Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases.
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.
Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA. haschard@hsph.harvard.edu
Hugues
H
Aschard
Jinbo
J
Chen
Marilyn C
MC
Cornelis
Lori B
LB
Chibnik
Elizabeth W
EW
Karlson
Peter
P
Kraft
D019540:Area Under Curve; D001172:Arthritis, Rheumatoid / Q000175:diagnosis / Q000235:genetics*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics*; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000235:genetics*; D004777:Environment; D005260:Female; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D012306:Risk; D012307:Risk Factors