Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.
Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.
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DOI：
10.1002/humu.23280
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发表时间：
2017-09
期刊：
影响因子：
3.9
通讯作者：
Morgan AA
中科院分区：
文献类型：
--
作者：
Daneshjou R;Wang Y;Bromberg Y;Bovo S;Martelli PL;Babbi G;Lena PD;Casadio R;Edwards M;Gifford D;Jones DT;Sundaram L;Bhat RR;Li X;Pal LR;Kundu K;Yin Y;Moult J;Jiang Y;Pejaver V;Pagel KA;Li B;Mooney SD;Radivojac P;Shah S;Carraro M;Gasparini A;Leonardi E;Giollo M;Ferrari C;Tosatto SCE;Bachar E;Azaria JR;Ofran Y;Unger R;Niroula A;Vihinen M;Chang B;Wang MH;Franke A;Petersen BS;Pirooznia M;Zandi P;McCombie R;Potash JB;Altman RB;Klein TE;Hoskins RA;Repo S;Brenner SE;Morgan AA
精准医学旨在通过使用患者的基因测序数据来预测患者的疾病风险和最佳治疗方案。基因组解释的关键评估（CAGI）是一个社区实验，由基因型-表型预测挑战组成;参与者建立模型，进行评估，并分享关键发现。对于CAGI 4，三个挑战涉及使用外显子组测序数据：双相情感障碍，克罗恩病和华法林剂量。先前的CAGI挑战包括克罗恩病挑战的先前版本。在这里，我们讨论了用于表型预测的技术范围，并讨论了用于评估预测模型的方法。此外，我们还概述了一些与预测和评估相关的困难。从外显子组挑战中吸取的教训可以应用于研究和临床努力，以改善从基因型预测表型。此外，这些挑战还可以作为与具有广泛专业知识的科学家以安全的方式共享临床和研究外显子组数据的工具，有助于协同努力，以促进我们对基因型-表型关系的理解。
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: bipolar disorder, Crohn’s disease, 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 and discuss 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.
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