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 Pejaver; Kymberleigh 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 Zandi; Richard McCombie; James B Potash; Russ B Altman; Teri E Klein; Roger A Hoskins; Susanna Repo; Steven E Brenner; Alexander A Morgan

doi:10.1002/humu.23280

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

Journal article

Open access

Peer reviewed

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, …

Human mutation, Vol.38(9), pp.1182-1192

09/2017

DOI: https://doi.org/10.1002/humu.23280

PMID: 28634997

Abstract

Bipolar Disorder - genetics

Computational Biology - methods

Crohn Disease - genetics

Databases, Genetic

Genetic Predisposition to Disease

Humans

Information Dissemination

Pharmacogenomic Variants

Precision Medicine - methods

Warfarin - pharmacology

Warfarin - therapeutic use

Whole Exome Sequencing - methods

Phenotype

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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Title: Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
Creators: Roxana Daneshjou - Department of Genetics, Stanford School of Medicine, Stanford, California
Yanran Wang - Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
Yana Bromberg - Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
Samuele Bovo - Biocomputing Group, BiGeA/CIG, "Luigi Galvani" Interdepartmental Center for Integrated Studies of Bioinformatics, Biophysics, and Biocomplexity, University of Bologna, Bologna, Italy
Pier L Martelli - Biocomputing Group, BiGeA/CIG, "Luigi Galvani" Interdepartmental Center for Integrated Studies of Bioinformatics, Biophysics, and Biocomplexity, University of Bologna, Bologna, Italy
Giulia Babbi - Biocomputing Group, BiGeA/CIG, "Luigi Galvani" Interdepartmental Center for Integrated Studies of Bioinformatics, Biophysics, and Biocomplexity, University of Bologna, Bologna, Italy
Pietro Di Lena - Biocomputing Group/Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
Rita Casadio - "Giorgio Prodi" Interdepartmental Center for Cancer Research, University of Bologna, Bologna, Italy
Matthew Edwards - Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
David Gifford - Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts
David T Jones - Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
Laksshman Sundaram - Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida, Gainesville, Florida
Rajendra Rana Bhat - Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida, Gainesville, Florida
Xiaolin Li - Large-scale Intelligent Systems Laboratory, NSF Center for Big Learning, University of Florida, Gainesville, Florida
Lipika R Pal - Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
Kunal Kundu - Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
Yizhou Yin - Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland
John Moult - Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland
Yuxiang Jiang - Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana
Vikas Pejaver - Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington
Kymberleigh A Pagel - Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana
Biao Li - Gilead Sciences, Foster City, California
Sean D Mooney - Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington
Predrag Radivojac - Department of Computer Science and Informatics, Indiana University, Bloomington, Indiana
Sohela Shah - Qiagen Bioinformatics, Redwood City, California
Marco Carraro - Department of Biomedical Science, University of Padova, Padova, Italy
Alessandra Gasparini - Department of Woman and Child Health, University of Padova, Padova, Italy
Emanuela Leonardi - Department of Woman and Child Health, University of Padova, Padova, Italy
Manuel Giollo - Department of Information Engineering, University of Padova, Padova, Italy
Carlo Ferrari - Department of Information Engineering, University of Padova, Padova, Italy
Silvio C E Tosatto - CNR Institute of Neuroscience, Padova, Italy
Eran Bachar - The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
Johnathan R Azaria - The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
Yanay Ofran - The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
Ron Unger - The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
Abhishek Niroula - Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
Mauno Vihinen - Protein Structure and Bioinformatics Group, Department of Experimental Medical Science, Lund University, Lund, Sweden
Billy Chang - Division of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, Chinese University of Hong Kong, Shatin, N.T., Hong Kong
Maggie H Wang - CUHK Shenzhen Research Institute, Shenzhen, China
Andre Franke - Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany
Britt-Sabina Petersen - Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel, Kiel, Germany
Mehdi Pirooznia - Department of Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland
Peter Zandi - Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
Richard McCombie - Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
James B Potash - Department of Psychiatry, University of Iowa, Iowa City, Iowa
Russ B Altman - Department of Genetics, Stanford School of Medicine, Stanford, California
Teri E Klein - Department of Genetics, Stanford School of Medicine, Stanford, California
Roger A Hoskins - Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California
Susanna Repo - Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California
Steven E Brenner - Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California
Alexander A Morgan - Stanford School of Medicine, Stanford, California
Publication Details: Human mutation, Vol.38(9), pp.1182-1192
Date published: 09/2017
Grant note: U41 HG007346 / NHGRI NIH HHS R24 GM061374 / NIGMS NIH HHS R01 HG008754 / NHGRI NIH HHS R01 GM110240 / NIGMS NIH HHS U01 GM115486 / NIGMS NIH HHS U24 MH068457 / NIMH NIH HHS R01 MH105524 / NIMH NIH HHS R13 HG006650 / NHGRI NIH HHS
Academic Unit: Biochemistry and Microbiology (SEBS)
Language: English
Resource Type: Journal article
Identifiers: 991031666362004646