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

Daneshjou R1, Wang Y2, Bromberg Y2, Bovo S3, Martelli PL3, Babbi G3, Lena PD4, Casadio R3,5, Edwards M6, Gifford D6, Jones DT7, Sundaram L8, Bhat R9, Li X8, Pal LR9, Kundu K9,10, Yin Y9,10, Moult J9,11, Jiang Y12, Pejaver V12,13, Pagel KA12, Li B14, Mooney SD13, Radivojac P12, Shah S15, Carraro M16, Gasparini A16,17, Leonardi E17, Giollo M16,18, Ferrari C18, Tosatto SCE16,19, Bachar E20, Azaria JR20, Ofran Y20, Unger R20, Niroula A21, Vihinen M21, Chang B22, Wang MH22,23, Franke A24, Petersen BS24, Pirooznia M25, Zandi P26, McCombie R27, Potash JB28, Altman R1, Klein TE1, Hoskins R29, Repo S29, Brenner SE29, Morgan AA30.

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Abstract

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.