Dataset Information

Efficient genome-wide association testing of gene-environment interaction in case-parent trios.

ABSTRACT: Complex trait variation is likely to be explained by the combined effects of genes, environmental factors, and gene x environment (G x E) interaction. The authors introduce a novel 2-step method for detecting a G x E interaction in a genome-wide association study (GWAS) of case-parent trios. The method utilizes 2 sources of G x E information in a trio sample to construct a screening step and a testing step. Across a wide range of models, this 2-step procedure provides substantially greater power to detect G x E interaction than a standard test of G x E interaction applied genome-wide. For example, for a disease susceptibility locus with minor allele frequency of 15%, a binary exposure variable with 50% prevalence, and a GWAS scan of 1 million markers in 1,000 case-parent trios, the 2-step method provides 87% power to detect a G x E interaction relative risk of 2.3, as compared with only 25% power using a standard G x E test. The method is easily implemented using standard software. This 2-step scan for G x E interaction is independent of any prior scan that may have been conducted for genetic main effects, and thus has the potential to uncover new genes in a GWAS that have not been previously identified.

SUBMITTER: Gauderman WJ

PROVIDER: S-EPMC2915477 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Efficient genome-wide association testing of gene-environment interaction in case-parent trios.

Gauderman W James WJ Thomas Duncan C DC Murcray Cassandra E CE Conti David D Li Dalin D Lewinger Juan Pablo JP

American journal of epidemiology 20100611 1

Complex trait variation is likely to be explained by the combined effects of genes, environmental factors, and gene x environment (G x E) interaction. The authors introduce a novel 2-step method for detecting a G x E interaction in a genome-wide association study (GWAS) of case-parent trios. The method utilizes 2 sources of G x E information in a trio sample to construct a screening step and a testing step. Across a wide range of models, this 2-step procedure provides substantially greater power ...[more]

PMID: 20543031

Similar Datasets

Project description:Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term. Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs). Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios.

Project description:Two large studies of case-parent trios ascertained through a proband with a non-syndromic orofacial cleft (OFC, which includes cleft lip and palate, cleft lip alone, or cleft palate alone) were used to test for possible gene-environment (G × E) interaction between genome-wide markers (both observed and imputed) and self-reported maternal exposure to smoking, alcohol consumption, and multivitamin supplementation during pregnancy. The parent studies were as follows: GENEVA, which included 1,939 case-parent trios recruited largely through treatment centers in Europe, the United States, and Asia, and 1,443 case-parent trios from the Pittsburgh Orofacial Cleft Study (POFC) also ascertained through a proband with an OFC including three major racial/ethnic groups (European, Asian, and Latin American). Exposure rates to these environmental risk factors (maternal smoking, alcohol consumption, and multivitamin supplementation) varied across studies and among racial/ethnic groups, creating substantial differences in power to detect G × E interaction, but the trio design should minimize spurious results due to population stratification. The GENEVA and POFC studies were analyzed separately, and a meta-analysis was conducted across both studies to test for G × E interaction using the 2 df test of gene and G × E interaction and the 1 df test for G × E interaction alone. The 2 df test confirmed effects for several recognized risk genes, suggesting modest G × E effects. This analysis did reveal suggestive evidence for G × Vitamin interaction for CASP9 on 1p36 located about 3 Mb from PAX7, a recognized risk gene. Several regions gave suggestive evidence of G × E interaction in the 1 df test. For example, for G × Smoking interaction, the 1 df test suggested markers in MUSK on 9q31.3 from meta-analysis. Markers near SLCO3A1 also showed suggestive evidence in the 1 df test for G × Alcohol interaction, and rs41117 near RETREG1 (a.k.a. FAM134B) also gave suggestive significance in the meta-analysis of the 1 df test for G × Vitamin interaction. While it remains quite difficult to obtain definitive evidence for G × E interaction in genome-wide studies, perhaps due to small effect sizes of individual genes combined with low exposure rates, this analysis of two large case-parent trio studies argues for considering possible G × E interaction in any comprehensive study of complex and heterogeneous disorders such as OFC.

Dataset Information

Efficient genome-wide association testing of gene-environment interaction in case-parent trios.

Publications

Efficient genome-wide association testing of gene-environment interaction in case-parent trios.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets