Statistical and Computational Genetics, Wellcome Trust Sanger Institute, Cambs, UK
Hum Mol Genet. 2011 Oct 15;20(R2):R182-8. doi: 10.1093/hmg/ddr378. Epub 2011 Aug 25.
Attempting to classify patients into high or low risk for disease onset or outcomes is one of the cornerstones of epidemiology. For some (but by no means all) diseases, clinically usable risk prediction can be performed using classical risk factors such as body mass index, lipid levels, smoking status, family history and, under certain circumstances, genetics (e.g. BRCA1/2 in breast cancer). The advent of genome-wide association studies (GWAS) has led to the discovery of common risk loci for the majority of common diseases. These discoveries raise the possibility of using these variants for risk prediction in a clinical setting. We discuss the different ways in which the predictive accuracy of these loci can be measured, and survey the predictive accuracy of GWAS variants for 18 common diseases. We show that predictive accuracy from genetic models varies greatly across diseases, but that the range is similar to that of non-genetic risk-prediction models. We discuss what factors drive differences in predictive accuracy, and how much value these predictions add over classical predictive tests. We also review the uses and pitfalls of idealized models of risk prediction. Finally, we look forward towards possible future clinical implementation of genetic risk prediction, and discuss realistic expectations for future utility.
试图将患者分为疾病发病或结局的高风险或低风险人群是流行病学的基石之一。对于某些（但绝不是全部）疾病，可以使用经典的风险因素（如体重指数、血脂水平、吸烟状况、家族史，在某些情况下，还有遗传学（如乳腺癌中的 BRCA1/2））进行临床可用的风险预测。全基因组关联研究（GWAS）的出现导致了大多数常见疾病的常见风险位点的发现。这些发现提出了在临床环境中使用这些变体进行风险预测的可能性。我们讨论了衡量这些位点预测准确性的不同方法，并调查了 GWAS 变体对 18 种常见疾病的预测准确性。我们表明，遗传模型的预测准确性在疾病之间差异很大，但范围与非遗传风险预测模型相似。我们讨论了哪些因素导致预测准确性的差异，以及这些预测相对于经典预测测试增加了多少价值。我们还回顾了风险预测理想化模型的用途和陷阱。最后，我们展望了遗传风险预测在未来临床实施的可能性，并讨论了对未来效用的现实期望。
