Working towards precision medicine: predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.

Roxana Daneshjou, Yanran Wang, Yana Bromberg, Samuele Bovo, Pier L Martelli, Giulia Babbi, Pietro Di Lena, Rita Casadio, Matthew Edwards, David Gifford, David T Jones, Laksshman Sundaram, Rajendra Bhat, Xiaolin Li, Lipika R Pal, Kunal Kundu, Yizhou Yin, John Moult, Yuxiang Jiang, Vikas Pejaver, Kymberleigh A Pagel, Biao Li, Sean D Mooney, Predrag Radivojac, Sohela Shah, Marco Carraro, Alessandra Gasparini, Emanuela Leonardi, Manuel Giollo, Carlo Ferrari, Silvio C E Tosatto, Eran Bachar, Johnathan R Azaria, Yanay Ofran, Ron Unger, Abhishek Niroula, Mauno Vihinen, Billy Chang, Maggie H Wang, Andre Franke, Britt-Sabina Petersen, Mehdi Pirooznia, Peter Zandi, Richard McCombie, James B Potash, Russ Altman, Teri E Klein, Roger Hoskins, Susanna Repo, Steve E Brenner, Alexander A Morgan
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Human mutation
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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. This article is protected by copyright. All rights reserved.