Juan Pablo Lewinger

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.

Finding Novel Genes by Testing G × E Interactions in a Genome-Wide Association Study

In a genome‐wide association study (GWAS), investigators typically focus their primary analysis on the direct (marginal) associations of each single nucleotide polymorphism (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 two‐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 two‐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 two‐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.

Radical Prostatectomy or External Beam Radiation Therapy vs No Local Therapy for Survival Benefit in Metastatic Prostate Cancer: A SEER-Medicare Analysis

PURPOSE We assessed survival after radical prostatectomy, intensity modulated radiation therapy or conformal radiation therapy vs no local therapy for metastatic prostate cancer adjusting for patient comorbidity, androgen deprivation therapy and other factors. MATERIALS AND METHODS We identified men 66 years old or older with metastatic prostate cancer treated with radical prostatectomy, intensity modulated radiation therapy, conformal radiation therapy or no local therapy in the SEER-Medicare linked database from 2004 to 2009. Multivariable Cox proportional hazards models before and after inverse propensity score weighting were used to assess all cause and prostate cancer specific mortality. Competing risk regression analysis was done to assess prostate cancer specific mortality. RESULTS Of 4,069 men with metastatic prostate cancer radical prostatectomy in 47, intensity modulated radiation therapy in 88 and conformal radiation therapy in 107 were selected as local therapy vs no local therapy in 3,827. Radical prostatectomy was associated with a 52% decrease (HR 0.48, 95% CI 0.27-0.85) in the risk of prostate cancer specific mortality after adjusting for sociodemographics, primary tumor characteristics, comorbidity, androgen deprivation therapy and bone radiation within 6 months of diagnosis. Intensity modulated radiation therapy was associated with a 62% decrease (HR 0.38, 95% CI 0.24-0.61) in the risk of prostate specific cancer specific mortality. Conformal radiation therapy was not associated with improved survival compared to no local therapy. Propensity score weighting yielded comparable results. Competing risk analysis revealed a 42% and 57% decrease (SHR 0.58, 95% CI 0.35-0.95 and SHR 0.43, 95% CI 0.27-0.68, respectively) in the risk of prostate cancer specific mortality for radical prostatectomy and intensity modulated radiation therapy. CONCLUSIONS Local therapy with radical prostatectomy and intensity modulated radiation therapy but not with conformal radiation therapy was associated with a survival benefit in men with metastatic prostate cancer. This finding warrants prospective evaluation in clinical trials.

Red meat and poultry, cooking practices, genetic susceptibility and risk of prostate cancer: results from a multiethnic case–control study

Red meat, processed and unprocessed, has been considered a potential prostate cancer (PCA) risk factor; epidemiological evidence, however, is inconclusive. An association between meat intake and PCA may be due to potent chemical carcinogens that are generated when meats are cooked at high temperatures. We investigated the association between red meat and poultry intake and localized and advanced PCA taking into account cooking practices and polymorphisms in enzymes that metabolize carcinogens that accumulate in cooked meats. We analyzed data for 1096 controls, 717 localized and 1140 advanced cases from the California Collaborative Prostate Cancer Study, a multiethnic, population-based case-control study. We examined nutrient density-adjusted intake of red meat and poultry and tested for effect modification by 12 SNPs and 2 copy number variants in 10 carcinogen metabolism genes: GSTP1, PTGS2, CYP1A2, CYP2E1, EPHX1, CYP1B1, UGT1A6, NAT2, GSTM1 and GSTT1. We observed a positive association between risk of advanced PCA and high intake of red meat cooked at high temperatures (trend P = 0.026), cooked by pan-frying (trend P = 0.035), and cooked until well-done (trend P = 0.013). An inverse association was observed for baked poultry and advanced PCA risk (trend P = 0.023). A gene-by-diet interaction was observed between an SNP in the PTGS2 gene and the estimated levels of meat mutagens (interaction P = 0.008). Our results support a role for carcinogens that accumulate in meats cooked at high temperatures as potential PCA risk factors, and may support a role for heterocyclic amines (HCAs) in PCA etiology.

Genotype–Environment Interactions in Microsatellite Stable/Microsatellite Instability-Low Colorectal Cancer: Results from a Genome-Wide Association Study

Background: Genome-wide association studies (GWAS) have led to the identification of a number of common susceptibility loci for colorectal cancer (CRC); however, none of these GWAS have considered gene–environment (G × E) interactions. Therefore, it is unclear whether current hits are modified by environmental exposures or whether there are additional hits whose effects are dependent on environmental exposures. Methods: We conducted a systematic search for G × E interactions using genome wide data from the Colon Cancer Family Registry that included 1,191 cases of microsatellite stable (MSS) or microsatellite instability–low (MSI-L) CRC and 999 controls genotyped using either the Illumina Human1M or Human1M-Duo BeadChip. We tested for interactions between genotypes and 14 environmental factors using 3 methods: a traditional case–control test, a case-only test, and the recently proposed 2-step method by Murcray and colleagues. All potentially significant findings were replicated in the ARCTIC Study. Results: No G × E interactions were identified that reached genome-wide significance by any of the 3 methods. When analyzing previously reported susceptibility loci, 7 significant G × E interactions were found at a 5% significance level. We investigated these 7 interactions in an independent sample and none of the interactions were replicated. Conclusions: Identifying G × E interactions will present challenges in a GWAS setting. Our power calculations illustrate the need for larger sample sizes; however, as CRC is a heterogeneous disease, a tradeoff between increasing sample size and heterogeneity needs to be considered. Impact: The results from this first genome-wide analysis of G × E in CRC identify several challenges, which may be addressed by large consortium efforts. Cancer Epidemiol Biomarkers Prev; 20(5); 758–66. ©2011 AACR.

Testicular germ cell tumor susceptibility associated with the UCK2 locus on chromosome 1q23

Genome-wide association studies (GWASs) have identified multiple common genetic variants associated with an increased risk of testicular germ cell tumors (TGCTs). A previous GWAS reported a possible TGCT susceptibility locus on chromosome 1q23 in the UCK2 gene, but failed to reach genome-wide significance following replication. We interrogated this region by conducting a meta-analysis of two independent GWASs including a total of 940 TGCT cases and 1559 controls for 122 single-nucleotide polymorphisms (SNPs) on chromosome 1q23 and followed up the most significant SNPs in an additional 2202 TGCT cases and 2386 controls from four case-control studies. We observed genome-wide significant associations for several UCK2 markers, the most significant of which was for rs3790665 (PCombined = 6.0 × 10(-9)). Additional support is provided from an independent familial study of TGCT where a significant over-transmission for rs3790665 with TGCT risk was observed (PFBAT = 2.3 × 10(-3)). Here, we provide substantial evidence for the association between UCK2 genetic variation and TGCT risk.

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.

Cryptorchidism and testicular germ cell tumors: comprehensive meta-analysis reveals that association between these conditions diminished over time and is modified by clinical characteristics

Introduction: Risk of testicular germ cell tumors (TGCT) is consistently associated with a history of cryptorchidism (CO) in epidemiologic studies. Factors modifying the association may provide insights regarding etiology of TGCT and suggest a basis for individualized care of CO. To identify modifiers of the CO-TGCT association, we conducted a comprehensive, quantitative evaluation of epidemiologic data. Materials and Methods: Human studies cited in PubMed or ISI Web of Science indices through December 2011 and selected unpublished epidemiologic data were reviewed to identify 35 articles and one unpublished dataset with high-quality data on the CO-TGCT association. Association data were extracted as point and 95% confidence interval estimates of odds ratio (OR) or standardized incidence ratio (SIR), or as tabulated data. Values were recorded for each study population, and for subgroups defined by features of study design, CO and TGCT. Extracted data were used to estimate summary risk ratios (sRR) and evaluate heterogeneity of the CO-TGCT association between subgroups. Results: The overall meta-analysis showed that history of CO is associated with four-fold increased TGCT risk [RR = 4.1(95% CI = 3.6–4.7)]. Subgroup analyses identified five determinants of stronger association: bilateral CO, unilateral CO ipsilateral to TGCT, delayed CO treatment, TGCT diagnosed before 1970, and seminoma histology. Conclusions: Modifying factors may provide insight into TGCT etiology and suggest improved approaches to managing CO. Based on available data, CO patients and their parents or caregivers should be made aware of elevated TGCT risk following orchidopexy, regardless of age at repair, unilateral vs. bilateral non-descent, or position of undescended testes.