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

Daneshjou, Roxana; Wang, Yanran; Bromberg, Yana; Bovo, Samuele; Martelli, Pier Luigi; Babbi, Giulia; Lena, Pietro Di; Casadio, Rita; Edwards, Matthew and Gifford, David, et al. (2017) In Human Mutation 38(9). p.1182-1192
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... (More)

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.

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published
subject
keywords
Bipolar disorder, Crohn's disease, Exomes, Machine learning, Phenotype prediction, Warfarin
in
Human Mutation
volume
38
issue
9
pages
1182 - 1192
publisher
John Wiley & Sons
external identifiers
  • scopus:85022033200
  • wos:000407861100014
ISSN
1059-7794
DOI
10.1002/humu.23280
language
English
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yes
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0a757bc7-434c-4f8c-a451-0d486b06bcaa
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2017-08-18 10:23:10
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2017-09-18 11:41:56
@article{0a757bc7-434c-4f8c-a451-0d486b06bcaa,
  abstract     = {<p>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.</p>},
  author       = {Daneshjou, Roxana and Wang, Yanran and Bromberg, Yana and Bovo, Samuele and Martelli, Pier Luigi and Babbi, Giulia and Lena, Pietro Di and Casadio, Rita and Edwards, Matthew and Gifford, David and Jones, David T W and Sundaram, Laksshman and Bhat, Rajendra and Li, Xiaolin and Pal, Lipika R. and Kundu, Kunal and Yin, Yizhou and Moult, John and Jiang, Yuxiang and Pejaver, Vikas R. and Pagel, Kymberleigh A. and Li, Biao and Mooney, Sean D. and Radivojac, Predrag and Shah, Sohela and Carraro, Marco and Gasparini, Alessandra and Leonardi, Emanuela and Giollo, Manuel and Ferrari, Carlo and Tosatto, Silvio C.E. and Bachar, Eran and Azaria, Johnathan R. and Ofran, Yanay and Unger, Ron and Niroula, Abhishek and Vihinen, Mauno and Chang, Billy and Wang, Maggie H. and Franke, Andre and Petersen, Britt Sabina and Pirooznia, Mehdi and Zandi, Peter and Mccombie, Richard and Potash, James B. and Altman, Russ B. and Klein, Teri E. and Hoskins, Roger A. and Repo, Susanna and Brenner, Steven E and Morgan, Alexander A.},
  issn         = {1059-7794},
  keyword      = {Bipolar disorder,Crohn's disease,Exomes,Machine learning,Phenotype prediction,Warfarin},
  language     = {eng},
  number       = {9},
  pages        = {1182--1192},
  publisher    = {John Wiley & Sons},
  series       = {Human Mutation},
  title        = {Working toward precision medicine : Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges},
  url          = {http://dx.doi.org/10.1002/humu.23280},
  volume       = {38},
  year         = {2017},
}