Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

European Journal of Human Genetics (2011) 19; doi:10.1038/ejhg.2011.27 & 2011 Macmillan Publishers Limited All rights reserved 1018-4813/11 www.nature.com/ejhg POLICY Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration A Cecile JW Janssens*,1, 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 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. European Journal of Human Genetics (2011) 19; doi:10.1038/ejhg.2011.27; published online 16 March 2011 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. Genetic risk prediction studies typically concern the development and/or evaluation of models for the prediction of a particular health outcome, but there is considerable variation in their design, conduct and analysis. Genetic risk models most frequently predict risk of disease, but they are also being investigated for the prediction of prognostic outcome, treatment response or treatment side effects. Risk prediction models are used in research and clinical settings to classify individuals into homogeneous groups for example, for randomization in clinical trials and for targeting preventive or therapeutic interventions. The main study designs are cohort, cross-sectional or case– control. The genetic risk factors often are SNPs, but other variants such as insertions/deletions, haplotypes and copy number variations can be included as well. The risk models are based on genetic variants only, or include both genetic and non-genetic risk factors. Risk prediction models are statistical algorithms, which can be simple genetic risk scores (eg, risk allele counts), or be based on regression analyses (eg, weighted risk scores or predicted risks) or on more complex analytic approaches such as support vector machine learning 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 and 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: GRIPS statement: explanation and elaboration ACJW Janssens et al or classification trees. Papers on genetic risk prediction vary as to whether they present the development of a risk model only, the validation of one or more risk models only, or both development and validation of a risk model.3 Lastly, studi (...truncated)