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

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.

SUBMITTER: Daneshjou R

PROVIDER: S-EPMC5600620 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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

Daneshjou Roxana R Wang Yanran Y Bromberg Yana Y Bovo Samuele S Martelli Pier L PL Babbi Giulia G Lena Pietro Di PD Casadio Rita R Edwards Matthew M Gifford David D Jones David T DT Sundaram Laksshman L Bhat Rajendra Rana RR Li Xiaolin X Pal Lipika R LR Kundu Kunal K Yin Yizhou Y Moult John J Jiang Yuxiang Y Pejaver Vikas V Pagel Kymberleigh A KA Li Biao B Mooney Sean D SD Radivojac Predrag P Shah Sohela S Carraro Marco M Gasparini Alessandra A Leonardi Emanuela E Giollo Manuel M Ferrari Carlo C Tosatto Silvio C E SCE Bachar Eran E Azaria Johnathan R JR Ofran Yanay Y Unger Ron R Niroula Abhishek A Vihinen Mauno M Chang Billy B Wang Maggie H MH Franke Andre A Petersen Britt-Sabina BS Pirooznia Mehdi M Zandi Peter P McCombie Richard R Potash James B JB Altman Russ B RB Klein Teri E TE Hoskins Roger A RA Repo Susanna S Brenner Steven E SE Morgan Alexander A AA

Human mutation 20170707 9

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 in ...[more]

PMID: 28634997

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Project description:More reliable and cheaper sequencing technologies have revealed the vast mutational landscapes characteristic of many phenotypes. The analysis of such genetic variants has led to successful identification of altered proteins underlying many Mendelian disorders. Nevertheless the simple one-variant one-phenotype model valid for many monogenic diseases does not capture the complexity of polygenic traits and disorders. Although experimental and computational approaches have improved detection of functionally deleterious variants and important interactions between gene products, the development of comprehensive models relating genotype and phenotypes remains a challenge in the field of genomic medicine. In this context, a new view of the pathologic state as significant perturbation of the network of interactions between biomolecules is crucial for the identification of biochemical pathways associated with complex phenotypes. Seminal studies in systems biology combined the analysis of genetic variation with protein-protein interaction networks to demonstrate that even as biological systems evolve to be robust to genetic variation, their topologies create disease vulnerabilities. More recent analyses model the impact of genetic variants as changes to the "wiring" of the interactome to better capture heterogeneity in genotype-phenotype relationships. These studies lay the foundation for using networks to predict variant effects at scale using machine-learning or algorithmic approaches. A wealth of databases and resources for the annotation of genotype-phenotype relationships have been developed to support developments in this area. This overview describes how study of the molecular interactome has generated insights linking the organization of biological systems to disease mechanism, and how this information can enable precision medicine. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods.

Dataset Information

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

Publications

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

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