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Critical Assessment of Genome Interpretation Consortium (Müller, N.S.) ; Critical Assessment of Genome Interpretation Consortium (Eraslan, G.)

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.

Genome Biol. 25:46 (2024)

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Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. Results: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. Conclusions: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords In-silico Tools; Assessing Predictions; Evolutionary Action; Missense Variants; Protein-structure; Functional Predictions; Bipolar Disorder; Crohns-disease; Pathogenicity; Performance

ISSN (print) / ISBN 1474-760X

e-ISSN 1465-6906

Journal Genome Biology

Quellenangaben Volume: 25, Issue: 1, Article Number: 46

Publisher BioMed Central

Publishing Place Campus, 4 Crinan St, London N1 9xw, England

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)

Grants NHGRI