Article

Gene-Environment Interactions in Genome-Wide Association Studies: A Comparative Study of Tests Applied to Empirical Studies of Type 2 Diabetes

Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA.
American journal of epidemiology (Impact Factor: 4.98). 12/2011; 175(3):191-202. DOI: 10.1093/aje/kwr368
Source: PubMed

ABSTRACT The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses' Health Study, 1976-2006, and the Health Professionals Follow-up Study, 1986-2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.

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Available from: Peter Kraft, Aug 16, 2015
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    • "If there are correlation between null SNPs and E, the two plots on the bottom of Figure 3 shows that the power advantage of SBERIA and SBERIA-M is reduced, which is expected because the correlation between null SNPs and E would make the null SNPs more likely to be selected and therefore dilute the interaction signal. It is worth noting , however, that gene-environment correlation in population is relatively rare in real applications [Cornelis et al., 2012]. "
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    ABSTRACT: Identification of gene-environment interaction (G × E) is important in understanding the etiology of complex diseases. However, partially due to the lack of power, there have been very few replicated G × E findings compared to the success in marginal association studies. The existing G × E testing methods mainly focus on improving the power for individual markers. In this paper, we took a different strategy and proposed a set-based gene-environment interaction test (SBERIA), which can improve the power by reducing the multiple testing burdens and aggregating signals within a set. The major challenge of the signal aggregation within a set is how to tell signals from noise and how to determine the direction of the signals. SBERIA takes advantage of the established correlation screening for G × E to guide the aggregation of genotypes within a marker set. The correlation screening has been shown to be an efficient way of selecting potential G × E candidate SNPs in case-control studies for complex diseases. Importantly, the correlation screening in case-control combined samples is independent of the interaction test. With this desirable feature, SBERIA maintains the correct type I error level and can be easily implemented in a regular logistic regression setting. We showed that SBERIA had higher power than benchmark methods in various simulation scenarios, both for common and rare variants. We also applied SBERIA to real genome-wide association studies (GWAS) data of 10,729 colorectal cancer cases and 13,328 controls and found evidence of interaction between the set of known colorectal cancer susceptibility loci and smoking.
    Genetic Epidemiology 07/2013; 37(5). DOI:10.1002/gepi.21735 · 2.95 Impact Factor
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    • "Diet-gene interactions in diabetes (1-df) test, which is represented as β 5 of Model 3. Models 1 and 2 tested the marginal effects of dietary heme iron intake and SNPs respectively, adjusting for age and BMI. We also performed a two degree of freedom joint test (2-df) by comparing the fit of the null model containing the environment exposure only (Model 1) to the model with gene and gene–environment covariates (Model 3) as an alternate test of the gene–environment interaction (Cornelis et al., 2011; Manning et al., 2011). After running these models separately in NHS and HPFS, we performed tests for heterogeneity of the cohort specific results to check for appropriateness of pooling the data. "
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    ABSTRACT: Aims/hypothesis: Genome-wide association studies have identified over 50 new genetic loci for type 2 diabetes (T2D). Several studies conclude that higher dietary heme iron intake increases the risk of T2D. Therefore we assessed whether the relation between genetic loci and type 2 diabetes is modified by dietary heme iron intake. Methods: We used Affymetrix Genome-Wide Human 6.0 array data (681,770 single nucleotide polymorphisms (SNPs)) and dietary information collected in the Health Professionals Follow-up Study (n=725 cases; n=1,273 controls) and the Nurses’ Health Study (n=1,081 cases; n=1,692 controls). We assessed whether genome-wide SNPs or iron metabolism SNPs interacted with dietary heme iron intake in relation to T2D, testing for associations in each cohort separately and then meta-analyzing to pool the results. Finally, we created 1,000 synthetic pathways matched to an iron metabolism pathway on number of genes, and number of SNPs in each gene. We compared the iron metabolic pathway SNPs with these synthetic SNP assemblies in their relation to T2D to assess if the pathway as a whole interacts with dietary heme iron intake. Results: Using a genomic approach, we found no significant gene-environment interactions with dietary heme iron intake in relation to T2D (top SNP in pooled analysis: intergenic rs10980508; p=1.03E-06 > Bonferroni corrected p=7.33E-08). Furthermore, no SNP in the iron metabolic pathway significantly interacted with dietary heme iron intake (top SNP in pooled analysis: rs1805313; p=1.14E-03 > Bonferroni corrected p=2.10E-04). Finally, neither the main genetic effects (pooled empirical p by SNP=0.41), nor gene – dietary heme-iron interactions (pooled empirical p value for the interactions=0.72) were significant for the iron metabolic pathway as a whole. Conclusions: We found no significant interactions between dietary heme iron intake and common SNPs in relation to T2D.
    Frontiers in Genetics 01/2013; 4:7. DOI:10.3389/fgene.2013.00007
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    • "If there are correlation between null SNPs and E, the two plots on the bottom of Figure 3 shows that the power advantage of SBERIA and SBERIA-M is reduced, which is expected because the correlation between null SNPs and E would make the null SNPs more likely to be selected and therefore dilute the interaction signal. It is worth noting , however, that gene-environment correlation in population is relatively rare in real applications [Cornelis et al., 2012]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Identification of gene-environment interaction (GxE) is important in understanding the etiology of complex diseases. However, partially due to the lack of power, there have been very few replicated GxE findings compared to the success in marginal association studies. The existing GxE testing methods mainly focus on improving the power for individual markers. In this paper, we took a different strategy and proposed a Set Based gene EnviRonment InterAction test (SBERIA), which can improve the power by reducing the multiple testing burdens and aggregating signals within a set. The major challenge of the signal aggregation within a set is how to tell signals from noise and how to determine the direction of the signals. SBERIA takes advantage of the established correlation screening for GxE to guide the aggregation of genotypes within a marker set. The correlation screening has been shown to be an efficient wayof selecting potential GxE candidate SNPs in case-control studies for complex diseases. Importantly, the correlation screening in case-control combined samples is independent of the interaction test. With this desirable feature, SBERIA maintains the correct type I error level and can be easily implemented in a regular logistic regression setting. We showed that SBERIA had higher power than benchmark methods in various simulation scenarios, both for common and rare variants. We also applied SBERIA to real GWAS data of 10,729 colorectal cancer cases and 13,328 controls and found evidence of interaction between the set of known colorectal cancer susceptibility loci and smoking.
    Genetic Epidemiology 01/2013; · 2.95 Impact Factor
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