European Journal of Human Genetics - Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

European Journal of Human Genetics advance online publication 16 March 2011; doi: 10.1038/ejhg.2011.27

Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration
EJHGOpen
A Cecile JW Janssens1, John PA Ioannidis2,3,4,5,6, Sara Bedrosian7, Paolo Boffetta8,9, Siobhan M Dolan10, Nicole Dowling7, Isabel Fortier11, Andrew N Freedman12, Jeremy M Grimshaw13,14, Jeffrey Gulcher15, Marta Gwinn7, Mark A Hlatky16, Holly Janes17, Peter Kraft18, Stephanie Melillo7, Christopher J O'Donnell19,20, Michael J Pencina21,22, David Ransohoff23, Sheri D Schully12, Daniela Seminara12, Deborah M Winn12, Caroline F Wright24, Cornelia M van Duijn1, Julian Little25 and Muin J Khoury7

1Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
2Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
3Biomedical Research Institute, Foundation for Research and Technology, Ioannina, Greece
4Department of Medicine, Tufts University School of Medicine, Boston, MA, USA
5Center for Genetic Epidemiology and Modeling and Tufts CTSI, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
6Stanford Prevention Research Center, Stanford University School of Medicine, StanfordCA, USA
7Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, GA, USA
8The Tisch Cancer Institute, Mount Sinai School of Medicine, New York, NY, USA
9International Prevention Research Institute, Lyon, France
10Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine/ Montefiore Medical Center, Bronx, NY, USA
11Public Population Project in Genomics (P3G), Montreal, QC, Canada
12Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
13Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
14Department of Medicine, University of Ottawa, Ottawa, ON, Canada
15deCODE Genetics, Reykjavik, Iceland
16Department of Health Research and Policy, Stanford University, Palo Alto, CA, USA
17Fred Hutchinson Cancer Research Center, Vaccine and Infectious Disease Institute and Division of Public Health Sciences, Seattle, WA, USA
18Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
19National Heart, Lung and Blood Institute (NHLBI) and the NHLBI's Framingham Heart Study, Framingham, MA, USA
20Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
21Department of Biostatistics, Boston University, Boston, MA, USA
22Harvard Clinical Research Institute, Boston, MA, USA
23University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
24PHG Foundation, Cambridge, UK
25Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, ON, Canada
Correspondence: Dr ACJW Janssens, Department of Epidemiology, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam, The Netherlands. E-mail: a.janssens@erasmusmc.nl


Abstract
The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but they vary widely in completeness of reporting and apparent quality. Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS), building on the principles established by previous reporting guidelines. These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

The advent of genome-wide association studies has accelerated the discovery of novel genetic markers, in particular single nucleotide polymorphisms (SNPs), which are associated with risk for common complex diseases. Technological developments in large-scale genomic studies, such as whole genome sequencing, will facilitate the discovery of novel common SNPs, as well as of rare variants, copy number variations, deletions/insertions, structural variations (eg, inversions) and epigenetic effects that influence the regulation of gene expression. These developments are fueling interest in the translation of this basic knowledge to health care practice. Knowledge about genetic risk factors may be used to target diagnostic, preventive and therapeutic interventions for complex disorders based on a person's genetic risk, or to complement existing risk models based on classical non-genetic factors such as the Framingham risk score for cardiovascular disease. Implementation of genetic risk prediction in health care requires a series of studies that encompass all phases of translational research,1, 2 starting with a comprehensive evaluation of genetic risk prediction.

full-text:
European Journal of Human Genetics - Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration