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A Network of Investigator Networks in Human Genome Epidemiology

by John P. A. Ioannidis1,2, Jonine Bernstein3, Paolo Boffetta4, John Danesh5, Siobhan Dolan6, Patricia Hartge7, David Hunter8, Peter Inskip7, Marjo-Riitta Jarvelin9,10, Julian Little11, Demetrius M. Maraganore12, Julia A.Newton Bishop13, Thomas R. O'Brien7, Gloria Petersen14, Elio Riboli15, Daniela Seminara16, Emanuela Taioli17, André G. Uitterlinden18, Paolo Vineis9,19, Deborah M. Winn7, Georgia Salanti20, Julian P. T. Higgins20,21 and Muin J. Khoury22

American Journal of Epidemiology 2005; 162(4):302-304

1 Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannia School of Medicine, Ioannina, Greece
2 Department of Medicine, Tufts University School of Medicine, Boston, MA
3 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY
4 Gene-Environment Epidemiology Group, International Agency for Research on Cancer, Lyon, France
5 Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
6 March of Dimes, White Plains, NY
7 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
8 Department of Epidemiology, Harvard School of Public Health, Boston, MA
9 Department of Epidemiology and Public Health, Imperial College, London, United Kingdom
10 Department of Public Health Science and General Practice, University of Oulu, Oulu, Finland
11 Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
12 Department of Neurology, Mayo Clinic, Rochester, MN
13 Genetic Epidemiology Division, CR-UK Clinical Centre, Leeds, United Kingdom
14 Department of Health Sciences Research, Mayo Clinic, Rochester, MN
15 Unit of Nutrition and Cancer, International Agency for Research on Cancer, Lyon, France
16 Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
17 Molecular and Genetic Epidemiology Unit, Ospedale Policlinico IRCCS, Milan, Italy
18 Departments of Internal Medicine and of Epidemiology and Biostatistics, Erasmus MC, Rotterdam, the Netherlands
19 ISI Foundation, Torino, Italy
20 MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
21 Public Health Genetics Unit, Strangeways Research Laboratory, Cambridge, United Kingdom
22 Office of Genomics and Disease Prevention, Centers for Disease Control and Prevention, Atlanta, GA

Correspondence to Dr. John P. A. Ioannidis, Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece (e-mail: jioannid@cc.uoi.gr ).

 

Received for publication: April 20, 2005
Accepted for publication: May 18, 2005

 


 

Abstract

The task of identifying genetic determinants for complex, multigenetic diseases is hampered by small studies, publication and reporting biases, and lack of common standards worldwide. The authors propose the creation of a network of networks that include groups of investigators collecting data for human genome epidemiology research. Twenty-three networks of investigators addressing specific diseases or research topics and representing several hundreds of teams have already joined this initiative. For each field, the authors are currently creating a core registry of teams already participating in the respective network. A wider international registry will include all other teams also working in the same field. Independent investigators are invited to join the registries and existing networks and to join forces in creating additional ones as needed. The network of networks aims to register these networks, teams, and investigators; be a resource for information about or connections to the many networks; offer methodological support; promote sound design and standardization of analytical practices; generate inclusive overviews of fields at large; facilitate rapid confirmation of findings; and avoid duplication of effort.

Keywords: epidemiology; genome; meta-analysis; multicenter studies

 


 

The task of understanding the role played by human genetic variation in complex diseases is daunting (1–4). High-throughput genotyping, exploratory statistical analyses in studies with limited sample sizes, publication bias, and selective reporting may generate spurious findings that fail replication (1). Publication bias and selective reporting are well-recognized problems across all domains of epidemiologic and clinical research (5–7). The editors of leading medical journals recently required up-front registration of clinical trials as a prerequisite for eventual publication in their journals (8). However, study registration is difficult to implement in genetic epidemiology: molecular studies often can be carried out rapidly by using established specimen and data collections, and investigators are reluctant to publicly register their hypotheses in advance of publication.

An alternative to study registration is to create inclusive registries of investigators and information about sample and/or data collections in different fields. Whereas individual investigators continue to pursue their chosen lines of research, networks permit broad, consistent, and transparent assessment and replication of novel findings obtained in individual studies (9). These networks can also facilitate prompt publication—with due credit—of "negative" results and explore reasons for conflicting findings. Finally, the quality and credibility of research can be enhanced by standardization of clinical, laboratory, and statistical methods used by investigators working on the same research questions.

Several registries and networks addressing specific diseases or research questions are already ongoing (9). It is important to share with the international research community at large the expertise and experiences in creating these networks and to provide a comprehensive registry including information on the existing networks. A network of networks is needed to perform this function across diseases and genes. Doing so is critical, because some biologic pathways can affect the risk of many different diseases, and genetic effects also impact multiple pathways and diseases. Moreover, many issues affecting large-scale collaborations such as publication policies, informed consent, biospecimen processing and management, and creation of informatics infrastructures are common to networks across disciplines. The network of networks should offer methodological support, promote sound design and standardization of practices, and generate up-to-date overviews of each field. This structure will operate without limiting the scientific independence of each network to maximize efficiency and will avoid unhelpful duplication of efforts.

The proposed network-of-networks concept arose at a Human Genome Epidemiology Network–sponsored (10, 11) workshop in Cambridge, United Kingdom, in November 2004 that targeted methodological issues pertaining to the quantitative synthesis of genetic epidemiologic information from diverse studies. The workshop was attended by several scientists involved in existing networks. Within a short time, we have grown to include 23 networks representing several hundreds of teams and thousands of individual investigators (Table 1) and have identified many additional networks. Most consortia focus on specific diseases, but others are organized around a common interest in specific genes or environmental risk factors modulated by genes.

We are currently creating and expanding the core registry that lists information on all teams participating in each existing network, as well as wider registries that also list information on all other teams working in the same field worldwide. The wider registries are being compiled based on electronic searches of the published literature in each field, but we also want to identify all investigators interested in working in the specific area. These efforts will generate a comprehensive map of each field and would eventually facilitate creation of a credible synopsis of validated associations of genetic variants with complex disease. We encourage investigators worldwide to communicate with the coordinators of each network (refer to Table 1 for contact information) to discuss the possibility of mutually beneficial collaboration. We want to be as inclusive as possible. We also invite other existing networks to join in this initiative, and we encourage investigators who want to create networks in other fields to communicate with us. An October 6–7, 2005, meeting is being planned in Cambridge, United Kingdom, where network representatives will present and share experiences in creating, managing, and maintaining their collaborations. We would also like to have this meeting attended by scientists interested in creating new networks in their fields. Other future meetings and consensus statements will aim at developing "best practices" for study designs and standardizing statistical methods for analyzing data from international consortia.

References

  1. Ioannidis JP. Genetic associations: false or true? Trends Mol Med 2003;9:135–8.
  2. Lander ES, Schork NJ. Genetic dissection of complex traits. Science 1994;265:2037–48.
  3. Ioannidis JP, Ntzani EE, Trikalinos TA, et al. Replication validity of genetic association studies. Nat Genet 2001;29:306–9.
  4. Lohmueller KE, Pearce CL, Pike M, et al. Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 2003;33:177–82.
  5. Chan AW, Hrobjartsson A, Haahr MT, et al. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA 2004;291:2457–65.
  6. Ioannidis JP. Effect of the statistical significance of results on the time to completion and publication of randomized efficacy trials. JAMA 1998;279:281–6.
  7. Easterbrook PJ, Berlin JA, Gopalan R, et al. Publication bias in clinical research. Lancet 1991;337:867–72.
  8. De Angelis C, Drazen JM, Frizelle FA, et al. Clinical trial registration: a statement from the International Committee of Medical Journal Editors. N Engl J Med 2004;351:1250–1.
  9. Ioannidis JP, Rosenberg PS, Goedert JJ, et al. Commentary: meta-analysis of individual participants' data in genetic epidemiology. Am J Epidemiol 2002;156:204–10.
  10. Khoury MJ, Little J. Human genome epidemiologic reviews: the beginning of something HuGE. Am J Epidemiol 2000;151:2–3.
  11. Little J, Khoury MJ, Bradley L, et al. The Human Genome Project is complete. How do we develop a handle for the pump? Am J Epidemiol 2003;157:667–73.
 

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