TY - JOUR
T1 - CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
AU - The Critical Assessment of Genome Interpretation Consortium
AU - Jain, Shantanu
AU - Bakolitsa, Constantina
AU - Brenner, Steven E.
AU - Radivojac, Predrag
AU - Moult, John
AU - Repo, Susanna
AU - Hoskins, Roger A.
AU - Andreoletti, Gaia
AU - Barsky, Daniel
AU - Chellapan, Ajithavalli
AU - Chu, Hoyin
AU - Dabbiru, Navya
AU - Kollipara, Naveen K.
AU - Ly, Melissa
AU - Neumann, Andrew J.
AU - Pal, Lipika R.
AU - Odell, Eric
AU - Pandey, Gaurav
AU - Peters-Petrulewicz, Robin C.
AU - Srinivasan, Rajgopal
AU - Yee, Stephen F.
AU - Yeleswarapu, Sri Jyothsna
AU - Zuhl, Maya
AU - Adebali, Ogun
AU - Patra, Ayoti
AU - Beer, Michael A.
AU - Hosur, Raghavendra
AU - Peng, Jian
AU - Bernard, Brady M.
AU - Berry, Michael
AU - Dong, Shengcheng
AU - Boyle, Alan P.
AU - Adhikari, Aashish
AU - Chen, Jingqi
AU - Hu, Zhiqiang
AU - Wang, Robert
AU - Wang, Yaqiong
AU - Miller, Maximilian
AU - Wang, Yanran
AU - Bromberg, Yana
AU - Turina, Paola
AU - Capriotti, Emidio
AU - Han, James J.
AU - Ozturk, Kivilcim
AU - Carter, Hannah
AU - Babbi, Giulia
AU - Bovo, Samuele
AU - Di Lena, Pietro
AU - Martelli, Pier Luigi
AU - Gagneur, Julien
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85187866396&partnerID=8YFLogxK
U2 - 10.1186/s13059-023-03113-6
DO - 10.1186/s13059-023-03113-6
M3 - Article
C2 - 38389099
AN - SCOPUS:85187866396
SN - 1474-7596
VL - 25
JO - Genome Biology
JF - Genome Biology
IS - 1
M1 - 53
ER -