Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies

Recommendations and proposed guidelines for assessing the cumulative evidence on joint effects of genes and environments on cancer occurrence in humans

We propose guidelines to evaluate the cumulative evidence of gene-environment (G × E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G × E interactions, discovery and modelling methods for G × E interactions, and issues in systematic reviews of evidence for G × E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.

Knowledge engineering for health: a new discipline required to bridge the "ICT gap" between research and healthcare

Despite vast amount of money and research being channeled toward biomedical research, relatively little impact has been made on routine clinical practice. At the heart of this failure is the information and communication technology "chasm" that exists between research and healthcare. A new focus on "knowledge engineering for health" is needed to facilitate knowledge transmission across the research-healthcare gap. This discipline is required to engineer the bidirectional flow of data: processing research data and knowledge to identify clinically relevant advances and delivering these into healthcare use; conversely, making outcomes from the practice of medicine suitably available for use by the research community. This system will be able to self-optimize in that outcomes for patients treated by decisions that were based on the latest research knowledge will be fed back to the research world. A series of meetings, culminating in the "I-Health 2011" workshop, have brought together interdisciplinary experts to map the challenges and requirements for such a system. Here, we describe the main conclusions from these meetings. An "I4Health" interdisciplinary network of experts now exists to promote the key aims and objectives, namely "integrating and interpreting information for individualized healthcare," by developing the "knowledge engineering for health" domain.

Using PhenX measures to identify opportunities for cross-study analysis

The PhenX Toolkit provides researchers with recommended, well-established, low-burden measures suitable for human subject research. The database of Genotypes and Phenotypes (dbGaP) is the data repository for a variety of studies funded by the National Institutes of Health, including genome-wide association studies. The dbGaP requires that investigators provide a data dictionary of study variables as part of the data submission process. Thus, dbGaP is a unique resource that can help investigators identify studies that share the same or similar variables. As a proof of concept, variables from 16 studies deposited in dbGaP were mapped to PhenX measures. Soon, investigators will be able to search dbGaP using PhenX variable identifiers and find comparable and related variables in these 16 studies. To enhance effective data exchange, PhenX measures, protocols, and variables were modeled in Logical Observation Identifiers Names and Codes (LOINC® ). PhenX domains and measures are also represented in the Cancer Data Standards Registry and Repository (caDSR). Associating PhenX measures with existing standards (LOINC® and caDSR) and mapping to dbGaP study variables extends the utility of these measures by revealing new opportunities for cross-study analysis.

Age and gender differences in physical capability levels from mid-life onwards: the harmonisation and meta-analysis of data from eight UK cohort studies

Using data from eight UK cohorts participating in the Healthy Ageing across the Life Course (HALCyon) research programme, with ages at physical capability assessment ranging from 50 to 90+ years, we harmonised data on objective measures of physical capability (i.e. grip strength, chair rising ability, walking speed, timed get up and go, and standing balance performance) and investigated the cross-sectional age and gender differences in these measures. Levels of physical capability were generally lower in study participants of older ages, and men performed better than women (for example, results from meta-analyses (N = 14,213 (5 studies)), found that men had 12.62 kg (11.34, 13.90) higher grip strength than women after adjustment for age and body size), although for walking speed, this gender difference was attenuated after adjustment for body size. There was also evidence that the gender difference in grip strength diminished with increasing age,whereas the gender difference in walking speed widened (p<0.01 for interactions between age and gender in both cases). This study highlights not only the presence of age and gender differences in objective measures of physical capability but provides a demonstration that harmonisation of data from several large cohort studies is possible. These harmonised data are now being used within HALCyon to understand the lifetime social and biological determinants of physical capability and its changes with age.

The NIDDK Central Repository at 8 years--ambition, revision, use and impact

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository makes data and biospecimens from NIDDK-funded research available to the broader scientific community. It thereby facilitates: the testing of new hypotheses without new data or biospecimen collection; pooling data across several studies to increase statistical power; and informative genetic analyses using the Repository's well-curated phenotypic data. This article describes the initial database plan for the Repository and its revision using a simpler model. Among the lessons learned were the trade-offs between the complexity of a database design and the costs in time and money of implementation; the importance of integrating consent documents into the basic design; the crucial need for linkage files that associate biospecimen IDs with the masked subject IDs used in deposited data sets; and the importance of standardized procedures to test the integrity data sets prior to distribution. The Repository is currently tracking 111 ongoing NIDDK-funded studies many of which include genotype data, and it houses over 5 million biospecimens of more than 25 types including serum, plasma, stool, urine, DNA, red blood cells, buffy coat and tissue. Repository resources have supported a range of biochemical, clinical, statistical and genetic research (188 external requests for clinical data and 31 for biospecimens have been approved or are pending). Genetic research has included GWAS, validation studies, development of methods to improve statistical power of GWAS and testing of new statistical methods for genetic research. We anticipate that the future impact of the Repository's resources on biomedical research will be enhanced by (i) cross-listing of Repository biospecimens in additional searchable databases and biobank catalogs; (ii) ongoing deployment of new applications for querying the contents of the Repository; and (iii) increased harmonization of procedures, data collection strategies, questionnaires etc. across both research studies and within the vocabularies used by different repositories.

China Kadoorie Biobank of 0.5 million people: survey methods, baseline characteristics and long-term follow-up

BACKGROUND

Large blood-based prospective studies can provide reliable assessment of the complex interplay of lifestyle, environmental and genetic factors as determinants of chronic disease.

METHODS

The baseline survey of the China Kadoorie Biobank took place during 2004-08 in 10 geographically defined regions, with collection of questionnaire data, physical measurements and blood samples. Subsequently, a re-survey of 25,000 randomly selected participants was done (80% responded) using the same methods as in the baseline. All participants are being followed for cause-specific mortality and morbidity, and for any hospital admission through linkages with registries and health insurance (HI) databases.

RESULTS

Overall, 512,891 adults aged 30-79 years were recruited, including 41% men, 56% from rural areas and mean age was 52 years. The prevalence of ever-regular smoking was 74% in men and 3% in women. The mean blood pressure was 132/79 mmHg in men and 130/77 mmHg in women. The mean body mass index (BMI) was 23.4 kg/m(2) in men and 23.8 kg/m(2) in women, with only 4% being obese (>30 kg/m(2)), and 3.2% being diabetic. Blood collection was successful in 99.98% and the mean delay from sample collection to processing was 10.6 h. For each of the main baseline variables, there is good reproducibility but large heterogeneity by age, sex and study area. By 1 January 2011, over 10,000 deaths had been recorded, with 91% of surviving participants already linked to HI databases.

CONCLUSION

This established large biobank will be a rich and powerful resource for investigating genetic and non-genetic causes of many common chronic diseases in the Chinese population.

Realizing the promise of population biobanks: a new model for translation

The promise of science lies in expectations of its benefits to societies and is matched by expectations of the realisation of the significant public investment in that science. In this paper, we undertake a methodological analysis of the science of biobanking and a sociological analysis of translational research in relation to biobanking. Part of global and local endeavours to translate raw biomedical evidence into practice, biobanks aim to provide a platform for generating new scientific knowledge to inform development of new policies, systems and interventions to enhance the public's health. Effectively translating scientific knowledge into routine practice, however, involves more than good science. Although biobanks undoubtedly provide a fundamental resource for both clinical and public health practice, their potentiating ontology--that their outputs are perpetually a promise of scientific knowledge generation--renders translation rather less straightforward than drug discovery and treatment implementation. Biobanking science, therefore, provides a perfect counterpoint against which to test the bounds of translational research. We argue that translational research is a contextual and cumulative process: one that is necessarily dynamic and interactive and involves multiple actors. We propose a new multidimensional model of translational research which enables us to imagine a new paradigm: one that takes us from bench to bedside to backyard and beyond, that is, attentive to the social and political context of translational science, and is cognisant of all the players in that process be they researchers, health professionals, policy makers, industry representatives, members of the public or research participants, amongst others.

Biobanking residual tissues

Health-care research relies largely on human materials stored in highly specialised biorepositories. Medical translational research on tissues can be performed using a variety of resources in distinct situations. The best known is the secondary use of pathology archives where paraffin-embedded tissues are stored for diagnostic reasons. Another is collecting and storing frozen material obtained from leftover surgical diagnosis. Such residual tissues can either be used directly in research projects or used in the context of a clinical trial with new interventional medicinal products. The latter can make the regulations governing the use of these materials for medical research much more complicated. The use of residual materials is very distinct from biobanking projects for which tissue is specifically collected. This article describes the consequences of using residual human material from different sources in distinct situations and why signed informed consent is not always the preferred choice of individual countries regarding the use of residual material. In addition, signed informed consent is overdone when using residual tissues in medical research. We maintain that the opt-out system is a balanced choice if certain requirements are met, relating to sufficient transparency about using residual tissue for research, the purpose of such research and to the confidentiality of the data used in that research. Finally, the international exchange of samples can be based on the laws and regulations of the countries of origin. Respecting these form the basis of what can and cannot be done in the country where the research on the samples is being performed.

The PhenX Toolkit: get the most from your measures

The potential for genome-wide association studies to relate phenotypes to specific genetic variation is greatly increased when data can be combined or compared across multiple studies. To facilitate replication and validation across studies, RTI International (Research Triangle Park, North Carolina) and the National Human Genome Research Institute (Bethesda, Maryland) are collaborating on the consensus measures for Phenotypes and eXposures (PhenX) project. The goal of PhenX is to identify 15 high-priority, well-established, and broadly applicable measures for each of 21 research domains. PhenX measures are selected by working groups of domain experts using a consensus process that includes input from the scientific community. The selected measures are then made freely available to the scientific community via the PhenX Toolkit. Thus, the PhenX Toolkit provides the research community with a core set of high-quality, well-established, low-burden measures intended for use in large-scale genomic studies. PhenX measures will have the most impact when included at the experimental design stage. The PhenX Toolkit also includes links to standards and resources in an effort to facilitate data harmonization to legacy data. Broad acceptance and use of PhenX measures will promote cross-study comparisons to increase statistical power for identifying and replicating variants associated with complex diseases and with gene-gene and gene-environment interactions.

Invited commentary: consolidating data harmonization--how to obtain quality and applicability?

It is recognized that very large sample sizes capable of providing adequate statistical power are required to properly investigate and understand the role and interaction of genetic, lifestyle, and environmental factors in modulating the risk and progression of chronic diseases. However, very few one-off studies provide access to very large numbers of participants, and the collection of high-quality data necessitates a major investment of resources. The scientific community is thus increasingly engaged in collaborative efforts to facilitate harmonization and synthesis of data across studies. Complementary harmonization approaches may be adopted to support these efforts. In the current issue of the American Journal of Epidemiology, Hamilton et al. (Am J Epidemiol. 2011;174(3):253-260) present the consensus measures for Phenotypes and eXposures (PhenX) Toolkit, which promotes the use of identical data collection tools and procedures to support harmonization across emerging studies. Data synthesis is greatly facilitated by the use of common measures and procedures. However, the "stringent" criteria required by PhenX can limit its utilization. The opportunity to make use of rigorous but more "flexible" harmonization approaches should also be considered.

Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies

BACKGROUND

Proper understanding of the roles of, and interactions between genetic, lifestyle, environmental and psycho-social factors in determining the risk of development and/or progression of chronic diseases requires access to very large high-quality databases. Because of the financial, technical and time burdens related to developing and maintaining very large studies, the scientific community is increasingly synthesizing data from multiple studies to construct large databases. However, the data items collected by individual studies must be inferentially equivalent to be meaningfully synthesized. The DataSchema and Harmonization Platform for Epidemiological Research (DataSHaPER; http://www.datashaper.org) was developed to enable the rigorous assessment of the inferential equivalence, i.e. the potential for harmonization, of selected information from individual studies.

METHODS

This article examines the value of using the DataSHaPER for retrospective harmonization of established studies. Using the DataSHaPER approach, the potential to generate 148 harmonized variables from the questionnaires and physical measures collected in 53 large population-based studies (6.9 million participants) was assessed. Variable and study characteristics that might influence the potential for data synthesis were also explored.

RESULTS

Out of all assessment items evaluated (148 variables for each of the 53 studies), 38% could be harmonized. Certain characteristics of variables (i.e. relative importance, individual targeted, reference period) and of studies (i.e. observational units, data collection start date and mode of questionnaire administration) were associated with the potential for harmonization. For example, for variables deemed to be essential, 62% of assessment items paired could be harmonized.

CONCLUSION

The current article shows that the DataSHaPER provides an effective and flexible approach for the retrospective harmonization of information across studies. To implement data synthesis, some additional scientific, ethico-legal and technical considerations must be addressed. The success of the DataSHaPER as a harmonization approach will depend on its continuing development and on the rigour and extent of its use. The DataSHaPER has the potential to take us closer to a truly collaborative epidemiology and offers the promise of enhanced research potential generated through synthesized databases.

Coordinating centers in cancer epidemiology research: the Asia Cohort Consortium coordinating center

Although it is tacitly recognized that a good coordinating center (CC) is essential to the success of any multisite collaborative project, very little study has been done on what makes a CC successful, why some CCs fail, or how to build a CC that meets the needs of a given project. Moreover, very little published guidance is available, as few CCs outside the clinical trial realm write about their work. The Asia Cohort Consortium (ACC) is a collaborative cancer epidemiology research project that has made strong scientific and organizational progress over the past 3 years by focusing its CC on the following activities: collaboration development; operations management; statistical and data management; and communications infrastructure and tool development. Our hope is that, by sharing our experience building the ACC CC, we can begin a conversation about what it means to run a CC for multi-institutional collaboration in cancer epidemiology, help other collaborative projects solve some of the issues associated with collaborative research, and learn from others.

Human lymphoblastoid cell lines: a goldmine for the biobankomics era

Biobanking became a necessity for translating genetic discoveries into clinical practice. Approaches to personalized medicine require a new model system for functional and pharmacogenomic studies of a variety of accumulating genetic variations, as well as new research environments such as biobankomics. Human lymphoblastoid cell lines (LCLs) will provide a valuable tool to meet such new demands in the biobankomics era. The National Biobank of Korea (NBK), which is leading the Korea Biobank Project, has a large collection of LCLs derived mostly from population-based cohort samples. Using a special long-term subculture collection of NBK LCLs, biological characteristics of early passage LCLs and terminally immortalized LCLs have been investigated to promote the utilization of LCLs and provide well quality-controlled LCLs for genetic and pharmacogenomic studies. As LCLs have been successfully phenotyped for cytotoxicity in response to various stimulators, including chemotherapeutic agents, environmental chemicals and irradiation, the utility of LCLs will increase in the future. Here, we discuss current and future applications of NBK LCLs for the biobankomics era.

SAIL--a software system for sample and phenotype availability across biobanks and cohorts

Summary: The Sample avAILability system—SAIL—is a web based application for searching, browsing and annotating biological sample collections or biobank entries. By providing individual-level information on the availability of specific data types (phenotypes, genetic or genomic data) and samples within a collection, rather than the actual measurement data, resource integration can be facilitated. A flexible data structure enables the collection owners to provide descriptive information on their samples using existing or custom vocabularies. Users can query for the available samples by various parameters combining them via logical expressions. The system can be scaled to hold data from millions of samples with thousands of variables.

Availability: SAIL is available under Aferro-GPL open source license: https://github.com/sail.

Contact:gostev@ebi.ac.uk, support@simbioms.org

Supplementary information: Supplementary data are available at Bioinformatics online and from http://www.simbioms.org.

The human gutome: nutrigenomics of the host-microbiome interactions

Demonstrating the importance of the gut microbiota in human health and well-being represents a major transformational task in both medical and nutritional research. Owing to the high-throughput -omics methodologies, the complexity, evolution with age, and individual nature of the gut microflora have been more thoroughly investigated. The balance between this complex community of gut bacteria, food nutrients, and intestinal genomic and physiological milieu is increasingly recognized as a major contributor to human health and disease. This article discusses the "gutome," that is, nutritional systems biology of gut microbiome and host-microbiome interactions. We examine the novel ways in which the study of the human gutome, and nutrigenomics more generally, can have translational and transformational impacts in 21st century practice of biomedicine. We describe the clinical context in which experimental methodologies, as well as data-driven and process-driven approaches are being utilized in nutrigenomics and microbiome research. We underscore the pivotal importance of the gutome as a common platform for sharing data in the emerging field of the integrated metagenomics of gut pathophysiology. This vision needs to be articulated in a manner that recognizes both the omics biotechnology nuances and the ways in which nutrigenomics science can effectively inform population health and public policy, and vice versa.

Current standards for the storage of human samples in biobanks

Biobanks are diverse in their design and purpose; the idea of fully harmonizing historical and future biobanks is unaffordable and unfeasible. Biobanks should focus their efforts instead on developing and maintaining high-quality collections of samples capable of providing a wide range of biological information using processes that minimize introduced variability. A full data audit trail on sample processing, archiving, and quality control procedures should also be provided. This should enable the data derived from biobanks to contribute as part of wider collaborative efforts with other similar resources.

Stem cell banking: between traceability and identifiability

Stem cell banks are increasingly seen as an essential resource of biological materials for both basic and translational research. Stem cell banks support transnational access to quality-controlled and ethically sourced stem cell lines from different origins and of varying grades. According to the Organisation for Economic Co-operation and Development, advances in regenerative medicine are leading to the development of a bioeconomy, 'a world where biotechnology contributes to a significant share of economic output'. Consequently, stem cell banks are destined to constitute a pillar of the bioeconomy in many countries. While certain ethical and legal concerns are specific to the nature of stem cells, stem cell banking could do well to examine the approaches fostered by tissue banking generally. Indeed, the past decade has seen a move to simplify and harmonize biological tissue and data banking so as to foster international interoperability. In particular, the issues of consent and of traceability illustrate not only commonalities but the opportunity for stem cell banking to appreciate the lessons learned in biobanking generally. This paper analyzes convergence and divergence in issues surrounding policy harmonization, transnational sharing, informed consent, traceability and return of results in the context of stem cell banks.