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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)962-972
Number of pages11
JournalAmerican journal of human genetics
Volume90
Issue number6
DOIs
StatePublished - Jun 8 2012

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Fingerprint Dive into the research topics of 'Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases'. Together they form a unique fingerprint.

Cite this