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Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open
Journal article
CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Genome Biology, Vol.25(1), pp.1-46
22/02/2024
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Source: InCites
Abstract
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.
Details
- Title
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
- Creators
- The Critical Assessment of Genome Interpretation ConsortiumLukas Folkman - Griffith University
- Publication Details
- Genome Biology, Vol.25(1), pp.1-46
- Publisher
- Springer Nature
- Grant note
- NHGRI
The authors are grateful for the contributions of Talal Amin, Patricia Babbitt, Eran Bachar, Stefania Boni, Kirstine Calloe, Ombretta Carlet, Shann-Ching Chen, Chien-Yuan Chen, Jun Cheng, Luigi Chiricosta, Alex Colavin, Qian Cong, Emma D'Andrea, Carla Davis, Xin Feng, Carlo Ferrari, Yao Fu, Alessandra Gasparini, David Goldgar, Solomon Grant, Steve Grossman, Todd Holyoak, Xiaolin Li, Quewang Liu, Beth Martin, Zev Medoff, Nasim Monfared, Susanna Negrin, Michael Parsons, Nathan Pearson, Alexandra Piryatinska, Catherine Plotts, Jennifer Poitras, Clive Pulinger, Francesco Reggiani, Melvin M. Scheinman, George Shackelford, Vasily Sitnik, Fiorenza Soli, Qingling Tang, Nancy Mutsaers Thomsen, Jing Wang, Chenling Xiong, Lijing Xu, Shuhan Yang, Lijun Zhan, and Huiying Zhao.
- Identifiers
- 991013341085602368
- Copyright
- © The Author(s) 2024.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article