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. 2012 Jun 8;90(6):962-72.
doi: 10.1016/j.ajhg.2012.04.017. Epub 2012 May 24.

Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases

Hugues Aschard  1 , Jinbo Chen, Marilyn C Cornelis, Lori B Chibnik, Elizabeth W Karlson, Peter Kraft
Affiliations

Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases

Hugues Aschard et al. Am J Hum Genet. .

Erratum in

  • Am J Hum Genet. 2012 Jun 8;90(6):1116

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.

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Figures

Figure 1
Figure 1
Absolute Increase in AUC and cNRI by the Number of Interactions Simulated Breast cancer (BRCA), rheumatoid arthritis (RA), and type 2 diabetes (T2D).
Figure 2
Figure 2
Absolute Increase in AUC and cNRI by the Maximum Interaction Effect Simulated Breast cancer (BRCA), rheumatoid arthritis (RA), and type 2 diabetes (T2D).
Figure 3
Figure 3
Comparison of Increase in AUC between Models that Include a Low Number of Strong-Interaction Effects with Models that Include a Large Number of Low-Interaction Effects We compared the absolute increase in AUC between models that include two strong interactions (dashed line) and models that include ten low interactions (solid line) for BRCA (blue), RA (green), and T2D (red). Probability density functions were estimated from 1,000 simulations for each scenario.
Figure 4
Figure 4
Absolute Increase in Sensitivity by Maximum Interaction Effect and Specify Threshold Breast cancer (BRCA), rheumatoid arthritis (RA), and type 2 diabetes (T2D).
See this image and copyright information in PMC

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