Working towards precision medicine: predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
Authors list
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 Pejaver Kymberleigh A Pagel Biao Li Sean D Mooney Predrag Radivojac Sohela Shah Marco Carraro Alessandra Gasparini Emanuela Leonardi Manuel Giollo Carlo Ferrari Silvio CE 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 Zandi Richard McCombie James B Potash Russ B Altman Teri E Klein Roger A Hoskins Susanna Repo Steven E Brenner Alexander A Morgan Toggle all authors (51)
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.
Journal details
Journal Human Mutation
Volume 38
Issue number 9
Pages 1182-1192
Available online
Publication date
Full text links
Publisher website (DOI) 10.1002/humu.23280
Europe PubMed Central 28634997
Pubmed 28634997
Keywords
Type of publication