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.     

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 interactionsbetween genes and environmental factors. However, investigatorsconducting genome-wide association studies typically test foronly the marginal effects of each genetic marker on disease.In this paper, the authors propose an efficient and easily implemented2-step analysis of genome-wide association study data aimedat identifying genes involved in a gene-environment interaction.The procedure complements screening for marginal genetic effectsand thus has the potential to uncover new genetic signals thathave not been identified previously. association; environment; genes; genetic markers; genetics; genome     

Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome

One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.     

Testing association between disease and multiple SNPs in a candidate gene

Current technology allows investigators to obtain genotypes at multiple single nucleotide polymorphism (SNPs) within a candidate locus. Many approaches have been developed for using such data in a test of association with disease, ranging from genotype-based to haplotype-based tests. We develop a new approach that involves two basic steps. In the first step, we use principal components (PCs) analysis to compute combinations of SNPs that capture the underlying correlation structure within the locus. The second step uses the PCs directly in a test of disease association. The PC approach captures linkage-disequilibrium information within a candidate region, but does not require the difficult computing implicit in a haplotype analysis. We demonstrate by simulation that the PC approach is typically as or more powerful than both genotype- and haplotype-based approaches. We also analyze association between respiratory symptoms in children and four SNPs in the Glutathione-S-Transferase P1 locus, based on data from the Children's Health Study. We observe stronger evidence of an association using the PC approach (p = 0.044) than using either a genotype-based (p = 0.13) or haplotype-based (p = 0.052) approach.     

Murcray et al. Respond to the Invited Commentaries

We thank Chatterjee and Wacholder (1) and Khoury and Wacholder(2) for their insightful comments related to our paper in thisissue of the Journal (3). Khoury and Wacholder underscore akey motivating factor in our work, specifically that genome-wideassociation studies have the potential to discover genes thatinteract with environmental factors. They raise several importantissues that must be considered when integrating environmentalfactors     

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 GE 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 genomewide 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.