Cassandra E. Murcray

Gene-Environment Interaction in Genome-Wide Association Studies

It is a commonly held belief that most complex diseases (e.g., diabetes, asthma, cancer) are affected in part by interactions between genes and environmental factors. However, investigators conducting genome-wide association studies typically test for only the marginal effects of each genetic marker on disease. In this paper, the authors propose an efficient and easily implemented 2-step analysis of genome-wide association study data aimed at identifying genes involved in a gene-environment interaction. The procedure complements screening for marginal genetic effects and thus has the potential to uncover new genetic signals that have not been identified previously.

Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies

Variants identified in recent genome‐wide association studies based on the common‐disease common‐variant hypothesis are far from fully explaining the hereditability of complex traits. Rare variants may, in part, explain some of the missing hereditability. Here, we explored the advantage of the extreme phenotype sampling in rare‐variant analysis and refined this design framework for future large‐scale association studies on quantitative traits. We first proposed a power calculation approach for a likelihood‐based analysis method. We then used this approach to demonstrate the potential advantages of extreme phenotype sampling for rare variants. Next, we discussed how this design can influence future sequencing‐based association studies from a cost‐efficiency (with the phenotyping cost included) perspective. Moreover, we discussed the potential of a two‐stage design with the extreme sample as the first stage and the remaining nonextreme subjects as the second stage. We demonstrated that this two‐stage design is a cost‐efficient alternative to the one‐stage cross‐sectional design or traditional two‐stage design. We then discussed the analysis strategies for this extreme two‐stage design and proposed a corresponding design optimization procedure. To address many practical concerns, for example measurement error or phenotypic heterogeneity at the very extremes, we examined an approach in which individuals with very extreme phenotypes are discarded. We demonstrated that even with a substantial proportion of these extreme individuals discarded, an extreme‐based sampling can still be more efficient. Finally, we expanded the current analysis and design framework to accommodate the CMC approach where multiple rare‐variants in the same gene region are analyzed jointly. Genet. Epidemiol. 2011.

Sample size requirements to detect gene‐environment interactions in genome‐wide association studies

Many complex diseases are likely to be a result of the interplay of genes and environmental exposures. The standard analysis in a genome‐wide association study (GWAS) scans for main effects and ignores the potentially useful information in the available exposure data. Two recently proposed methods that exploit environmental exposure information involve a two‐step analysis aimed at prioritizing the large number of SNPs tested to highlight those most likely to be involved in a G × E interaction. For example, Murcray et al. ([ 2009 ] Am J Epidemiol 169:219–226) proposed screening on a test that models the G‐E association induced by an interaction in the combined case‐control sample. Alternatively, Kooperberg and LeBlanc ([ 2008 ] Genet Epidemiol 32:255–263) suggested screening on genetic marginal effects. In both methods, SNPs that pass the respective screening step at a pre‐specified significance threshold are followed up with a formal test of interaction in the second step. We propose a hybrid method that combines these two screening approaches by allocating a proportion of the overall genome‐wide significance level to each test. We show that the Murcray et al. approach is often the most efficient method, but that the hybrid approach is a powerful and robust method for nearly any underlying model. As an example, for a GWAS of 1 million markers including a single true disease SNP with minor allele frequency of 0.15, and a binary exposure with prevalence 0.3, the Murcray, Kooperberg and hybrid methods are 1.90, 1.27, and 1.87 times as efficient, respectively, as the traditional case‐control analysis to detect an interaction effect size of 2.0. Genet. Epidemiol. 35:201‐210, 2011.  © 2011 Wiley‐Liss, Inc.

Efficient Genome-Wide Association Testing of Gene-Environment Interaction in Case-Parent Trios

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.

Detecting Gene-Environment Interactions in Genome-Wide Association Data

Despite the importance of gene‐environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome‐wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of G×E interactions in both case‐control and family‐based data using both cross‐sectional and longitudinal study designs. Many of these contributions detected significant G×E interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family‐based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing G×E interactions are discussed. Genet. Epidemiol. 33 (Suppl. 1):S68–S73, 2009.

Efficient Two-Step Testing of Gene-Gene Interactions in Genome-Wide Association Studies

Exhaustive testing of all possible SNP pairs in a genome‐wide association study (GWAS) generally yields low power to detect gene‐gene (G × G) interactions because of small effect sizes and stringent requirements for multiple‐testing correction. We introduce a new two‐step procedure for testing G × G interactions in case‐control GWAS to detect interacting single nucleotide polymorphisms (SNPs) regardless of their marginal effects. In an initial screening step, all SNP pairs are tested for gene‐gene association in the combined sample of cases and controls. In the second step, the pairs that pass the screening are followed up with a traditional test for G × G interaction. We show that the two‐step method is substantially more powerful to detect G × G interactions than the exhaustive testing approach. For example, with 2,000 cases and 2,000 controls, the two‐step method can have more than 90% power to detect an interaction odds ratio of 2.0 compared to less than 50% power for the exhaustive testing approach. Moreover, we show that a hybrid two‐step approach that combines our newly proposed two‐step test and the two‐step test that screens for marginal effects retains the best power properties of both. The two‐step procedures we introduce have the potential to uncover genetic signals that have not been previously identified in an initial single‐SNP GWAS. We demonstrate the computational feasibility of the two‐step G × G procedure by performing a G × G scan in the asthma GWAS of the University of Southern California Childrens Health Study.