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

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.

SUBMITTER: Aschard H

PROVIDER: S-EPMC3370279 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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

Aschard Hugues H Chen Jinbo J Cornelis Marilyn C MC Chibnik Lori B LB Karlson Elizabeth W EW Kraft Peter P

American journal of human genetics 20120524 6

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 a ...[more]

PMID: 22633398

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