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Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors. © 2013 WILEY PERIODICALS, INC.

Genet Med

Omics research poses acute challenges regarding how to enhance validation practices and eventually the utility of this rich information. Several strategies may be useful, including routine replication, public data and protocol availability, funding incentives, reproducibility rewards or penalties, and targeted repeatability checks. Science

Recent emphasis on translational research (TR) is highlighting the role of epidemiology in translating scientific discoveries into population health impact. The authors present applications of epidemiology in TR through 4 phases designated T1-T4, illustrated by examples from human genomics. In T1, epidemiology explores the role of a basic scientific discovery (e.g., a disease risk factor or biomarker) in developing a "candidate application" for use in practice (e.g., a test used to guide interventions). In T2, epidemiology can help to evaluate the efficacy of a candidate application by using observational studies and randomized controlled trials. In T3, epidemiology can help to assess facilitators and barriers for uptake and implementation of candidate applications in practice. In T4, epidemiology can help to assess the impact of using candidate applications on population health outcomes. Epidemiology also has a leading role in knowledge synthesis, especially using quantitative methods (e.g., meta-analysis). To explore the emergence of TR in epidemiology, the authors compared articles published in selected issues of the Journal in 1999 and 2009. The proportion of articles identified as translational doubled from 16% (11/69) in 1999 to 33% (22/66) in 2009 (P = 0.02). Epidemiology is increasingly recognized as an important component of TR. By quantifying and integrating knowledge across disciplines, epidemiology provides crucial methods and tools for TR. Am J Epidemiol

Biol Psychiatry

PURPOSE: Consortia of investigators currently compile sufficiently large sample sizes to investigate the effects of low-risk susceptibility genetic variants. It is not clear how the results obtained by consortia compare with those derived from meta-analyses of published studies. METHODS: We performed meta-analyses of published data for 16 genetic polymorphisms investigated by the Breast Cancer Association Consortium, and compared sample sizes, heterogeneity, and effect sizes. PubMed, Web of Science, and Human Genome Epidemiology Network databases were searched for breast cancer case-control association studies. RESULTS: We found that meta-analyses of published data and consortium analyses were based on substantially different data. Published data by non-consortium teams amounted on average to 26.9% of all available data (range 3.0 -50.0%). Both approaches showed statistically significant decreased breast cancer risks for CASP8 D302H. The meta-analyses of published data demonstrated statistically significant results for five other genes and the consortium analyses for two other genes, but the strength of this evidence, evaluated on the basis of the Venice criteria, was not strong. CONCLUSIONS: Because both approaches identified the same gene out of 16 candidates, the methods can be complimentary. The expense and complexity of consortium-based studies should be considered vis-a-vis the potential methodological limitations of synthesis of published studies. Genet Med

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading. Am J Hum Genet

The authors evaluated whether there is an excess of statistically significant results in studies of genetic associations with Alzheimer's disease reflecting either between-study heterogeneity or bias. Among published articles on genetic associations entered into the comprehensive AlzGene database (www.alzgene.org) through January 31, 2007, 1,348 studies included in 175 meta-analyses with 3 or more studies each were analyzed. The number of observed studies (O) with statistically significant results (P = 0.05 threshold) was compared with the expected number (E) under different assumptions for the magnitude of the effect size. In the main analysis, the plausible effect size of each association was the summary effect presented in the respective meta-analysis. Overall, 19 meta-analyses (all with eventually nonsignificant summary effects) had a documented excess of O over E: Typically single studies had significant effects pointing in opposite directions and early summary effects were dissipated over time. Across the whole domain, O was 235 (17.4%), while E was 164.8 (12.2%) (P < 10(-6)). The excess showed a predilection for meta-analyses with nonsignificant summary effects and between-study heterogeneity. The excess was seen for all levels of statistical significance and also for studies with borderline P values (P = 0.05-0.10). The excess of significant findings may represent significance-chasing biases in a setting of massive testing. Am J Epidemiol

In an effort to pinpoint potential genetic risk factors for schizophrenia, research groups worldwide have published over 1,000 genetic association studies with largely inconsistent results. To facilitate the interpretation of these findings, we have created a regularly updated online database of all published genetic association studies for schizophrenia ('SzGene'). For all polymorphisms having genotype data available in at least four independent case-control samples, we systematically carried out random-effects meta-analyses using allelic contrasts. Across 118 meta-analyses, a total of 24 genetic variants in 16 different genes (APOE, COMT, DAO, DRD1, DRD2, DRD4, DTNBP1, GABRB2, GRIN2B, HP, IL1B, MTHFR, PLXNA2, SLC6A4, TP53 and TPH1) showed nominally significant effects with average summary odds ratios of approximately 1.23. Seven of these variants had not been previously meta-analyzed. According to recently proposed criteria for the assessment of cumulative evidence in genetic association studies, four of the significant results can be characterized as showing 'strong' epidemiological credibility. Our project represents the first comprehensive online resource for systematically synthesized and graded evidence of genetic association studies in schizophrenia. As such, it could serve as a model for field synopses of genetic associations in other common and genetically complex disorders. Nat Genet

Many gene-disease associations proposed to date have not been consistently replicated across different populations. Nonreplication often reflects false positives in the original claims. However, occasionally, nonreplication may be due to heterogeneity due to biases or even genuine diversity of the genetic effects in different populations. Here, we propose methods for estimating the required sample size to replicate an association across many studies with different amounts of between-study heterogeneity, when data are summarized through metaanalysis. We demonstrate thresholds of between-study heterogeneity (tau(0)(2)) above which one cannot reach adequate power to replicate a proposed association at a specified level of statistical significance when k studies are performed (regardless of how large these studies are). Based on empirical evidence from 91 proposed gene-disease associations (50 on candidate genes and 41 from genome-wide association efforts), the observed between-study heterogeneity is often close to or even surpasses nonreplicability thresholds. With more modest between-study heterogeneity, the required sample size increases considerably compared with when no between-study heterogeneity exists. Increases are steep as tau(0)(2) is approached. Therefore, some true associations may not be practically possible to replicate with consistency, no matter how large studies are conducted. Efforts should be made to minimize between-study heterogeneity in targeted genetic effects. Proc Natl Acad Sci U S A

Emerging technologies are allowing researchers to study hundreds of thousands of genetic variants simultaneously as risk factors for common complex diseases. Both theoretical considerations and empirical evidence suggest that specific genetic variants causally associated with common diseases will have small effects (risk ratios mostly

The authors evaluated whether statistically significant violations of Hardy-Weinberg equilibrium (HWE) or the magnitude of deviations from HWE may contribute to the problem of replicating postulated gene-disease associations across different studies. Forty-two gene-disease associations assessed in meta-analyses of 591 studies were examined. Studies with disease-free controls in which HWE was violated gave significantly different results from HWE-conforming studies in five instances. Exclusion of the former studies resulted in loss of statistical significance of the overall meta-analysis in three instances and more than a 10% change in the summary odds ratio in six. Exclusion of HWE-violating studies changed the formal significance of the estimated between-study heterogeneity in three instances. After adjustment for the magnitude of the deviation from HWE for the controls, formal significance was lost in another three instances. Studies adjusted for the magnitude of deviation from HWE tended to become more heterogeneous among themselves, and, for seven gene-disease associations, between-study heterogeneity became significant, while it was not so in the unadjusted analyses. Gene-disease association studies and meta-analyses thereof should routinely scrutinize the potential impact of HWE violations as well as nonsignificant deviations from the exact frequencies expected under HWE. Postulated genetic associations with modest-sized odds ratios and borderline statistical significance may not be robust in such sensitivity analyses. Am J Epidemiol

Accumulated evidence from searching for candidate gene-disease associations of complex diseases can offer some insights as the field moves toward discovery-oriented approaches with massive genome-wide testing. Meta-analyses of 50 non-human lymphocyte antigen gene-disease associations with documented overall statistical significance (752 studies) show summary odds ratios with a median of 1.43 (interquartile range, 1.28-1.65). Many different biases may operate in this field, for both single studies and meta-analyses, and these biases could invalidate some of these seemingly "validated" associations. Studies with a sample size of >500 show a median odds ratio of only 1.15. The median sample size required to detect the observed summary effects in each population addressed in the 752 studies is estimated to be 3,535 (interquartile range, 1,936-9,119 for cases and controls combined). These estimates are steeply inflated in the presence of modest bias. Population heterogeneity, as well as gene-gene and gene-environment interactions, could steeply increase these estimates and may be difficult to address even by very large biobanks and observational cohorts. The one visible solution is for a large number of teams to join forces on the same research platforms. These collaborative studies ideally should be designed up front to also assess more complex gene-gene and gene-environment interactions. Am J Epidemiol

OBJECTIVES: The onset and severity of the clinical expression of most diseases that are of public health importance are influenced by genetic predisposition. The ability to assess human genetic predisposition for many diseases is increasing rapidly. Therefore, state public health agencies should be incorporating new developments in genetics and disease prevention into their core functions of assessment, policy development, and assurance. The authors assessed the status of this process. METHODS: The Council of State and Territorial Epidemiologists (CSTE) surveyed states about projects and concerns related to genetics and public health activities. Respondents were the Health Officer, the Maternal and Child Health/Genetics Program Director, the Chronic Disease Program Director, and the Laboratory Director. Where applicable, responses were categorized into assessment, policy development, and assurance functions. RESULTS: Thirty-eight (76%) state health departments responded. Ongoing genetics activities were assurance (82%), assessment (17%), and policy development (2%). In contrast, Health Officers responded that future genetics activities would be distributed differently: assurance, 41%; assessment, 36%; and policy development, 23%. Future assurance activities would be largely educational. Topics of interest and recently initiated activities in genetics were primarily assessment functions. Funding was the greatest concern, followed by lack of proven disease prevention measures and outcomes data. CONCLUSIONS: State health departments recognize a need to realign their activities to meet future developments in genetics. Lack of adequate resources, proven disease prevention measures, and outcomes data are potential barriers. Public health agencies need to develop a strategic plan to meet the opportunities associated with the development and implementation of genetic tests and procedures.

Rhabdomyosarcoma (RMS) is an uncommon malignant soft tissue sarcoma whose cause is largely unknown. Reported risk factors include genetic alterations (e.g., p53 mutations, a defective gene at 11p15.5, or specific chromosomal translocation of t(2:13)), and parents' use of drugs around the time of conception. We present results from a national, case‐control study of 249 RMS cases (170 males and 79 females) and 302 controls (196 males and 106 females). The cases, aged 0–20 years at diagnosis, were identified via the Intergroup RMS Study‐III during 1982–1988. Controls were selected by random digit telephone dialing. As a supplement to the original study, information on genetic diseases and birth defects (BD) was collected from the subjects' parents by telephone interview. Fifty‐six (22.5%) cases and 55 (18.2%) controls were reported to have genetic diseases or BD (odds ratio [OR] = 1.30,95% confidence interval [CI] = 0.85–2.02, P = .21). The case group had a significantly higher frequency of neurofibromatosis type I (NF1) than did the control group, i.e., five cases (2.0%) had NF1 vs. zero controls (P = .02). The case group also had a higher frequency of major BDs than did the control group (6.0% vs. 2.6%, OR = 2.36, 95% CI = 0.92–6.52, P = .05). However, this excess was only observed in males (7.6% vs. 2.6%, OR = 3.16, 95% CI = 1.02–10.41, P = .02). Among the 15 cases having both RMS and major BDs, six (40.0%) had both conditions in the same regional anatomic site: Two (13.3%) had both in the extremities, two (13.3%) in the genitourinary system, and two in the head and neck. These findings suggest that common genetic mechanisms or in utero exposures may be invilved in the development of many childhood tumors and congenital abnormalities.