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

Alin Voskanian; Panagiotis Katsonis; Olivier Lichtarge; Vikas Pejaver; Predrag Radivojac; Sean D Mooney; Emidio Capriotti; Yana Bromberg; Yanran Wang; Max Miller; Pier Luigi Martelli; Castrense Savojardo; Giulia Babbi; Rita Casadio; Yue Cao; Yuanfei Sun; Yang Shen; Aditi Garg; Debnath Pal; Yao Yu; Chad D Huff; Sean V Tavtigian; Erin Young; Susan L Neuhausen; Elad Ziv; Lipika R Pal; Gaia Andreoletti; Steven E Brenner; Maricel G Kann

doi:10.1002/humu.23849

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

Journal article

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Peer reviewed

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

Alin Voskanian, Panagiotis Katsonis, Olivier Lichtarge, Vikas Pejaver, Predrag Radivojac, Sean D Mooney, Emidio Capriotti, Yana Bromberg, Yanran Wang, Max Miller, …

Human mutation, Vol.40(9), pp.1612-1622

09/2019

DOI: https://doi.org/10.1002/humu.23849

PMID: 31241222

Abstract

Adult

Aged

Breast Neoplasms - ethnology

Breast Neoplasms - genetics

Case-Control Studies

Checkpoint Kinase 2 - genetics

Computational Biology - methods

Computer Simulation

Female

Genetic Predisposition to Disease

Hispanic Americans - genetics

Humans

Linear Models

Middle Aged

Polymorphism, Single Nucleotide

United States - ethnology

Whole Exome Sequencing

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the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods.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.

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Title: Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer
Creators: Alin Voskanian - Department of Biological Sciences, University of Maryland, Baltimore, Maryland
Panagiotis Katsonis - Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
Olivier Lichtarge - Department of Pharmacology, Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas
Vikas Pejaver - The eScience Institute, University of Washington, Seattle, Washington
Predrag Radivojac - Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts
Sean D Mooney - Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington
Emidio Capriotti - BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Bologna, Italy
Yana Bromberg - Institute for Advanced Study, Technical University of Munich, Garching, Germany
Yanran Wang - Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
Max Miller - Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey
Pier Luigi Martelli - Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Bologna, Italy
Castrense Savojardo - Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Bologna, Italy
Giulia Babbi - Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Bologna, Italy
Rita Casadio - Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Bologna, Italy
Yue Cao - Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
Yuanfei Sun - Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
Yang Shen - Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
Aditi Garg - Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
Debnath Pal - Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
Yao Yu - Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas
Chad D Huff - Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas
Sean V Tavtigian - Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
Erin Young - Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
Susan L Neuhausen - Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California
Elad Ziv - Division of General Internal Medicine, Department of Medicine, Institute of Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California
Lipika R Pal - Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland
Gaia Andreoletti - Department of Plant and Microbial Biology, University of California, Berkeley, California
Steven E Brenner - Department of Plant and Microbial Biology, University of California, Berkeley, California
Maricel G Kann - Department of Biological Sciences, University of Maryland, Baltimore, Maryland
Publication Details: Human mutation, Vol.40(9), pp.1612-1622
Date published: 09/2019
Grant note: GM079656 / NIH HHS R35 GM124952 / NIGMS NIH HHS R01 GM104390 / NIGMS NIH HHS R13 HG006650 / NIH HHS R35GM124952 / NIH HHS R01GM104390 / Andrew Sabin Family Foundation Fellowships U01 GM115486 / NIGMS NIH HHS R01 GM066099 / NIGMS NIH HHS U01GM115486 / NIH HHS U41 HG007346 / NHGRI NIH HHS R01CA CA184585 / NIH HHS R01 CA184585 / NCI NIH HHS GM066099 / NIH HHS R01 GM079656 / NIGMS NIH HHS R13 HG006650 / NHGRI NIH HHS
Academic Unit: Biochemistry and Microbiology (SEBS)
Language: English
Resource Type: Journal article
Identifiers: 991031665547304646