Chapter 7 - The emergence of networks in human genome epidemiology: challenges and opportunities Tables
ShareCompartir
This website is archived for historical purposes and is no longer being maintained or updated.
Human Genome Epidemiology (2nd ed.): Building the evidence for using genetic information to improve health and prevent disease
“The findings and conclusions in this book are those of the author(s) and do not
necessarily represent the views of the funding agency.”
necessarily represent the views of the funding agency.”
These chapters were published with modifications by Oxford University Press (2010)
Daniela Seminara, Muin J. Khoury, Thomas R. O’Brien, Teri Manolio, Marta Gwinn, Julian Little, Julian P. T. Higgins, Jonine L. Bernstein, Paolo Boffetta, Melissa L. Bondy, Molly S. Bray, Paul E. Brenchley, Patricia A. Buffler, Juan Pablo Casas, Anand P. Chokkalingam, John Danesh, George Davey Smith, Siobhan M. Dolan, Ross Duncan, Nelleke A. Gruis, Mia Hashibe, David J. Hunter, Marjo-Riitta Jarvelin, Beatrice Malmer, Demetrius M. Maraganore, Julia A. Newton-Bishop, Elio Riboli, Georgia Salanti, Emanuela Taioli, Nic Timpson, André G. Uitterlinden, Paolo Vineis, Nick Wareham, Deborah M. Winn, Ron Zimmern, and John P. A. Ioannidis
Table 7-1
Challenges faced by networks of investigators in human genome epidemiology and possible solutions
| Major Challenges | Possible Solutions |
|---|---|
| Resources for establishing the initial infrastructure, supporting consortia implementation, and adding new partners | New and more flexible funding mechanisms: planning grants, collaborative research grants Coordination among national and international funding agencies and foundations Appropriate evaluation criteria for continuation of funding |
| Coordination: minimize administration to maximize scientific progress and avoid conflicts | Clear leadership structure: steering committee and working groups Early development of policies and processes Cutting-edge communication technology |
| Selection of target projects | Questions that can be uniquely addressed by collaborative groups Preliminary supportive evidence High-profile controversial hypothesis Biologic plausibility Genomewide evidence |
| Variable data and biospecimen quality from participating teams | Eligibility criteria based on sample size Sound and appropriate study design Accurate phenotype outcome and genotype assessments State-of-the-art biospecimen repositories |
| Handling of information from nonparticipating teams and of negative results | Integration of evidence across all teams and networks in a field Comprehensive reporting to maintain transparency Curated updated encyclopedia of knowledge base |
| Collection, management, and analysis of complex and heterogeneous data sets | Central informatics unit or coordinating center “Think tank” for analytic challenges of retrospective and prospective data sets Centralization of genotyping Standardization or harmonization of phenotypic and genotypic data Standardization of quality control protocols across participating teams |
| Anticipating future needs | Rapid integration of evolving high throughput genomic technologies Consideration of centralized platforms Maximizing use of bioresources Public–private partnerships Development of analytic approaches for large and complex data sets |
| Communication and coordination | Web-based communication: Web sites and portals Teleconferences and meeting support |
| Scientific credits and career development |
Upfront definition of publication policies Mentorship of young investigators Change in tenure and authorship criteria |
| Access to the scientific community at large and transparency | Data-sharing plans and policies Support for release of public data sets Availability and dissemination of both “positive” and “negative” results Encyclopedia of knowledge |
| Peer review | Review criteria appropriate for interdisciplinary large science Education of peer scientists to consortia issues Inclusion of interdisciplinary expertise in initial review groups |
| Informed consent | Anticipation of data and biospecimen sharing requirements and careful phrasing of informed consent Sensitivity to local and national legislations |
Table 7-2
| Potential for networks to contribute to research progress in human genome epidemiology |
|---|
| Improve the quality of primary studies |
| Improve the standards of clinical, laboratory, and statistical methods |
| Strengthen the quality of international collaborative studies, and thereby reduce language and publication biases (50) |
| Provide empirical evidence for developing the optimal criteria for grading the credibility of evidence for genetic association studies (51) |
| Facilitate testing of between-studies heterogeneity in both allele frequencies and size of genetic effects across participating groups studying different populations |
| Facilitate replication of complex associations involving entire loci or pathways in large-scale data sets |
| Support methodologic development |
- Page last reviewed: January 6, 2010 (archived document)
- Content source: