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首页> 外文期刊>Human Heredity >Effect size measures in genetic association studies and age-conditional risk prediction.
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Effect size measures in genetic association studies and age-conditional risk prediction.

机译：基因关联研究和年龄条件风险预测中的效应量度。

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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.
机译：最近，对使用基因组图谱进行风险预测的兴趣激增。在关联研究中正确解释效应量度对准确预测风险至关重要。在这项研究中，我们阐明了在遗传关联研究中优势比（OR），相对风险和发生率之间的关系。我们证明，在对流行病例和对照进行抽样的常规做法下，得出的OR近似于发病率比率。基于此结果，我们提出了一个框架，该框架在考虑到死亡的竞争风险的情况下，根据当前年龄和随访时间（包括终身风险）来计算疾病风险。我们考虑了两个扩展。一种是校正发病率以反映未受疾病侵袭的人的年数，另一种是将患病率转换为发病率估计值。将该方法应用于乳腺癌预测的例子。我们观察到，简单地将OR乘以平均生命周期风险估算值，就可以得出最终估算值> 100％（101％），而使用我们的方法来计算竞争风险则只能得出63％的估算值。我们还将该方法应用于香港阿尔茨海默氏病的风险预测。考虑到竞争风险，我们建议提供直接面向消费者的基因测试的公司采用更严格的预测算法。

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