Daneshjou Roxana, Wang Yanran, Bromberg Yana, Bovo Samuele, Martelli Pier L, Babbi Giulia, Lena Pietro Di, Casadio Rita, Edwards Matthew, Gifford David, Jones David T, Sundaram Laksshman, Bhat Rajendra Rana, Li Xiaolin, Pal Lipika R, Kundu Kunal, Yin Yizhou, Moult John, Jiang Yuxiang, Pejaver Vikas, Pagel Kymberleigh A, Li Biao, Mooney Sean D, Radivojac Predrag, Shah Sohela, Carraro Marco, Gasparini Alessandra, Leonardi Emanuela, Giollo Manuel, Ferrari Carlo, Tosatto Silvio C E, Bachar Eran, Azaria Johnathan R, Ofran Yanay, Unger Ron, Niroula Abhishek, Vihinen Mauno, Chang Billy, Wang Maggie H, Franke Andre, Petersen Britt-Sabina, Pirooznia Mehdi, Zandi Peter, McCombie Richard, Potash James B, Altman Russ B, Klein Teri E, Hoskins Roger A, Repo Susanna, Brenner Steven E, Morgan Alexander A
Department of Genetics, Stanford School of Medicine, Stanford, California.
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.
Hum Mutat. 2017 Sep;38(9):1182-1192. doi: 10.1002/humu.23280. Epub 2017 Jul 7.
Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.
精准医学旨在通过利用个体的基因测序数据来预测患者的疾病风险和最佳治疗方案。基因组解读关键评估（CAGI）是一项由基因型-表型预测挑战组成的社区实验；参与者构建模型、接受评估并分享关键发现。对于CAGI 4，三项挑战涉及使用外显子组测序数据：克罗恩病、双相情感障碍和华法林剂量。之前的CAGI挑战包括克罗恩病挑战的早期版本。在此，我们讨论用于表型预测的技术范围以及用于评估预测模型的方法。此外，我们概述了进行预测和评估预测时面临的一些困难。从外显子组挑战中学到的经验教训可应用于研究和临床工作，以改善从基因型进行的表型预测。此外，这些挑战是一种与具有广泛专业知识的科学家以安全方式共享临床和研究外显子组数据的手段，有助于共同努力增进我们对基因型-表型关系的理解。
