Risk Models Only Slightly Up Prediction of Complex Diseases

Modest improvement for risk models that consider gene-gene, gene-environment interactions

WEDNESDAY, May 30 (HealthDay News) -- Risk models that take gene-gene and gene-environment interactions into account only slightly improve the prediction of risk for three complex diseases, according to a study published online May 24 in the American Journal of Human Genetics.

Hugues Aschard, Ph.D., from the Harvard School of Public Health in Boston, and colleagues performed simulations to determine whether including multiple gene-gene and gene-environment interactions would affect the prediction of disease risk for breast cancer, type 2 diabetes, and rheumatoid arthritis. The improvement in discriminative ability was assessed using the difference in the area under the receiver operating characteristic curve (AUC).

The researchers found that, on inclusion of two to 10 gene-gene and gene-environment effects in the risk models, the improvement in discriminative ability was 2.82 percent for breast cancer, 1.40 percent for rheumatoid arthritis, and 0.85 percent for type 2 diabetes. There was an increase in the improvement of the AUC with increasing interaction effect, with the magnitude differing by disease.

"This study suggests that the identification of statistical interactions among these factors might have a modest impact on risk prediction and discrimination for common complex diseases," the authors write. "We stress that although gene-gene and gene-environment interactions might have modest impacts on risk prediction, an understanding of the interplay between genes and the environment can provide insights into disease etiology; this understanding, in turn, can lead to improved treatment and prevention strategies.

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