of Program Coordination, Planning, and Strategic Initiatives
Title of proposed idea: A synthetic cohort for the analysis of longitudinal effects of gene-environment interactions
Nominator: NIH Institutes/Centers
Major obstacle/challenge to overcome: Francis Collins has emphasized that there is a pressing need for a large-scale US prospective cohort study of genes and the environment, with a minimal sample size estimated at 500,000 (Collins, 2004; Manolio et al., 2006). However, even with a very large cohort, at least 7 to 15 years follow-up is needed to accrue enough incident cases to adequately power studies of common diseases (Burton et al, 2009).
Several NIH institutes support multiple high-quality cohort studies with rich biomedical, environmental, behavioral, and social data that, if strategically coordinated, could help elucidate how genes and environments interact to affect trajectories of health and disease (Willett et al., 2007). Recently, many of these cohorts have been extensively genotyped and some have undergone whole exome sequencing. Open sharing policies through dbGaP have created databases of extensive genotype data linked to phenotypes which are incompletely harmonized, and whose data standards vary significantly. More complex analyses could be served by a synthetic cohort with harmonized data. The longitudinal data included in these cohorts increase the reliability of measures and afford the examination of change phenotypes, which are crucial for understanding the causal pathways leading to changes in health. An integrated, harmonized, synthetic cohort will also facilitate the selection of genetically distinctive subpopulations for specific studies of genetic and environmental influences and their interaction. It would accelerate and support new approaches to discovery such as PheWAS (Denny et al., 2010; see also phenotype mining), fine-grained admixture mapping (Shriner et al., 2011), or conditioning on known risk variants to find potential gene-gene interactions via GWAS (Wijsman et al., 2011). Harnessing the potential inherent in these existing studies will allow us to analyze the roles played by life-style factors, social circumstances, and environmental exposures in modulating disease risk and progression.
We face a similar set of opportunities and challenges from the ever-growing number of patient registries, which increasingly include rich data, but where there is little consistency to date as to what data are reported or required. Investigators and patient advocacy groups alike express the need for creation of patient registries to facilitate research, but we are not yet getting the maximal benefit from the available data (Drolet & Johnson, 2008). In rare diseases especially, the ability to rapidly and accurately identify affected persons and to know enough about genotype and phenotype to be able to determine who might be informative for basic biology studies and who might be available for clinical trials is critical to the efficient conduct of research studies across the biomedical spectrum. The feasibility of greatly expanding current patient registries is reinforced by the fact that patients are increasingly willing to share their data by their participation in internet sites such as Patients Like Me (http://www.patientslikeme.com/), but these data collected through the sites have limited utility to researchers. Patients are also signing up to participate in research studies through sites such as Research Match at Vanderbilt (https://www.researchmatch.org/). The value of existing and proposed registries is severely limited by inconsistencies in data standards, ontologies, and policies for access and sharing and by the phenomenal inefficiencies of duplication of effort and investments. The need for such registries crosses multiple ICs.
Emerging scientific opportunity ripe for Common Fund investment: The initiative would systematically evaluate design issues for a synthetic cohort study of genes, environment, health, and behavior drawing from existing cohort studies which have rich longitudinal or early life data. The large collection of longitudinal NIH-funded cohorts that have valuable data on exposures throughout the life course have already had profound impact on medical, behavioral, and social science even before the genomic era. Many of these cohorts have added genetic and biomarker collections and now provide unparalleled opportunities for exploiting these epidemiological findings at a genomic level. To date, however, attempts to harmonize or synthesize data collection efforts among studies have been modest, in part due to resource constraints and IC boundaries.
Strikingly similar issues have arisen for patient registries, which have been proliferating rapidly both for rare and common diseases, with their goals now not limited to enhancing participation in clinical trials, but to enhancing our understanding of patient outcomes (e.g., AHRQ’s 2010 Registries for Evaluating Patient Outcomes: a User’s Guide). The common obstacle faced by both cohort studies and patient registries is a lack of harmonization among existing efforts. Essential analyses requiring large samples are frustrated by the lack of documented history of the creation of the registry, of common or documented consent procedures, of data standards, of common measures, and of time points for observation across the sample. Patient registries thus present an additional opportunity for trans-NIH harmonization. It should be possible to leverage the investment in the design of a virtual cohort to create a format for all NIH IC supported registries. With a common set of data standards, sharing of and access to data, all ICs could support registries within the virtual cohort system which would create efficiencies of scale and consistency of operations to greatly enhance the impact of individual IC investments.
With this initiative, we could also explore the possibility of collaborating with privately funded efforts (e.g., 23andMe) and cohorts funded by other government agencies. Although implementing the synthetic cohort design would require costs for harmonization and data collection, there are existing platforms for the former (e.g., P3G, www.p3g.org, resources developed by caBIG) and a growing number of freely available, high quality measures of phenotypes and exposures developed by NIH (e.g., instruments from the HRS, PROMIS, PhenX, and the NIH Toolbox). Furthermore, the HITECH and Affordable Care Acts have enhanced the prospects for the widespread incorporation of these measures in EHRs (e.g., the eMERGE network). This initiative is therefore well poised to create a time- and cost-effective harmonization plan.
Common Fund investment that could accelerate scientific progress in this field:
The recent special issue of Science (Feb 11 2011) on large datasets emphasized that ”large integrated data sets can potentially provide a much deeper understanding of both nature and society and open up many new avenues of research”. Better organization and access to data is imperative to realizing emerging scientific opportunities, including the development of common metadata. We propose that the Common Fund be used to invest in the three most important planning activities for a future synthetic cohort project and explore the potential for extending this effort to cover patient registries.
Although we believe that in the long run the synthetic cohort would be cost effective given that ICs currently maintain the full costs of participating studies, there are uncertainties as to the actual per-participant costs of any follow-up CF activity, and it will be crucial during the pilot to determine the post-CF longer term maintenance costs of the synthetic cohort and identify a credible source of funds (e.g., costs could be distributed across ICs/agencies supporting the synthesized cohorts, buying the substantial benefits of harmonization). The target design would include a synthetic cohort of 500,000 well-phenotyped participants. We estimate the cost of this initial developmental and feasibility initiative at $2.0 million per year for two years, with funds to be divided between RMS needs and supplements to enable cohort study investigators to participate.
Should the initial process of creating a virtual cohort prove successful, efforts could be extended to include issues related to patient registries. The initial investment by the Common Fund could create a program for investment of IC-specific resources for global benefit at marginal increases in cost. The Office of Rare Diseases has recently a web-based Global Rare Diseases Patient Registry-Data Repository (GRDR) which could form the nucleus of the registry (see Forrest et al. 2010, and Rubenstein et al., 2010, for more information). To ensure maximal benefit, the NIH would require the following of all data deposited in a new, central Registry:
Potential impact of Common Fund investment: The plan for a synthetic cohort for the analysis of longitudinal effects of gene-environment interactions would establish the feasibility and cost of an intended scalable synthetic national cohort of people for discovery research in health and disease; in essence it would design an affordable but well-powered phenome-genome project as recommended at the recent NIH Innovation Brainstorm meeting. Because the founder cohorts are longitudinal, the synthetic cohort would provide rapid access to trajectory information as well as a richer characterization of the social, environmental, and genetic factors influencing health and health disparities. Leveraging such a system to harmonize new and existing registries would ensure ongoing value, and provide multiple benefits: increased sample size for study of the pathogenesis and treatment of rare diseases, the ability to study the overlap in pathogenesis of apparently unrelated diseases sharing an etiology (e.g., coronary artery disease and invasive melanoma; see Manolio, 2010), and the chance to observe g-e interactions for phenotypically specific or genetically overlapping disease states, identifying genetic and environmental “pathogens” that can be studied in a synthetic cohort. This planning activity could also inform the design of a de novo national cohort study, or determine to what degree a synthetic cohort could provide similar information while allowing a greater degree of innovation within individual studies to address questions within their specific areas of science.
If determined to be feasible, the development of a centralized Registry, within which IC and outside organizations could deposit secure and sharable data, would allow more rapid translation of basic science information into clinically useful knowledge, greatly improved data quality, and enhanced engagement of patient advocacy groups in support of research at minimal cost. The centralized process would facilitate communication efforts to ensure broad awareness of the resource.
Burton, P. R., Hanell, A. L., Fortier, I., Manolio, T. A., Khoury, M. J., Little, J. & Elliott, P. (2009). Size matters: just how big is BIG? Quantifying realistic sample size requirements for human genome epidemiology. International Journal of Epidemiology, 38(1) 263-273.
Collins, F. S. (2004). The case for a US prospective cohort study of genes and environment. Nature, 429, 475-477.
Collins, F. S. & Manolio, T. A. (2007). Necessary but not sufficient. Nature, 445, 259.
Denny, J. C., Ritchie, M. D., Basford, M. A. Pulle, J. M. Bastarache, L., and others. (2010). PheWAS: demonstrating the feasibility of phenome-wide scan to discover gene-disease associations. Bioinformatics, 26(9), 1205-1210.
Doplet B. C., Johnson K. B. (2008). Categorizing the world of registries. Journal of Biomedical Informatics 411009–1020.
Forrest, CB, Bartek, RJ, Rubinstein Y, and Groft, SC; The Case for a Global Rare Diseases Registry The Lancet, 377, 1057-1059.
Manolio, T. A., Bailery-Wilson, J. E., & Collins, F. S. (2006). Genes, environment and the value of prospective cohort studies. Nature Reviews Genetics, 7, 812-820.
Manolio, T. A. (2010). Genomewide association studies and assessment of the risk of disease. NEJM, 363, 166-176.
Science Staff. (2011). Challenges and Opportunities. Science, 331, 692-693.
Shriner, D., Adeyemo, A., Ramos, E., Chen, G., & Rotimi, C. N. (2011). Mapping of disease-associated variants in admixed populations. Genome Biology, 12, 223-231.
Willett, W. C., Blot, W. J., Colditz, G., A., Folsom, A. R., Henderson, B. E. & Stampfer, M. J. (2007). Not worth the wait. Nature, 445, 257-258.
Wijsman, E.M. Pankratz, N.D., Choi, Y., Rothstein, J. H., Faber, K. M. and others (2011). Genome-wide association of familial late-onset Alzheimer’s disease replicates BIN1 and CLU and nominates CUGBP2 in interaction with APOE. PLoS Genetics, 7(2), e1001308.
Rubenstein, Y. R., Groft, S, C., Bartek, R., Brown, K., Christensen, and others. Creating a global rare disease patient registry linked to a rare diseases biorepository database: Rare Disease-HUB (RD-HUB), Contemporary Clinical Trials, 31(5), 394-404
Tags: human, cohort, genetics/genomics, data integration, data sharing, environmental
Title of proposed idea: Innovative Mobile and Wireless Technologies (mHealth) to Improve Health Research and Health Outcomes
Major obstacle/challenge to overcome:
Mobile and wireless health (mHealth) technologies have developed at an exponential pace in recent years; however, the integration and translation of these cutting-edge technologies into rigorously evaluated health research and healthcare tools have lagged behind. For example, low-cost, real-time devices to assess disease, movement, images, behavior, social interactions, environmental toxins, hormones, and other physiological variables, have made remarkable advances in the last decade because of increased computational sophistication, as well as reductions in size and power requirements. The basic engineering and computer science knowledge exists to develop technologies that will alter the collection of health-related data for basic and translational research, clinical practice, healthcare delivery, and public health in ways that were not imaginable a decade ago. Scientific investments are needed to translate this basic science into quality mobile and wireless health technologies that also leverage other rapidly advancing biomedical technologies.
In fact, development of the mobile and wireless health technologies is currently progressing at a much faster pace than the science to evaluate their validity and efficacy. Unnecessary devices will be created with little medical impact because they were developed without an empirical foundation and input from the health research community. Private sector technology companies, along with a limited amount of public funding from NSF and NIBIB, support the basic development of novel wireless and mobile technologies, but NIH provides limited support toward the translation of these basic technologies into quality wireless and mobile solutions to facilitate research and improve health. Once a technology is fully developed, various NIH institutes support rigorous evaluation, but there is insufficient funding for the period between basic technology development and evaluation; that is, the development, integration and validation of software and hardware required to develop these cutting-edge technologies into evaluable tools.
Moreover, these technologies, which promise to sense and assess physiology, disease, behavior, and environmental changes continuously and in real-time, will generate an avalanche of multi-faceted, longitudinal data. The rich longitudinal datasets generated by these multiple inputs also require advanced analytics, akin to a high throughput approach to a continuous stream of data. These analytic tools and sophisticated visualization techniques will provide interpretable data for researchers and/or actionable data for healthcare providers and public health practitioners, as well as new approaches to efficient management of chronic disease.
Emerging scientific opportunity ripe for Common Fund investment:
Mobile and wireless health (mHealth) is a nascent and rapidly growing field. These technologies provide the potential to advance research, prevent disease, enhance diagnostics, improve treatment, reduce disparities, increase access to health services and lower healthcare costs in ways previously unimaginable. Real-time, continuous biological, behavioral and environmental data collected by wireless and mobile technologies will improve our understanding of the etiology of health and disease, particularly when integrated with data from areas such as genomics, biomarkers, and electronic medical records. These data are also essential for answering the difficult questions of gene-environment interplay in health and disease, adherence, and the developmental origins of adult disease, as well as informing the development of treatments and prevention programs that are preemptive, personalized and adaptive over time. Further, these tools have the potential to transform clinical trials. Remote monitoring and sensing can allow researchers to recruit and follow patients without the need or cost to transport them to a research or healthcare setting. This will increase participant access and decrease burden, while increasing sample representativeness and the quantity and quality of follow-up data, all at decreased cost.
A major opportunity also arises from the potential of mobile and wireless health technologies to continuously monitor chronic medical conditions around the world, as well as to implement disease management plans that capitalize on this expanded information. Chronic disease conditions have been recognized in the developed world as a major source of morbidity and mortality. Similarly, in the low- and middle-income countries (LMICs), chronic disease is increasingly being cited as an emerging problem and a major component of disease burden. A prospective in the NEJM (2007;356:209-211) cites that cardiovascular disease accounts for nearly 30% of all deaths worldwide and this percentage is similar in LMICs to the global average. A fundamental characteristic of most chronic disease is that the medical profession manages the disease rather than ‘cures‘ it. The hypothesis that better monitoring will lead to better management, better outcomes and reduced disease burden has yet to be tested.
The need for rigorous research that examines the potential, as well as the challenges, of harnessing mobile technologies to improve health outcomes is critical to global health. Given the high penetration of cell phones and related technologies in LMICs, as well as the lack of bandwidth in many parts of these countries, research investments could illuminate the potential of these technologies to serve as the underlying infrastructure for transmission of health information and data in low-resource settings. For example, given the capacity for adaptive learning facilitated by these technologies and the potentially heightened level of empowerment experienced by users, research investments can inform how best to use mobile technologies to help educate and train the next generation of providers and patients in low-resource settings, as well as serve as a vehicle for behavior change across diseases and conditions. Equally exciting is the potential for these technologies to provide low-cost alternatives to traditional imaging modalities for screening of chronic, non-communicable diseases, such as cancer and heart disease. Therefore, in addition to the ways in which mobile and wireless technologies support research and health in the United States, numerous specific areas of global health research could benefit from increased and targeted NIH investment in this field.
To ensure long-term impact of investments in mobile and wireless technologies and to improve health globally as well as domestically, NIH funding should be designed to ensure that both the technology developers and the researchers start with problems that demand solving, so that the field is needs-driven, rather than product-driven. In addition, mobile and wireless technologies are part of an information and healthcare ecosystem in which systems must be able to communicate with each other; therefore, NIH can provide leadership to encourage and support the development of novel, interoperable solutions. Furthermore, significant support for building research capacity in this field will help to ensure a pipeline of investigators both in the U.S. and abroad who have the skills and experience to advance the field forward as technologies and public health needs evolve.
Computer scientists, engineers, and biomedical/behavioral researchers are beginning to collaborate, and transdisciplinary groups are forming, making this area ripe for Common Fund investment now. In addition, the wide interest in this area provides an opportunity for potential federal (National Science Foundation, World Bank) and non-federal collaborations (e.g., Robert Wood Johnson Foundation, private technology and communication companies) that could augment Common Fund resources and increase the value of the initiative. In addition, NIH has an mHealth Scientific Interest Group that will ensure programmatic expertise across the Institutes and Centers.
With its potential for providing low-cost, high quality data to enhance health research and improve health outcomes around the world, mobile/wireless health is of growing interest to the NIH ICs, but no individual IC is able to foster the integrated development needed to move basic wireless/mobile technological development to evaluable solutions, especially since most of these technologies apply to multiple diseases and conditions. This initiative provides the funding to develop and translate novel technologies from prototype components to integrated and validated tools to advance health research, diagnose and treat disease and promote health.
Common Fund investment in this area would stimulate the required interdisciplinary efforts among computer scientists, engineers, and biomedical, behavioral, and social scientists to fill this translation, development, integration and validation gap. Funding would target four essential aims:
1. Translation, development, integration of interoperable and affordable mobile and wireless technology into novel scientifically-validated tools for use in research, healthcare or public health;
2. Validation and implementation of existing wireless and mobile devices into ongoing clinical trials, especially those addressing treatment of chronic disease; and,
3. Development of “high throughput” analytic techniques for complex, comprehensive, and multi-streamed data, as well as models of and data visualization to enhance the value of these data.
4. Development of mobile health technologies that can address infectious and noncommunicable disease problems (obesity, cancer, diabetes, cardiac disease, etc.) around the world by facilitating disease prevention and behavior change.
Potential applicants would include technology developers, industry partners, health researchers and others in an iterative development process for which there is currently no model of public funding. Currently, mobile and wireless health research requires multiple grants targeting each step of development, causing delays and preventing research from keeping pace with technological change. Partnerships with industry and other stakeholders will facilitate commercialization and sustained development. Further, to address global health challenges and to facilitate the exchange of information, collaborations between U.S. investigators and partners in low-resource settings (both globally and in the U.S.) would be encouraged. This initiative would also develop a cadre of reviewers with experience evaluating grant applications that involve a combination of technical development aims and health outcome aims. By providing models for how to move these basic technologies through integrated development and rigorous outcome evaluation, this effort could eventually be subsumed by technology companies and basic technology funders extending their reach into integrative development and by having NIH and other clinical research funders expand their interests into the integrated development required to prepare mobile and wireless applications for clinical evaluation.
Potential impact of Common Fund investment:
One impact of Common Fund investment of mobile and wireless technologies would be to move this field from developing devices to developing solutions for chronic disease management or other conditions. One recent example of the potential (Lancet, 2011; 377:658-666) demonstrated that wireless pulmonary artery monitoring of individuals with chronic heart failure resulted in a 40% reduction in heart-failure related hospitalization over the six month follow-up period. The infrastructure developed could also have a significant impact on research on disease monitoring, treatment , and management.
Further, if this Common Fund program achieves its objectives, scientific and business models will be created for moving cutting-edge technologies much more quickly through integrated development to research evaluation. Currently, many of the wireless and mobile technologies being evaluated with NIH support are considered old, if not obsolete, by the technology community. As a result, the wireless and mobile technologies evaluated with NIH funding will be much more innovative and novel and the pipeline from basic technological development to health research evaluation will be accelerated and streamlined. In addition, this initiative will support the development of the methods needed to analyze and present these complex data sets to enhance both health research, but also healthcare delivery and public health as these large, complex data streams are summarized into actionable health information.
Tags: validation, efficacy, technology, wireless, mobile, healthcare, translational, global, behavior, environmental, computational
Title of proposed idea: Developmental Origins of Health and Disease: Disease Prevention Across Generations
Major obstacle/challenge to overcome: It is clear that many complex diseases and conditions result from a combination of genetics and environment. What is not clear is when and how this interaction of genetics and environment actually leads to disease. The concept of developmental origins of health and disease (DOHaD) is a fundamental principle underlying many chronic diseases and conditions in children and adults. Decades of DOHaD studies suggest that a wide variety of early exposures occurring during periods of time where tissues and organ systems are developing markedly increase risk for (or even cause) disease across the life course. These “environmental” exposures are varied and include drugs, nutrition, chemicals, stress, microbes or viral infections. Examples of these non-communicable diseases and disorders (NCDs) include obesity, type II diabetes, insulin resistance, asthma, cardiovascular diseases, dyslipidemia, cognitive and behavioral disorders, neurodegenerative diseases, a variety of cancers, and reproductive disorders. Disadvantaged populations may experience greater exposure to these hazards and exhibit higher rates of disease incidence, morbidity and mortality. Understanding and modulating this risk in humans during critical windows of development offers the promise of primary prevention for many of these NCDs and may result in reducing health disparities.
Although the range of diseases and conditions believed to result (at least in part) from early life exposures spans nearly all of the NIH Institutes and Centers (ICs), the concept of developmental origins has yet to be broadly adopted as a new research paradigm. A trans-NIH program funded by the Common Fund (CF) will support research to 1) characterize early life exposures and their health effects in a comprehensive way, 2) encourage cellular and molecular research on the mechanisms of these triggers (or stressors) on development, and 3) encourage the development of targeted interventional studies in human subjects. The initiative would result in a new awareness among researchers funded across the NIH to consider the role of developmental stressors in triggering the diseases and conditions they study. This would also jumpstart the field of transgenerational inheritance, i.e., the transmission of environmentally-induced phenotypes to subsequent generations without direct exposure. This phenomenon is well-described in non-mammalian systems, but despite the existence of several published examples, it remains highly controversial whether it truly occurs in humans or rodents, or how common it might be. Finally, the initiative would build the body of science needed to strengthen prevention research, an important element of health care reform.
Emerging scientific opportunity ripe for Common Fund investment: No single IC has the capability or the expertise to integrate all the approaches and technologies needed to assess how genetics and multiple environmental triggers (or stressors) combine to affect health across the lifespan and even across generations. Furthermore, early life exposures even to a single stressor can lead to adverse health outcomes affecting multiple tissues and organ systems that are not readily appreciated in traditional single institute research programs. For these reasons, adopting a coordinated, trans-NIH approach is a critical step in changing existing paradigms about the etiology of a variety of diseases and conditions, and transforming this information into knowledge that targets the most advantageous times, and possible interventions, to prevent their occurrence. This program would also leverage and integrate current CF and trans-NIH initiatives such as those on epigenetics, exposure biology, genetics/genomics, the microbiome, bioinformatics, developmental biology and programming, stem cell development and differentiation, and animal, epidemiologic and clinical assessment of disease.
Common Fund investment that could accelerate scientific progress in this field: We propose a CF program that will transform thinking about disease prevention: a comprehensive investigation of the developmental basis of a wide range of diseases and conditions. Such a program would enable the NIH to identify a host of environmental stressors that increase disease risk, and the mechanisms by which these exposures alter normal developmental programs, manifesting in disease or conditions, years and even generations later. Moreover, a comprehensive research program focused on the developmental origins of disease would enable scientists to pinpoint susceptibility windows – unique developmental time points at which humans are most susceptible to the combined effects of environmental exposures and genetic factors. Identifying these developmental windows and developing predictive biomarkers of exposure will dramatically increase our ability not only to understand disease etiology, but also to develop intervention strategies that will ultimately prevent disease, by reducing exposure. Possible investments include:
1. Develop centralized, well-characterized, novel models and clinical research designs and analytic techniques that would promote effective multigenerational analysis and would leverage existing CF investments and infrastructure, such as that in mouse phenotyping (KOMP2).
2. Identify stressors (nutritional, environmental, social) and investigate early gene-environment interactions that may perturb the normative development of various tissues and organ systems (such as the cardiovascular, neurological, immune, gastrointestinal, skeletal, endocrine, reproductive systems), increasing the risk of disease or conditions later in life and across generations. Particular focus would be placed on diseases, conditions or syndromes that have been steadily increasing in incidence and where health disparities are apparent in the United States. A focus on exposure characterization during early life would be imperative and would include vulnerable populations defined by race, ethnicity, and socioeconomic status.
3. Apply state-of-the-art sequencing technologies to investigate epigenetic and genetic mechanisms by which early life events lead to developmental reprogramming, impacting disease risk both in both children and adults (e.g., somatic or cognitive changes) long after the stressor is gone, and how increased risk is transmitted to subsequent generations through various mechanisms (i.e., germline, mitochondrial, or other changes).
4. Use birth cohorts in human subjects, to identify sex-specific developmental susceptibility windows that are specific to common diseases and conditions in early development.
5. Identify biomarkers of developmental stress for single exposures and combinations that predict susceptibility to specific diseases and conditions later in life that could also be used to target and develop preventive interventions.
6. Develop bioinformatics and statistical programs to allow the assessment and integration of developmental exposure to a variety of stresses and their importance in the development of disease outcomes.
Potential impact of Common Fund investment: This would be the first CF program specifically designed to focus on disease prevention, and it will do it in an integrated and transformative manner. Once it is clear how certain diseases or conditions originate in early development – including which stressors or combinations of stressors are responsible for the altered programming, and during which developmental stages humans are most sensitive to these effects – effective strategies can be developed both to reduce exposure to the stressors and or intervene to reduce disease incidence. Such strategies have the potential not only to reduce the overall societal burden of disease but to reduce or eliminate health disparities.
Tags: new tools, computational, prevention, intervention, developmental, genetics/genomics, biomarker, disease, environmental
Title of proposed idea: Group Effects
Nominator: Innovation Brainstorm participants
Major obstacle/challenge to overcome: Exposures are highly variable and dynamic throughout the lifetime of an individual. Needed are systematic, unbiased screens for studying how multiple factors (e.g. microbiological, chemical, lifestyle and dietary exposures) interact to contribute to susceptibility to disease, disease progression, and treatment outcomes. In addition to curating/annotating data obtained using current models, improved testing systems are needed that are equipped to analyze multi-factorial issues.
Emerging scientific opportunity ripe for Common Fund investment: Several opportunities exist to address the need for better models and analytic tools. These include the availability of inexpensive exposure screening tools (e.g. virochip, protein adducts) and bioinformatic techniques that can handle large, clinical datasets to track exposures. The development of screening tools, methods, and model systems that are particularly well suited for studying mechanisms of environmental influence also provide opportunities in this area. Point-of-care tools are likely to be especially useful to monitor exposure in global and other low-resource settings. Expanding these toward multiplex capability is another opportunity.
Common Fund investment that could accelerate scientific progress in this field: The Common Fund (CF) could shift the curve to accelerate progress by expanding the number and quality of tools to systematically measure multiple exposures and by supporting the development of computational tools that will support multifactorial research: viral, bacterial, chemical, and dietary. Data handling for these types of studies is an enormous challenge. A database that catalogs and characterizes model systems that are suitable for studying multifactorial research would also be helpful.
Potential impact of Common Fund investment: Implementing projects in this area could have significant impact in helping to better clarify the age-old question of the relative influences of “nature and nurture;” yet, it would go further by ultimately explaining how complex mixtures of genetic loci and environmental exposures influence health and disease susceptibility. In time, these insights will point to preventive strategies that help to fulfill the goals of personalized medicine.
Tags: new tools, computational, model organism, drug screening, predictive model, environmental
Title of proposed idea: Beyond GWAS (genome wide association studies)
Major obstacle/challenge to overcome: Although GWAS (genome wide association studies) have uncovered many genetic loci for a range of conditions and diseases, a major challenge is translating this knowledge into functional insights. One key roadblock is the inability to capture precisely various and diverse environmental measurements. Incomplete, nonstandardized, and shallow collection of phenotype data contributes to the difficulty of using GWAS data to define mechanisms and/or suggest potential interventions. Insufficient sample sizes prohibit the clarification of the role and relevance of complex traits in health and disease. In some cases, valuable opportunities may be missed, as in harnessing genotyping data from randomized clinical trials that have rich phenotypic data. For the massive amounts of data that already exist, practical and effective strategies for integration lag behind. Possible remedies include new algorithms for performing higher-order ‘omics studies, a repository of rare knockouts, and more complete sharing of data and biospecimens.
Fig. 1. Scientific progress over time
Emerging scientific opportunity ripe for Common Fund investment: Further progress in GWAS requires both persistence and innovation. While GWAS execution is routine and fairly well-established (in the “D” portion of the graph below), others are in a period of rapid growth [in the “C” region of the curve: single trait analysis, expression quantitative trait loci (eQTLs)], and still others require a substantial push to reach their potential (in the “A” and “B” areas below: functional annotation of genetic variants, annotation of a reference genome (ENCODE, the ENCyclopedia Of DNA Elements), whole-genome analyses in unrelateds/families, large-scale phenotyping, and clinical translation). This last group is likely to be the most ripe for Common Fund (CF) investment.
Common Fund investment that could accelerate scientific progress in this field: Three proposed projects (each independent but complementary and potentially synergistic) could overcome some of the current roadblocks in this area.
Potential impact of Common Fund investment: Moving GWAS beyond its current capability offers faster movement from association to function, which will likely accelerate discovery for multiple traits. Clinical relevance of most GWAS to date is lacking: The proposed projects aim to lead to better clinical decision support, new diagnostics and therapeutics, improved coordination with industry, as well as the realization of meaningful use criteria of the HITECH act (The Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009, was signed into law on February 17, 2009, to promote the adoption and meaningful use of health information technology).
Tags: genetics/genomics, functional analysis, clinical, cohort, environmental