Chapter 7 - The emergence of networks in human genome epidemiology: challenges and opportunities Tables

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.”
These chapters were published with modifications by Oxford University PressExternal (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

Major Challenges Possible Solutions
Table 7-1
Challenges faced by networks of investigators in human genome epidemiology and 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
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
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
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

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Potential for networks to contribute to research progress in human genome epidemiology
Table 7-2
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

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Page last reviewed: January 6, 2010 (archived document)