常见复杂疾病个体基因组检测预测风险的差异。
Variations in predicted risks in personal genome testing for common complex diseases.
机构信息
Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Department of Human and Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands.
出版信息
Genet Med. 2014 Jan;16(1):85-91. doi: 10.1038/gim.2013.80. Epub 2013 Jun 27.
PURPOSE
The promise of personalized genomics for common complex diseases depends, in part, on the ability to predict genetic risks on the basis of single nucleotide polymorphisms. We examined and compared the methods of three companies (23andMe, deCODEme, and Navigenics) that have offered direct-to-consumer personal genome testing.
METHODS
We simulated genotype data for 100,000 individuals on the basis of published genotype frequencies and predicted disease risks using the methods of the companies. Predictive ability for six diseases was assessed by the AUC.
RESULTS
AUC values differed among the diseases and among the companies. The highest values of the AUC were observed for age-related macular degeneration, celiac disease, and Crohn disease. The largest difference among the companies was found for celiac disease: the AUC was 0.73 for 23andMe and 0.82 for deCODEme. Predicted risks differed substantially among the companies as a result of differences in the sets of single nucleotide polymorphisms selected and the average population risks selected by the companies, and in the formulas used for the calculation of risks.
CONCLUSION
Future efforts to design predictive models for the genomics of common complex diseases may benefit from understanding the strengths and limitations of the predictive algorithms designed by these early companies.
目的
个性化基因组学在常见复杂疾病中的应用前景，部分取决于能否基于单核苷酸多态性预测遗传风险。我们研究并比较了三家提供直接面向消费者的个人基因组检测服务的公司（23andMe、deCODEme 和 Navigenics）的方法。
方法
我们根据已发表的基因型频率模拟了 10 万名个体的基因型数据，并使用公司的方法预测了疾病风险。采用 AUC 评估了 6 种疾病的预测能力。
结果
疾病和公司之间的 AUC 值存在差异。AUC 值最高的疾病为年龄相关性黄斑变性、乳糜泻和克罗恩病。乳糜泻方面公司之间的差异最大：23andMe 的 AUC 为 0.73，deCODEme 的 AUC 为 0.82。由于所选单核苷酸多态性集和公司选择的平均人群风险以及用于计算风险的公式不同，各公司之间的预测风险存在显著差异。
结论
未来设计常见复杂疾病基因组学预测模型的工作可能会受益于对这些早期公司设计的预测算法的优势和局限性的了解。
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