Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
Name
nihms885554.pdf
Description
Accepted version
Size
2.63 MB
Format
Adobe PDF
Checksum (MD5)
e8192eab26ce48620884432aee355d16
Author(s)
Daneshjou, Roxana
Wang, Yanran
Bromberg, Yana
Bovo, Samuele
Martelli, Pier L
Babbi, Giulia
Di Lena, Pietro
Casadio, Rita
Edwards, Matthew D
Gifford, David K
Date Issued
August 2017
Journal
Human Mutation
Publisher
Wiley
Citation
Daneshjou, Roxana et al. "Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges." Human Mutation 38, 9 (September 2017): 1182-1192 © 2017 Wiley Periodicals, Inc.
Version
Author's final manuscript
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: 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1002/HUMU.23280
