Working toward 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 Rana Bhat, Xiaolin Li, Lipika R. Pal, Kunal Kundu, Yizhou Yin, John Moult, Yuxiang Jiang, Vikas PejaverKymberleigh 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 P Zandi, Richard McCombie, James Bennett Potash, Russ B. Altman, Teri E. Klein, Roger A. Hoskins, Susanna Repo, Steven E. Brenner, Alexander A. Morgan

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)1182-1192
Number of pages11
JournalHuman Mutation
Volume38
Issue number9
DOIs
StatePublished - Sep 1 2017

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Keywords

  • bipolar disorder
  • Crohn's disease
  • exomes
  • machine learning
  • phenotype prediction
  • warfarin

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

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. R., Li, X., Pal, L. R., Kundu, K., Yin, Y., Moult, J., Jiang, Y., ... Morgan, A. A. (2017). Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges. Human Mutation, 38(9), 1182-1192. https://doi.org/10.1002/humu.23280