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

Daneshjou, R., Wang, Y., Bromberg, Y., Bovo, S., Martelli, P. L., Babbi, G., Lena, P. D., Casadio, R., Edwards, M., Gifford, D., Jones, D. T., Sundaram, L., Bhat, R., Li, X., Pal, L. R., Kundu, K., Yin, Y., Moult, J., Jiang, Y., Pejaver, V., Pagel, K. A., Li, B., Mooney, S. D., Radivojac, P., Shah, S., Carraro, M., Gasparini, A., Leonardi, E., Giollo, M., Ferrari, C., Tosatto, S. C. E., Bachar, E., Azaria, J. R., Ofran, Y., Unger, R., Niroula, A., Vihinen, M., Chang, B., Wang, M. H., Franke, A., Petersen, B. S., Pirooznia, M., Zandi, P., McCombie, R., Potash, J. B., Altman, R., Klein, T. E., Hoskins, R., Repo, S., Brenner, S. E., Morgan, A. A. (2017) Working towards precision medicine: predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Hum Mutat, 38 (9). pp. 1182-1192. ISSN 1059-7794

URL: https://www.ncbi.nlm.nih.gov/pubmed/28634997
DOI: 10.1002/humu.23280

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.

Item Type: Paper
Uncontrolled Keywords: Crohn's disease bipolar disorder exomes machine learning phenotype prediction warfarin
Subjects: bioinformatics
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > genomes
CSHL Authors:
Communities: CSHL labs > McCombie lab
Depositing User: Matt Covey
Date Deposited: 30 Jun 2017 20:02
Last Modified: 31 Aug 2017 19:54
Related URLs:
URI: http://repository.cshl.edu/id/eprint/35035

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