Incremental value of rare genetic variants for the prediction of multifactorial diseases

• Type of Material: Article
• Authors: Raluca Mihaescu, Erasmus University Medical Center; Michael J Pencina, Boston University; Alvaro Alonso, University of Minnesota; Kathryn L Lunetta, Boston University; Susan R Heckbert, University of Washington; Emelia J Benjamin, The National Heart, Lung, and Blood Institute's Framingham Heart Study; A Cecile Janssens, Emory University
• Language: English
• Date: 2013
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2013 Mihaescu et al.; licensee BioMed Central Ltd.
• License: Creative Commons Attribution 2.0 Generic
• Final Published Version (URL): http://genomemedicine.com/content/5/8/76
• Title of Journal or Parent Work: Genome Medicine
• ISSN: 1756-994X
• Volume: 5
• Issue: 8
• Start Page: 76
• End Page: 76
• Grant/Funding Information: This work was supported by the Netherlands Organization for Scientific Research (Vidi grant to ACJWJ); Centre for Medical Systems Biology in the framework of the Netherlands Genomics Initiative (to ACJWJ); Erasmus Trustfonds (to RM); National Institutes of Health/American Recovery and Reinvestment Act Risk Prediction of Atrial Fibrillation (1RC1HL101056 to MP and EJB); National Institutes of Health/National Heart, Lung, and Blood Institute (RC1HL099452 to AA; HL068986 to SRH; HL092577 to EJB, KLL, and SRH; HL080295 to SRH; 1R01HL102214 to EJB; N01-HC 25195 to EJB); and the American Heart Association (09SDG2280087 to AA).
• Research Categories: Biology, Genetics

Abstract

Background It is often assumed that rare genetic variants will improve available risk prediction scores. We aimed to estimate the added predictive ability of rare variants for risk prediction of common diseases in hypothetical scenarios. Methods In simulated data, we constructed risk models with an area under the ROC curve (AUC) ranging between 0.50 and 0.95, to which we added a single variant representing the cumulative frequency and effect (odds ratio, OR) of multiple rare variants. The frequency of the rare variant ranged between 0.0001 and 0.01 and the OR between 2 and 10. We assessed the resulting AUC, increment in AUC, integrated discrimination improvement (IDI), net reclassification improvement (NRI(>0.01)) and categorical NRI. The analyses were illustrated by a simulation of atrial fibrillation risk prediction based on a published clinical risk model. Results We observed minimal improvement in AUC with the addition of rare variants. All measures increased with the frequency and OR of the variant, but maximum increment in AUC remained below 0.05. Increment in AUC and NRI(>0.01) decreased with higher AUC of the baseline model, whereas IDI remained constant. In the atrial fibrillation example, the maximum increment in AUC was 0.02 for a variant with frequency = 0.01 and OR = 10. IDI and NRI showed at most minimal increase for variants with frequency greater than or equal to 0.005 and OR greater than or equal to 5. Conclusions Since rare variants are present in only a minority of affected individuals, their predictive ability is generally low at the population level. To improve the predictive ability of clinical risk models for complex diseases, genetic variants must be common and have substantial effect on disease risk.