1 Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina 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{at}cc.uoi.gr).
Received for publication April 20, 2005. Accepted for publication May 18, 2005.
![]() |
ABSTRACT |
---|
![]() ![]() ![]() |
---|
epidemiology; genome; meta-analysis; multicenter studies
The task of understanding the role played by human genetic variation in complex diseases is daunting (14
). 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 publicationwith due creditof "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 Networksponsored (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.
|
![]() |
ACKNOWLEDGMENTS |
---|
![]() |
References |
---|
![]() ![]() ![]() |
---|