CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
Jain, S. ; Niroula, A. LU ; Vihinen, M. LU
and Zook, J.M.
(2024)
In Genome Biology
25(1).
- 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... (More)
- 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. © The Author(s) 2024. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/14622294-f25a-471f-8a14-a043cac87a6e
- author
- Jain, S.
; Niroula, A.
LU
; Vihinen, M.
LU
and Zook, J.M.
- author collaboration
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- nucleotide, area under the curve, art, Article, biochemistry, cancer risk, CDKN2A gene, cell growth, CHEK2 gene, clinical practice, complex trait disease risk, computational genetic variant interpretation method, computer model, correlation coefficient, Critical Assessment of Genome Interpretation, Crohn disease, diagnostic accuracy, diagnostic test accuracy study, diseases, DNA repair, DNA splicing, effect size, environmental risk, enzyme activity, FXN gene, gene deletion, gene expression, gene insertion, gene linkage disequilibrium, genetic analysis, genetic database, genetic identification, genetic information, genetic trait, genetic transcription, genetic variability, genetic variation, genome, genome-wide association study, genomic scale, germline mutation, human, intellectual impairment, interpretation bias, malignant neoplasm, medical research, missense mutation, monogenic disorder, MRE11 gene, multiomics, NBS1 gene, NPMALK gene, NSMCE2 gene, pathogenicity, phenotype, practice guideline, prediction, protein function, protein stability, PTEN gene, RAD50 gene, receiver operating characteristic, regulatory mechanism, social aspect, structure analysis, TP53 gene, TPMT gene, tumor suppressor gene, whole organism fitness
- in
- Genome Biology
- volume
- 25
- issue
- 1
- article number
- 53
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:85187866396
- pmid:38389099
- ISSN
- 1474-7596
- DOI
- 10.1186/s13059-023-03113-6
- language
- English
- LU publication?
- yes
- id
- 14622294-f25a-471f-8a14-a043cac87a6e
- date added to LUP
- 2024-04-03 15:53:38
- date last changed
- 2024-04-04 03:00:08
@article{14622294-f25a-471f-8a14-a043cac87a6e,
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. © The Author(s) 2024.}},
author = {{Jain, S. and Niroula, A. and Vihinen, M. and Zook, J.M.}},
issn = {{1474-7596}},
keywords = {{nucleotide; area under the curve; art; Article; biochemistry; cancer risk; CDKN2A gene; cell growth; CHEK2 gene; clinical practice; complex trait disease risk; computational genetic variant interpretation method; computer model; correlation coefficient; Critical Assessment of Genome Interpretation; Crohn disease; diagnostic accuracy; diagnostic test accuracy study; diseases; DNA repair; DNA splicing; effect size; environmental risk; enzyme activity; FXN gene; gene deletion; gene expression; gene insertion; gene linkage disequilibrium; genetic analysis; genetic database; genetic identification; genetic information; genetic trait; genetic transcription; genetic variability; genetic variation; genome; genome-wide association study; genomic scale; germline mutation; human; intellectual impairment; interpretation bias; malignant neoplasm; medical research; missense mutation; monogenic disorder; MRE11 gene; multiomics; NBS1 gene; NPMALK gene; NSMCE2 gene; pathogenicity; phenotype; practice guideline; prediction; protein function; protein stability; PTEN gene; RAD50 gene; receiver operating characteristic; regulatory mechanism; social aspect; structure analysis; TP53 gene; TPMT gene; tumor suppressor gene; whole organism fitness}},
language = {{eng}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{Genome Biology}},
title = {{CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods}},
url = {{http://dx.doi.org/10.1186/s13059-023-03113-6}},
doi = {{10.1186/s13059-023-03113-6}},
volume = {{25}},
year = {{2024}},
}