred method because it is the simple and effective method applicable to different scenarios.In addition,when the effect of SNP-environment interaction is known to exist,it can be generalized to use more advanced data-driven methods such as different forms of interaction to estimate interactions after the fact.Then,we introduce a test method of joint effect of SNP and SNP-environment interaction method under the random effect model.We also introduce an interaction test method of SNP-environmental interaction.We evaluate the null distribution of these tests and the power through likelihood ratio functions.This method was verified by simulation to give a similar power with random effect model meta-regression without the need of group level statistical data.When there are no group level statistical data,this method is a preferred method.However,this method needs to assume the form of an interaction in advance.To test a new hypothesis,it needs to reformulate the model and re-estimate the effect.Next,inspired by the methods of testing the main effect of gene with overlapping data,we proposed an overlapping meta-regulation method to address the issue in testing the gene-environment interaction.We generalized the variance and covariance matrices of the regular meta-regression model by employing Lin's and Han's correlation structures to incorporate the correlations introduced by the overlapping data.Based on our proposed models,we further provided statistical significance tests of the SNP-environment interaction as well as joint effects of the SNP main effect and the interaction.Through simulations,we examined null distributions and statistical powers of our proposed methods at different levels of data overlap among studies.We demonstrated that our method issuible and simultaneously achieved statistical power comparable with the method that removed overlapping samples a priori before the meta-analysis,i.e.,the splitting method.On the other hand,ignoring overlapping data will lead the upward of the points of null distribution.Our proposed method for testing SNP-environment interaction handles the issue of overlapping data effectively and statistically efficiently.Finally,based on the random effect meta-regression method and overlapping meta-regression method,we propose a random effect overlapping meta-regression method that simultaneously considers heterogeneity and overlapping data.Tests for the likelihood ratio statistic of the SNP-environment interaction effect and SNP and SNP-environment joint effects are given.In our simulations,null distributions were proposed to verify the suitability of our method,and powers were proposed to evaluate the superiority of our method.Based on the simulation,we concluded that this method gave higher power than fixed effect model overlapping meta-regression method when overlapping data existed and heterogeneity was high.