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American Journal of Epidemiology Advance Access originally published online on September 5, 2007
American Journal of Epidemiology 2007 166(8):863-866; doi:10.1093/aje/kwm248
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American Journal of Epidemiology © The Author 2007. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Turning the Pump Handle: Evolving Methods for Integrating the Evidence on Gene-Disease Association

Julian P. T. Higgins1, Julian Little2, John P. A. Ioannidis3,5, Molly S. Bray4, Teri A. Manolio6, Liam Smeeth7, Jonathan A. Sterne8, Betsy Anagnostelis9, Adam S. Butterworth10, John Danesh10, Carol Dezateux11, John E. Gallacher12, Marta Gwinn13, Sarah J. Lewis8, Cosetta Minelli14, Paul D. Pharoah15, Georgia Salanti3, Simon Sanderson10, Lesley A. Smith16, Emanuela Taioli17, John R. Thompson18, Simon G. Thompson1, Neil Walker19, Ron L. Zimmern20 and Muin J. Khoury13

1 MRC Biostatistics Unit, Cambridge, United Kingdom
2 Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
3 Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
4 Center for Human Genetics, Institute of Molecular Medicine and School of Public Health, University of Texas, Houston, TX
5 Department of Medicine, Tufts University School of Medicine, Boston, MA
6 National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
7 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
8 Department of Social Medicine, University of Bristol, Bristol, United Kingdom
9 Royal Free Hospital Medical Library, University College London, London, United Kingdom
10 Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
11 Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, London, United Kingdom
12 Department of Epidemiology, Cardiff University, Cardiff, Wales, United Kingdom
13 National Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA
14 National Heart and Lung Institute, Imperial College, London, United Kingdom
15 Cancer Research UK Human Cancer Genetics Group, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
16 School of Health and Social Care, Oxford Brookes University, Oxford, United Kingdom
17 University of Pittsburgh Medical Center, Pittsburgh, PA
18 Department of Health Sciences, University of Leicester, Leicester, United Kingdom
19 Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, United Kingdom
20 PHG Foundation, Cambridge, United Kingdom

Correspondence to Dr J. P. T. Higgins, MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, United Kingdom (e-mail: julian.higgins@mrc-bsu.cam.ac.uk).

Received for publication July 2, 2007. Accepted for publication August 1, 2007.

Abbreviations: HuGE, Human Genome Epidemiology; HuGENet, Human Genome Epidemiology Network

The first 10% of the full text of this article appears below.

Recent findings from genome-wide association studies have demonstrated their considerable potential for identifying genetic determinants of common diseases of public health significance such as cancer, heart disease, and diabetes (1), but they have also highlighted the continued importance of targeted genotyping to replicate genome-wide association findings (2). Approaches to the integration of evidence in human genome epidemiology have evolved rapidly in the last few years. The combination of results from multiple studies, often known as meta-analysis, has a key role both in enhancing power and in characterizing relative risks (3). As evidence accumulates on genetic variants that confer identifiable effects on disease susceptibility, so does the need to summarize the evidence in digestible and accessible formats. Here, we describe how the Human Genome Epidemiology Network (HuGENet) is keeping abreast of developments in methods . . . [Full Text of this Article]


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