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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

Voskanian, Alin and Katsonis, Panagiotis and Lichtarge, Olivier and Pejaver, Vikas and Radivojac, Predrag and Mooney, Sean D and Capriotti, Emidio and Bromberg, Yana and Wang, Yanran and Miller, Max and Martelli, Pier Luigi and Savojardo, Castrense and Babbi, Giulia and Casadio, Rita and Cao, Yue and Sun, Yuanfei and Shen, Yang and Garg, Aditi and Pal, Debnath and Yu, Yao and Huff, Chad D and Tavtigian, Sean and Young, Erin and Neuhausen, Susan L and Ziv, Elad and Pal, Lipika R and Andreoletti, Gaia and Brenner, Steven E and Kann, Maricel G (2019) Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer. In: HUMAN MUTATION, 40 (9, SI). pp. 1612-1622.

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Official URL: http:/dx.doi.org/10.1002/humu.23849


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the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods.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

Item Type: Journal Article
Publisher: WILEY
Additional Information: Copyright of this article belongs to WILEY
Keywords: breast cancer; CAGI; CHEK2; Hispanic women; SNV
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 06 Dec 2019 05:56
Last Modified: 06 Dec 2019 05:56
URI: http://eprints.iisc.ac.in/id/eprint/63868

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