The Emergence of Networks in Human Genome Epidemiology: Challenges and Opportunities


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Excerpt

Large-scale “big science” is advocated as an approach to complex research problems in many scientific areas.1 Epidemiologists have long recognized the value of large collaborative studies to address important questions that are beyond the scope of a study conducted at a single institution.2 We define networks (or, interchangeably, consortia) as groups of scientists from multiple institutions who cooperate in research efforts involving, but not limited to, the conduct, analysis, and synthesis of information from multiple population studies. Networks, by virtue of their greater scope, resources, population size, and opportunities for interdisciplinary collaboration, can address complex scientific questions that a single team alone cannot.3There is a strong rationale for using networks in human genome epidemiology particularly. Genetic epidemiology benefits from a large-scale population-based approach to identify genes underlying complex common diseases, to assess associations between genetic variants and disease susceptibility, and to examine potential gene–environment interactions.4–6 Because the epidemiologic risk for an individual genetic variant is likely to be small, a large sample size is needed for adequate statistical power.7 Power issues are even more pressing for less common disease outcomes. Replication in different populations and exposure settings is also required to confirm and validate results. The adoption of common guidelines for the conduct, analysis, reporting, and integration of studies across different teams is essential for credible replication. Transparency in acknowledging and incorporating both “positive” and “negative” results is necessary to direct subsequent research. Furthermore, newer and more efficient genotyping technologies must be integrated rapidly into current and planned population studies.8,9 Networks can support studies with sample sizes large enough to achieve “definitive” results, promote spinoff research projects, and yield faster “translation” of results into clinical and public health applications. Networks can also foster interdisciplinary and international collaboration.10 Lastly, networks can assemble databases that are useful for developing and applying new statistical methods for large data sets.11The experience of established networks provides an important knowledge base on which to develop recommendations for improving future efforts.12 The Human Genome Epidemiology Network (HuGENet) recently launched a global network of consortia working on human genome epidemiology.13 This Network of Investigator Networks aims to create a resource to share information, to offer methodologic support, to generate inclusive overviews of studies conducted in specific fields, and to facilitate rapid confirmation of findings. In October 2005, HuGENet brought together representatives from established and emerging networks to share their experiences at a workshop in Cambridge, U.K.14 In advance of the meeting, a qualitative questionnaire was distributed to workshop participants. The questionnaire elicited information on experiences and practices in building and maintaining consortia. This article reports on the numerous challenges and their possible solutions as identified by the workshop participants (summarized in Table 1) as well as new opportunities offered by the network approach to genetic and genomic epidemiology.SCIENTIFIC APPROACHSelection of Scientific QuestionsTo date, most networks have targeted projects originating from preliminary evidence of specific associations or for the purpose of genetic linkage. In most consortia, projects are selected through group discussion and informal or semiformal (eg, voting) prioritization of candidate gene targets. Most networks try to focus on the best possible candidates to generate definitive evidence, but, given the large proportion of false-positives in genetic epidemiology,15 there is considerable uncertainty about the criteria for selecting such targets.

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