Inclusion of Gene-Gene and Gene-Environment Interactions Unlikely to Dramatically Improve Risk Prediction for Complex Diseases.

Aschard H, Chen J, Cornelis MC, Chibnik LB, Karlson EW, Kraft P.

Source

Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, 02115 USA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115 USA.

Abstract

Genome-wide association studies have identified hundreds of common genetic variants associated with the risk of multifactorial diseases. However, their impact on discrimination and risk prediction is limited. It has been suggested that the identification of gene-gene (G-G) and gene-environment (G-E) interactions would improve disease prediction and facilitate prevention. We conducted a simulation study to explore the potential improvement in discrimination if G-G and G-E interactions exist and are known. We used three diseases (breast cancer, type 2 diabetes, and rheumatoid arthritis) as motivating examples. We show that the inclusion of G-G and G-E interaction effects in risk-prediction models is unlikely to dramatically improve the discrimination ability of these models.
Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.