Effect size measures in genetic association studies and age-conditional risk prediction.

The interest in risk prediction using genomic profiles has surged recently. A proper interpretation of effect size measures in association studies is crucial to accurate risk prediction. In this study, we clarified the relationship between the odds ratio (OR), relative risk and incidence rate ratios in the context of genetic association studies. We demonstrated that under the common practice of sampling prevalent cases and controls, the resulting ORs approximate the incidence rate ratios. Based on this result, we presented a framework to compute the disease risk given the current age and follow-up period (including lifetime risk), with consideration of competing risks of mortality. We considered two extensions. One is correcting the incidence rate to reflect the person-years alive and disease-free, the other is converting prevalence to incidence estimates. The methodology was applied to an example of breast cancer prediction. We observed that simply multiplying the OR by the average lifetime risk estimates yielded a final estimate >100% (101%), while using our method that accounts for competing risks produces an estimate of 63% only. We also applied the method to risk prediction of Alzheimer's disease in Hong Kong. We recommend that companies offering direct-to-consumer genetic testing employ more rigorous prediction algorithms considering competing risks.