Literature DB >> 28397312

Lessons from the CAGI-4 Hopkins clinical panel challenge.

John-Marc Chandonia1, Aashish Adhikari2, Marco Carraro3, Aparna Chhibber4, Garry R Cutting5, Yao Fu4, Alessandra Gasparini3,6, David T Jones7, Andreas Kramer8, Kunal Kundu9,10, Hugo Y K Lam4, Emanuela Leonardi6, John Moult9,11, Lipika R Pal9, David B Searls12, Sohela Shah8, Shamil Sunyaev13,14, Silvio C E Tosatto3,15, Yizhou Yin9,10, Bethany A Buckley5.   

Abstract

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  CAGI; genetic testing; phenotype prediction; variant interpretation

Mesh:

Year:  2017        PMID: 28397312      PMCID: PMC5600166          DOI: 10.1002/humu.23225

Source DB:  PubMed          Journal:  Hum Mutat        ISSN: 1059-7794            Impact factor:   4.878


  27 in total

1.  Estimating the support of a high-dimensional distribution.

Authors:  B Schölkopf; J C Platt; J Shawe-Taylor; A J Smola; R C Williamson
Journal:  Neural Comput       Date:  2001-07       Impact factor: 2.026

Review 2.  Computational Approach to Annotating Variants of Unknown Significance in Clinical Next Generation Sequencing.

Authors:  Wade L Schulz; Christopher A Tormey; Richard Torres
Journal:  Lab Med       Date:  2015

3.  Reassessment of Genomic Sequence Variation to Harmonize Interpretation for Personalized Medicine.

Authors:  Kathryn B Garber; Lisa M Vincent; John J Alexander; Lora J H Bean; Sherri Bale; Madhuri Hegde
Journal:  Am J Hum Genet       Date:  2016-10-27       Impact factor: 11.025

Review 4.  Crowdsourcing biomedical research: leveraging communities as innovation engines.

Authors:  Julio Saez-Rodriguez; James C Costello; Stephen H Friend; Michael R Kellen; Lara Mangravite; Pablo Meyer; Thea Norman; Gustavo Stolovitzky
Journal:  Nat Rev Genet       Date:  2016-07-15       Impact factor: 53.242

5.  Genetic Misdiagnoses and the Potential for Health Disparities.

Authors:  Arjun K Manrai; Birgit H Funke; Heidi L Rehm; Morten S Olesen; Bradley A Maron; Peter Szolovits; David M Margulies; Joseph Loscalzo; Isaac S Kohane
Journal:  N Engl J Med       Date:  2016-08-18       Impact factor: 91.245

6.  Predicting functional effect of human missense mutations using PolyPhen-2.

Authors:  Ivan Adzhubei; Daniel M Jordan; Shamil R Sunyaev
Journal:  Curr Protoc Hum Genet       Date:  2013-01

Review 7.  Molecular genetic testing and the future of clinical genomics.

Authors:  Sara Huston Katsanis; Nicholas Katsanis
Journal:  Nat Rev Genet       Date:  2013-06       Impact factor: 53.242

8.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders.

Authors:  Ada Hamosh; Alan F Scott; Joanna S Amberger; Carol A Bocchini; Victor A McKusick
Journal:  Nucleic Acids Res       Date:  2005-01-01       Impact factor: 16.971

9.  Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples.

Authors:  Roddy Walsh; Kate L Thomson; James S Ware; Birgit H Funke; Jessica Woodley; Karen J McGuire; Francesco Mazzarotto; Edward Blair; Anneke Seller; Jenny C Taylor; Eric V Minikel; Daniel G MacArthur; Martin Farrall; Stuart A Cook; Hugh Watkins
Journal:  Genet Med       Date:  2016-08-17       Impact factor: 8.822

10.  An integrated map of genetic variation from 1,092 human genomes.

Authors:  Goncalo R Abecasis; Adam Auton; Lisa D Brooks; Mark A DePristo; Richard M Durbin; Robert E Handsaker; Hyun Min Kang; Gabor T Marth; Gil A McVean
Journal:  Nature       Date:  2012-11-01       Impact factor: 49.962

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  3 in total

1.  Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge.

Authors:  Marco Carraro; Alexander Miguel Monzon; Luigi Chiricosta; Francesco Reggiani; Maria Cristina Aspromonte; Mariagrazia Bellini; Kymberleigh Pagel; Yuxiang Jiang; Predrag Radivojac; Kunal Kundu; Lipika R Pal; Yizhou Yin; Ivan Limongelli; Gaia Andreoletti; John Moult; Stephen J Wilson; Panagiotis Katsonis; Olivier Lichtarge; Jingqi Chen; Yaqiong Wang; Zhiqiang Hu; Steven E Brenner; Carlo Ferrari; Alessandra Murgia; Silvio C E Tosatto; Emanuela Leonardi
Journal:  Hum Mutat       Date:  2019-07-03       Impact factor: 4.878

2.  Reports from CAGI: The Critical Assessment of Genome Interpretation.

Authors:  Roger A Hoskins; Susanna Repo; Daniel Barsky; Gaia Andreoletti; John Moult; Steven E Brenner
Journal:  Hum Mutat       Date:  2017-09       Impact factor: 4.878

Review 3.  Genome interpretation using in silico predictors of variant impact.

Authors:  Panagiotis Katsonis; Kevin Wilhelm; Amanda Williams; Olivier Lichtarge
Journal:  Hum Genet       Date:  2022-04-30       Impact factor: 5.881

  3 in total

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