Predicting expression-altering promoter mutations with deep learning

• 成果类型: Article
• 署名作者: Jaganathan, Kishore; Ersaro, Nicole; Novakovsky, Gherman; Wang, Yuchuan; James, Terena; Schwartzentruber, Jeremy; Fiziev, Petko; Kassam, Irfahan; Cao, Fan; Hawe, Johann; Cavanagh, Henry; Lim, Ashley; Png, Grace; Mcrae, Jeremy; Banerjee, Abhimanyu; Kumar, Arvind; Ulirsch, Jacob; Zhang, Yan; Aguet, Francois; Wainschtein, Pierrick; Sundaram, Laksshman; Salcedo, Adriana; Panagiotopoulou, Sofia Kyriazopoulou; Aghamirzaie, Delasa; Padhi, Evin; Weng, Ziming; Dong, Shan; Smedley, Damian; Caulfield, Mark; O'Donnell-Luria, Anne; Rehm, Heidi L.; Sanders, Stephan J.; Kundaje, Anshul; Montgomery, Stephen B.; Ross, Mark T.; Farh, Kyle Kai-How
• 署名单位: Illumina; Stanford University; University of California System; University of California San Francisco; University of Oxford; University of London; Queen Mary University London; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital; Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital; Harvard University; Harvard Medical School; Harvard University Medical Affiliates; Boston Children's Hospital; Stanford University; Stanford University; Stanford University
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-10099
• DOI: 10.1126/science.ads7373
• 发表日期: 2025-08-07
• 关键词: transcription factor; gene-expression; binding-site; 5'-untranslated region; clinical-diagnosis; causal variants; receptor gene; FAMILY; associations; FRAMEWORK

摘要

Only a minority of patients with rare genetic diseases are presently diagnosed by exome sequencing, suggesting that additional unrecognized pathogenic variants may reside in noncoding sequence. In this work, we describe PromoterAI, a deep neural network that accurately identifies noncoding promoter variants that dysregulate gene expression. We show that promoter variants with predicted expression-altering consequences produce outlier expression at both the RNA and protein levels in thousands of individuals and that these variants experience strong negative selection in human populations. We observed that clinically relevant genes in patients with rare diseases are enriched for such variants and validated their functional impact through reporter assays. Our estimates suggest that promoter variation accounts for 6% of the genetic burden associated with rare diseases.