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.

In a genomewide association study (GWAS), investigators typically focus their primary analysis on the direct (marginal) associations of each SNP with the trait. Some SNPs that are truly associated with the trait may not be identified in this scan if they have a weak marginal effect and thus low power to be detected. However, these SNPs may be quite important in subgroups of the population defined by an environmental or personal factor, and may be detectable if such a factor is carefully considered in a gene-environment (G×E) interaction analysis. We address the question “Using a genome wide interaction scan (GWIS), can we find new genes that were not found in the primary GWAS scan?” We review commonly used approaches for conducting a GWIS in case-control studies, and propose a new 2-step screening and testing method (EDG×E) that is optimized to find genes with a weak marginal effect. We simulate several scenarios in which our 2-step method provides 70–80% power to detect a disease locus while a marginal scan provides less than 5% power. We also provide simulations demonstrating that the EDG×E method outperforms other GWIS approaches (including case only and previously proposed 2-step methods) for finding genes with a weak marginal effect. Application of this method to a G × Sex scan for childhood asthma reveals two potentially interesting SNPs that were not identified in the marginal-association scan. We distribute a new software program (G×Escan, available at http://biostats.usc.edu/software) that implements this new method as well as several other GWIS approaches.

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.

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) proposed screening on a test that models the G-E association induced by an interaction in the combined case-control sample. Alternatively, Kooperberg et al (2008) 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.

Purpose To assess survival following radical prostatectomy (RP), intensity modulated radiation therapy (IMRT) or conformal radiation therapy (CRT) versus no local therapy (NLT) for metastatic prostate cancer (MPCa), adjusting for patient comorbidity, androgen deprivation therapy (ADT) and other factors. Materials and Methods Men ≥66 with MPCa undergoing treatment by RP, IMRT, CRT or NLT identified from SEER-Medicare linked database (2004–2009). Multivariable Cox proportional hazards models, before and after inverse propensity score weighting, were used to assess all cause and PCa specific mortality. Competing risk regression analysis was used to assess PCa specific mortality. Results Among 4069 men with MPCa, RP (n=47), IMRT (n=88), CRT (n=107) were selected as local therapy versus NLT (n=3827). RP was associated with a 52% (HR: 0.48, 95% CI: 0.27–0.85) reduction in the risk of PCa specific mortality, after adjusting for socio-demographic, primary tumour characteristics, comorbidity, ADT and bone radiation within 6 months of diagnosis. IMRT was associated with a 62% (HR: 0.38, 95% CI: 0.24–0.61) reduction in the risk of PCa specific mortality, respectively. CRT was not associated with improved survival compared to NLT. Propensity score weighting yielded comparable results. Competing risk analysis revealed a 42% (SHR: 0.58, 95% CI: 0.35–0.95) and 57% (SHR: 0.43, 95% CI: 0.27–0.68) reduction in the risk of PCa specific mortality for RP and IMRT. Conclusions Local therapy with RP and IMRT, but not CRT, was associated with a survival benefit in MPC and warrants prospective evaluation in clinical trials