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. 2011 May;19(5):18 p preceding 494.
doi: 10.1038/ejhg.2011.27.

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

A Cecile J W Janssens  1 , John P A Ioannidis, Sara Bedrosian, Paolo Boffetta, Siobhan M Dolan, Nicole Dowling, Isabel Fortier, Andrew N Freedman, Jeremy M Grimshaw, Jeffrey Gulcher, Marta Gwinn, Mark A Hlatky, Holly Janes, Peter Kraft, Stephanie Melillo, Christopher J O'Donnell, Michael J Pencina, David Ransohoff, Sheri D Schully, Daniela Seminara, Deborah M Winn, Caroline F Wright, Cornelia M van Duijn, Julian Little, Muin J Khoury
Affiliations

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

A Cecile J W Janssens et al. Eur J Hum Genet. 2011 May.

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.

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Figures

Figure 1
Figure 1
Example: distribution of the number of disease risk alleles among sporadic long-lived participants of the Leiden 85 Plus Study and Netherlands twin register controls.
Figure 2
Figure 2
Example: ROC curve analysis of adding genetic variables to clinical risk factors for the prediction of age-related macular degeneration. Area under the receiver operating characteristic curve for the age-related macular degeneration (AMD). The risk models were constructed from published genotype/exposure frequencies and odds ratios, using a simulation method that has been described previously. The clinical prediction model was based on age, sex, education, baseline AMD grade, smoking, body mass index and treatment. The added genetic factors were six single nucleotide polymorphisms. The curves indicate the sensitivity and 1-specificity for every possible cut-off value of predicted risks. The diagonal line indicates a hypothetical random predictor, which AUC equals 0.50.
See this image and copyright information in PMC

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