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Hum Mutat. 2019 Sep;40(9):1612-1622. doi: 10.1002/humu.23849. Epub 2019 Aug 17.

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.

Author information

1
Department of Biological Sciences, University of Maryland, Baltimore, Maryland.
2
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
3
Department of Pharmacology, Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas.
4
Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington.
5
The eScience Institute, University of Washington, Seattle, Washington.
6
Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts.
7
BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy.
8
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.
9
Department of Genetics, Rutgers University, New Brunswick, New Jersey.
10
Institute for Advanced Study, Technical University of Munich, Garching, Germany.
11
Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Bologna, Italy.
12
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.
13
Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India.
14
Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas.
15
Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah.
16
Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California.
17
Division of General Internal Medicine, Department of Medicine, Institute of Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California.
18
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
19
Department of Plant and Microbial Biology, University of California, Berkeley, California.

Abstract

The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.

KEYWORDS:

CAGI; CHEK2; Hispanic women; SNV; breast cancer

PMID:
31241222
PMCID:
PMC6744287
[Available on 2020-09-01]
DOI:
10.1002/humu.23849

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