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Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
Author(s) -
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 BrittSabina,
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.
Publication year - 2017
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23280
Subject(s) - biology , precision medicine , exome , exome sequencing , computational biology , phenotype , disease , clinical phenotype , personalized medicine , human genetics , genetics , bioinformatics , gene , medicine
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.