|Higgins, Julian P|
|Casas, Juan Pablo|
|Smith, George Davey|
|Ioannidis, John P|
Submitted to: Epidemiology
Publication Type: Review Article
Publication Acceptance Date: 8/25/2006
Publication Date: 1/8/2007
Citation: Seminara, D., Khoury, M.J., O'Brien, T.R., Manolio, T., Gwinn, M.L., Little, J., Higgins, J.T., Bernstein, J.L., Boffetta, P., Bondy, M., Bray, M.S., Brenchley, P.E., Buffler, P.A., Casas, J.P., Chokkalingam, A.P., Danesh, J., Smith, G.D., Dolan, S., Duncan, R., Gruis, N.A., Hashibe, M., Hunter, D., Jarvelin, M., Malmer, B., Maraganore, D.M., Newton-Bishop, J.A., Riboli, E., Salanti, G., Taioli, E., Timpson, N., Uitterlinden, A.G., Vineis, P., Vareham, N., Winn, D.M., Zimmern, R., Ioannidis, J.P.A., for the Human Genome Epidemiology Network and the Network of Investigator Networks. 2007. The emergence of networks in human genome epidemiology: Challenges and opportunities. Epidemiology. 18(1):1-8. Interpretive Summary: This review paper describes the efforts being taken to coordinate the work of many large studies, in order to develop a better understanding of the role of gene variation in common disease. The paper gives recommendations and guidance as to how large networks of studies can be formed and how data from these studies can be shared. The paper strongly urges that coordinated efforts be made in the area of gene studies, especially for complex diseases.
Technical Abstract: Large-scale "big science" is advocated as an approach to complex research problems in many scientific areas. 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. 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 interdiciplinary collaborations, can address complex scientific questions that a single team alone cannot. There 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. Because the epidemiologic risk for an individual genetic variant is likely to be small, a large sample size is needed for adequate statistical power. 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. 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. Lastly, networks can assemble databases that are useful for developing and applying new statistical methods for large data sets.