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

The Critical Assessment of Genome Interpretation Consortium, Shantanu Jain, Constantina Bakolitsa, Steven E. Brenner, Predrag Radivojac, John Moult, Susanna Repo, Roger A. Hoskins, Gaia Andreoletti, Daniel Barsky, Ajithavalli Chellapan, Hoyin Chu, Navya Dabbiru, Naveen K. Kollipara, Melissa Ly, Andrew J. Neumann, Lipika R. Pal, Eric Odell, Gaurav Pandey, Robin C. Peters-PetrulewiczRajgopal Srinivasan, Stephen F. Yee, Sri Jyothsna Yeleswarapu, Maya Zuhl, Ogun Adebali, Ayoti Patra, Michael A. Beer, Raghavendra Hosur, Jian Peng, Brady M. Bernard, Michael Berry, Shengcheng Dong, Alan P. Boyle, Aashish Adhikari, Jingqi Chen, Zhiqiang Hu, Robert Wang, Yaqiong Wang, Maximilian Miller, Yanran Wang, Yana Bromberg, Paola Turina, Emidio Capriotti, James J. Han, Kivilcim Ozturk, Hannah Carter, Giulia Babbi, Samuele Bovo, Pietro Di Lena, Pier Luigi Martelli, Castrense Savojardo, Rita Casadio, Melissa S. Cline, Greet De Baets, Sandra Bonache, Orland Díez, Sara Gutiérrez-Enríquez, Alejandro Fernández, Gemma Montalban, Lars Ootes, Selen Özkan, Natàlia Padilla, Casandra Riera, Xavier De la Cruz, Mark Diekhans, Peter J. Huwe, Qiong Wei, Qifang Xu, Roland L. Dunbrack, Valer Gotea, Laura Elnitski, Gennady Margolin, Piero Fariselli, Ivan V. Kulakovskiy, Vsevolod J. Makeev, Dmitry D. Penzar, Ilya E. Vorontsov, Alexander V. Favorov, Julia R. Forman, Marcia Hasenahuer, Maria S. Fornasari, Gustavo Parisi, Ziga Avsec, Muhammed H. Çelik, Thi Yen Duong Nguyen, Julien Gagneur, Fang Yuan Shi, Matthew D. Edwards, Yuchun Guo, Kevin Tian, Haoyang Zeng, David K. Gifford, Jonathan Göke, Jan Zaucha, Julian Gough, Graham R.S. Ritchie, Adam Frankish, Jonathan M. Mudge, Jennifer Harrow, Erin L. Young, Yao Yu, Chad D. Huff, Katsuhiko Murakami, Yoko Nagai, Tadashi Imanishi, Christopher J. Mungall, Julius O.B. Jacobsen, Dongsup Kim, Chan Seok Jeong, David T. Jones, Mulin Jun Li, Violeta Beleva Guthrie, Rohit Bhattacharya, Yun Ching Chen, Christopher Douville, Jean Fan, Dewey Kim, David Masica, Noushin Niknafs, Sohini Sengupta, Collin Tokheim, Tychele N. Turner, Hui Ting Grace Yeo, Rachel Karchin, Sunyoung Shin, Rene Welch, Sunduz Keles, Yue Li, Manolis Kellis, Carles Corbi-Verge, Alexey V. Strokach, Philip M. Kim, Teri E. Klein, Rahul Mohan, Nicholas A. Sinnott-Armstrong, Michael Wainberg, Anshul Kundaje, Nina Gonzaludo, Angel C.Y. Mak, Aparna Chhibber, Hugo Y.K. Lam, Dvir Dahary, Simon Fishilevich, Doron Lancet, Insuk Lee, Benjamin Bachman, Panagiotis Katsonis, Rhonald C. Lua, Stephen J. Wilson, Olivier Lichtarge, Rajendra R. Bhat, Laksshman Sundaram, Vivek Viswanath, Riccardo Bellazzi, Giovanna Nicora, Ettore Rizzo, Ivan Limongelli, Aziz M. Mezlini, Ray Chang, Serra Kim, Carmen Lai, Robert O’Connor, Scott Topper, Jeroen van den Akker, Alicia Y. Zhou, Anjali D. Zimmer, Gilad Mishne, Timothy R. Bergquist, Marcus R. Breese, Rafael F. Guerrero, Yuxiang Jiang, Nikki Kiga, Biao Li, Matthew Mort, Kymberleigh A. Pagel, Vikas Pejaver, Moses H. Stamboulian, Janita Thusberg, Sean D. Mooney, Nuttinee Teerakulkittipong, Chen Cao, Kunal Kundu, Yizhou Yin, Chen Hsin Yu, Michael Kleyman, Chiao Feng Lin, Mary Stackpole, Stephen M. Mount, Gökcen Eraslan, Nikola S. Mueller, Tatsuhiko Naito, Aliz R. Rao, Johnathan R. Azaria, Aharon Brodie, Yanay Ofran, Aditi Garg, Debnath Pal, Alex Hawkins-Hooker, Henry Kenlay, John Reid, Eliseos J. Mucaki

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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.

Original languageEnglish
Article number53
JournalGenome biology
Volume25
Issue number1
DOIs
StatePublished - Dec 2024

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