Article
Optimal methods for metaanalysis of genomewide association studies.
Department of Health Research and Policy, Stanford University, Stanford, California, USA.
Genetic Epidemiology (Impact Factor: 2.95). 09/2011; 35(7):58191. DOI: 10.1002/gepi.20603 Source: PubMed

Conference Paper: Genomewide metaregression of geneenvironment interaction
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ABSTRACT: Understanding the effects of geneenvironment interaction on complex human diseases or traits in genomewide association studies (GWAS) can help uncover novel genes and identify environmental hazards that influence only certain genetically susceptible groups. Thus there is a pressing need to develop efficient and powerful interaction analysis methods. In this paper, we propose a novel metaanalysis method of geneenvironment interaction, based on metaregression (MRM&I). Compared with existing metaanalysis methods, MRM&I allows for heterogeneity in the environmental factor (E) by dividing the subjects in each study into groups according to the distribution of E. Moreover, it can readily estimate linear or nonlinear interactions, and thus it is more generally applicable to different scenarios. We use numerical examples to demonstrate the performance of MRM&I and compare it with two commonly used methods in current GWAS. The results show that MRM&I is more powerful than the other methods.Genomic Signal Processing and Statistics, (GENSIPS), 2012 IEEE International Workshop on; 01/2012  [Show abstract] [Hide abstract]
ABSTRACT: Genomewide association studies (GWAS) have created heightened interest in understanding the effects of geneenvironment interaction on complex human diseases or traits. Applying methods for analyzing such interaction can help uncover novel genes and identify environmental hazards that influence only certain genetically susceptible groups. However, the number of interaction analysis methods is still limited, so there is a need to develop more efficient and powerful methods. In this paper, we propose two novel metaanalysis methods of studying geneenvironment interaction, based on metaregression of estimated genetic effects on the environmental factor. The two methods can perform joint analysis of a single nucleotide polymorphism's (SNP) main and interaction effects, or analyze only the effect of the interaction. They can readily estimate any linear or nonlinear interactions by simply modifying the geneenvironment regression function. Thus, they are efficient methods to be applied to different scenarios. We use numerical examples to demonstrate the performance of our methods. We also compare them with two other methods commonly used in current GWAS, i.e., metaanalysis of SNP main effects (MAIN) and joint metaanalysis of SNP main and interaction effects (JMA). The results show that our methods are more powerful than MAIN when the interaction effect exists, and are comparable to JMA in the linear or quadratic interaction cases. In the numerical examples, we also investigate how the number of the divided groups and the sample size of the studies affect the performance of our methods.IEEE Transactions on NanoBioscience 12/2013; 12(4):354362. · 1.77 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: For analysis of the main effects of SNPs, metaanalysis of summary results from individual studies has been shown to provide comparable results as “megaanalysis” that jointly analyzes the pooled participant data from the available studies. This fact revolutionized the genetic analysis of complex traits through large GWAS consortia. Investigations of geneenvironment (G×E) interactions are on the rise since they can potentially explain a part of the missing heritability and identify individuals at high risk for disease. However, for analysis of geneenvironment interactions, it is not known whether these methods yield comparable results. In this empirical study, we report that the results from both methods were largely consistent for all four tests; the standard 1 degree of freedom (df) test of main effect only, the 1 df test of the main effect (in the presence of interaction effect), the 1 df test of the interaction effect, and the joint 2 df test of main and interaction effects. They provided similar effect size and standard error estimates, leading to comparable Pvalues. The genomic inflation factors and the number of SNPs with various thresholds were also comparable between the two approaches. Megaanalysis is not always feasible especially in very large and diverse consortia since pooling of raw data may be limited by the terms of the informed consent. Our study illustrates that metaanalysis can be an effective approach also for identifying interactions. To our knowledge, this is the first report investigating metaversus megaanalyses for interactions.Genetic Epidemiology 04/2014; 38(4). · 2.95 Impact Factor
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