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Hum Mutat. 2017 Sep;38(9):1155-1168. doi: 10.1002/humu.23225. Epub 2017 Jun 12.

Lessons from the CAGI-4 Hopkins clinical panel challenge.

Author information

1
Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California.
2
Department of Plant and Microbial Biology, University of California, Berkeley, California.
3
Department of Biomedical Sciences, University of Padova, Padova, Italy.
4
Roche Sequencing Solutions, Belmont, California.
5
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
6
Department of Women's and Children's Health, University of Padova, Padova, Italy.
7
Department of Computer Science, University College London, London, United Kingdom.
8
Qiagen Bioinformatics, Redwood City, California.
9
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
10
Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland.
11
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland.
12
Independent Consultant, Philadelphia, Pennsylvania.
13
Division of Genetics, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, Massachusetts.
14
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts.
15
CNR Institute of Neuroscience, Padova, Italy.

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.

KEYWORDS:

CAGI; genetic testing; phenotype prediction; variant interpretation

PMID:
28397312
PMCID:
PMC5600166
DOI:
10.1002/humu.23225
[Indexed for MEDLINE]
Free PMC Article

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