Marta Gwinn

The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?

Advances in genomics have led to mounting expectations in regard to their impact on health care and disease prevention. In light of this fact, a comprehensive research agenda is needed to move human genome discoveries into health practice in a way that maximizes health benefits and minimizes harm to individuals and populations. We present a framework for the continuum of multidisciplinary translation research that builds on previous characterization efforts in genomics and other areas in health care and prevention. The continuum includes four phases of translation research that revolve around the development of evidence-based guidelines. Phase 1 translation (T1) research seeks to move a basic genome-based discovery into a candidate health application (e.g., genetic test/intervention). Phase 2 translation (T2) research assesses the value of a genomic application for health practice leading to the development of evidence-based guidelines. Phase 3 translation (T3) research attempts to move evidence-based guidelines into health practice, through delivery, dissemination, and diffusion research. Phase 4 translation (T4) research seeks to evaluate the “real world” health outcomes of a genomic application in practice. Because the development of evidence-based guidelines is a moving target, the types of translation research can overlap and provide feedback loops to allow integration of new knowledge. Although it is difficult to quantify how much of genomics research is T1, we estimate that no more than 3% of published research focuses on T2 and beyond. Indeed, evidence-based guidelines and T3 and T4 research currently are rare. With continued advances in genomic applications, however, the full continuum of translation research needs adequate support to realize the promise of genomics for human health.

STrengthening the REporting of Genetic Association Studies (STREGA)--an extension of the STROBE statement

Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies.

A navigator for human genome epidemiology

To the Editor: Recent successes in large-scale genetic association studies call for renewed attention to integrating research results, not only among studies, but across disciplines1. At the molecular level, genetic polymorphisms provide a starting point for investigating the functions of complex biological systems. At the population level, epidemiologists can begin to use data on genetic variation, associations and interactions to interpret population attributable fractions and estimate the potential health impact of genetically directed interventions2. Publicly available genetic sequence databases have demonstrated their value in accelerating the Human Genome Project and advancing the field of molecular genetics; newer efforts, such as dbGaP and CGEMS, are now beginning to make genotypephenotype data broadly available to the scientific community3. The published scientific literature also reflects rapid growth in studies of human genetic factors in relation to health and disease. Since 2001, the Human Genome Epidemiology Network (HuGENet) has maintained a database of published, population-based epidemiologic studies of human genes extracted from PubMed4. We recently replaced our PubMed search strategy with a new approach using machine learning, which has reduced manual effort and increased both the sensitivity and specificity of screening. Our curator updates the database weekly with articles newly added to PubMed and assigns to them one or more study types (for example, observational study, meta-analysis or genome-wide association study) and data categories (for example, gene-disease association, gene-environment interaction or pharmacogenomics). Each article is indexed in the database with MeSH terms (using the MeSH hierarchical structure) and gene information from the National Center for Bioinformatics (NCBI) Entrez Gene database. As of November 2007, the database has indexed more than 30,000 articles, referencing more than 3,000 genes and nearly 2,000 disease terms (Table 1). Most articles (80%) describe genetic associations. Approximately 20% of all articles were published in 2007, including 68 of 82 genome-wide association studies. To make this database more accessible and useful to interdisciplinary researchers, we have developed an integrated set of applications known collectively as the HuGE Navigator (http://www.hugenavigator. net). Using PubMed abstracts as the core data source, we have developed data and text mining algorithms to create a knowledge base for exploring genetic associations, candidate gene selection and investigator networks. Genetic information can be displayed whenever needed from major gene-centered databases (for example, Entrez Gene, SwissProt, OMIM and GeneCards), as well as from databases of genetic variation and prevalence (for example, dbSNP and HapMap Project), pathways (for example, CGAP, KEGG and BioCarta), and other aspects (for example, Gene Ontology and Gene Clinics). The HuGE Navigator is constructed according to the principles of open source, standardization, interoperability and extensibility, so that new applications can be easily incorporated5. Currently, the HuGE Navigator allows users to navigate and search the database in an integrated manner by using the six applications discussed below. The HuGE Literature Finder is a search engine for finding published literature on human genome epidemiology, including genetic association studies. The search query can include disease terms, environmental factors, genes, or author names and affiliations. The search results can be further refined by using filtering features, including disease, gene, category, study type, author, year, journal, and country. The filtering process can be performed indefinitely until the desired result is obtained. The results (PubMed IDs) can be exported to the PubMed Web site for further exploration and downloading to bibliographic software. The HuGE Investigator Browser is a search engine for finding investigators or collaborators on the basis of research interests, such as diseases, risk factors, or genes. We extract investigator data by using an accessory utility that automatically parses the affiliation data provided by PubMed6. GeneSelectAssist is a search tool for finding possible candidate genes associated with the subject of interest. Search terms can include diseases and exposures. GeneSelectAssist selects and prioritizes genes on the basis of genetic association studies in the HuGE Navigator database, as well as other PubMed abstracts, and evidence from animal models in the NCBI Entrez Gene database. HuGE Watch is a tool for tracking the evolution of human genome epidemiology research dynamically, on the basis of the literature database. It allows users to view temporal trends in publication by gene, disease, and number of investigators, as well as by the geographic distribution of authors. HuGEpedia is an online encyclopedia that summarizes research on gene-disease associations. We are currently developing a system for extracting data from meta-analyses and published genome-wide associations that will form the basis for a disease-specific synopsis written by domain experts. HuGEpedia can be searched by gene or disease. HuGE Risk Translator is a tool that assesses the validity of genetic variants for predicting health outcomes by calculating epidemiologic measures such as population attributable risk, sensitivity, specificity and positive and negative predictive values. The HuGE Navigator offers a new way to navigate and mine the growing scientific literature on human gene-disease associations and related data in human genome epidemiology. As an interconnected system of applications that users can enter by using genes, diseases, or risk factors as the starting point, HuGE Navigator provides a potential bridge between epidemiologic and genetic research domains. Disease and gene names are mapped to standardized vocabularies, so investigators can use their preferred terms to query the knowledge base. By linking to disease-specific databases, such as AlzGene7, HuGE Navigator aims to be the vehicle for navigating the ‘network of networks’ of investigators now working to

Can family history be used as a tool for public health and preventive medicine

Most common chronic diseases are the result of complex interactions between multiple genetic variants and environmental factors. Despite significant advances in the last decade in the understanding of our genome, there are substantial limitations in epidemiological and analytic approaches to studying the effects of genetic determinants of common chronic diseases. Knowledge of genetic variation underlying disease susceptibility should improve our ability to diagnose, manage, and prevent these disorders. To date, however, DNA-based testing is limited for the most part to analysis of highly penetrant single gene disorders that account for approximately 5% of the total disease burden in the population.1, 2 It may be years before DNAbased tests are routinely applied to predict the onset of common diseases, their natural history, and response to therapy.

A Critical Appraisal of the Scientific Basis of Commercial Genomic Profiles Used to Assess Health Risks and Personalize Health Interventions

Predictive genomic profiling used to produce personalized nutrition and other lifestyle health recommendations is currently offered directly to consumers. By examining previous meta-analyses and HuGE reviews, we assessed the scientific evidence supporting the purported gene-disease associations for genes included in genomic profiles offered online. We identified seven companies that offer predictive genomic profiling. We searched PubMed for meta-analyses and HuGE reviews of studies of gene-disease associations published from 2000 through June 2007 in which the genotypes of people with a disease were compared with those of a healthy or general-population control group. The seven companies tested at least 69 different polymorphisms in 56 genes. Of the 56 genes tested, 24 (43%) were not reviewed in meta-analyses. For the remaining 32 genes, we found 260 meta-analyses that examined 160 unique polymorphism-disease associations, of which only 60 (38%) were found to be statistically significant. Even the 60 significant associations, which involved 29 different polymorphisms and 28 different diseases, were generally modest, with synthetic odds ratios ranging from 0.54 to 0.88 for protective variants and from 1.04 to 3.2 for risk variants. Furthermore, genes in cardiogenomic profiles were more frequently associated with noncardiovascular diseases than with cardiovascular diseases, and though two of the five genes of the osteogenomic profiles did show significant associations with disease, the associations were not with bone diseases. There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention.

Sodium and Potassium Intake and Mortality Among US Adults: Prospective Data From the Third National Health and Nutrition Examination Survey

BACKGROUND Several epidemiologic studies suggested that higher sodium and lower potassium intakes were associated with increased risk of cardiovascular diseases (CVD). Few studies have examined joint effects of dietary sodium and potassium intake on risk of mortality. METHODS To investigate estimated usual intakes of sodium and potassium as well as their ratio in relation to risk of all-cause and CVD mortality, the Third National Health and Nutrition Examination Survey Linked Mortality File (1988-2006), a prospective cohort study of a nationally representative sample of 12,267 US adults, studied all-cause, cardiovascular, and ischemic heart (IHD) diseases mortality. RESULTS During a mean follow-up period of 14.8 years, we documented a total of 2270 deaths, including 825 CVD deaths and 443 IHD deaths. After multivariable adjustment, higher sodium intake was associated with increased all-cause mortality (hazard ratio [HR], 1.20; 95% confidence interval [CI], 1.03-1.41 per 1000 mg/d), whereas higher potassium intake was associated with lower mortality risk (HR, 0.80; 95% CI, 0.67-0.94 per 1000 mg/d). For sodium-potassium ratio, the adjusted HRs comparing the highest quartile with the lowest quartile were HR, 1.46 (95% CI, 1.27-1.67) for all-cause mortality; HR, 1.46 (95% CI, 1.11-1.92) for CVD mortality; and HR, 2.15 (95% CI, 1.48-3.12) for IHD mortality. These findings did not differ significantly by sex, race/ethnicity, body mass index, hypertension status, education levels, or physical activity. CONCLUSION Our findings suggest that a higher sodium-potassium ratio is associated with significantly increased risk of CVD and all-cause mortality, and higher sodium intake is associated with increased total mortality in the general US population.

The Reduction in Risk of Ovarian Cancer Associated with Oral-Contraceptive Use

Although several studies have reported that the use of oral contraceptives decreases the risk of ovarian cancer, it is not clear whether the effect varies according to the oral-contraceptive formulation or the histologic type of cancer. To characterize this association more fully, we used data from a case-control study, the Cancer and Steroid Hormone Study. From 1980 to 1982, 546 women 20 to 54 years of age with ovarian cancer were enrolled from eight population-based cancer registries. The controls were 4228 women selected from the same areas. Women who had used oral contraceptives had a risk of epithelial ovarian cancer of 0.6 (95 percent confidence interval, 0.5 to 0.7) as compared with those who had never used them. This protective effect was seen in women who had used oral contraceptives for as little as three to six months, and it continued for 15 years after use ended; it was independent of the specific oral-contraceptive formulation and of the histologic type of epithelial ovarian cancer. (We could not adequately assess the association with nonepithelial ovarian cancers because of an insufficient number of cases.) We conclude that the use of oral contraceptives decreases the risk of epithelial ovarian cancer.

Pregnancy, breast feeding, and oral contraceptives and the risk of epithelial ovarian cancer

To quantify the effects of cumulative months of pregnancy, breast feeding, and oral contraceptive use on the risk of developing epithelial ovarian cancer, the authors used data collected for the Cancer and Steroid Hormone Study--a multicenter, population-based, case-control study. Detailed reproductive histories were obtained from 436 women aged 20-54 with epithelial ovarian cancer newly diagnosed between December 1980 and December 1982, and from 3833 women aged 20-54 selected at random from the same geographic areas. Estimated relative risks of epithelial ovarian cancer were 0.6 (95% confidence interval (CI) 0.5-0.8) for women who had ever been pregnant, 0.6 (95% CI 0.5-0.8) for women who had ever breast fed, and 0.5 (95% CI 0.5-0.7) for women who had ever used oral contraceptives. Logistic regression analysis revealed a strong trend in decreasing risk of epithelial ovarian cancer with increasing cumulative months of pregnancy; this effect was less pronounced in women aged 50-54 than in younger women. In contrast, a marked reduction in risk was associated with ever having breast fed or used oral contraceptives, while the decrease in risk from additional months of either of these exposures was less than that for pregnancy.

The Scientific Foundation for Personal Genomics: Recommendations from a National Institutes of Health–Centers for Disease Control and Prevention Multidisciplinary Workshop

The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed.

The Emergence of Translational Epidemiology: From Scientific Discovery to Population Health Impact

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.