研究小组描述了PromoterAI，这是一个深度神经网络，可以准确识别非编码启动子变异，从而失调基因表达。该课题组人员表明，具有预测表达改变后果的启动子变体在个体的RNA和蛋白质水平上产生异常表达，并且这些变体在人类群体中经历了强烈的负选择。该课题组观察到罕见病患者的临床相关基因中丰富了这些变异，并通过报告者试验验证了它们的功能影响。他们的估计表明，启动子变异占与罕见疾病相关的遗传负担的6%。
研究人员表示，目前，只有少数罕见遗传病患者通过外显子组测序得到诊断，这表明其他未被识别的致病变异可能存在于非编码序列中。
附：英文原文
Title: Predicting expression-altering promoter mutations with deep learning
Author: Kishore Jaganathan, Nicole Ersaro, Gherman Novakovsky, Yuchuan Wang, Terena James, Jeremy Schwartzentruber, Petko Fiziev, Irfahan Kassam, Fan Cao, Johann Hawe, Henry Cavanagh, Ashley Lim, Grace Png, Jeremy McRae, Abhimanyu Banerjee, Arvind Kumar, Jacob Ulirsch, Yan Zhang, Francois Aguet, Pierrick Wainschtein, Laksshman Sundaram, Adriana Salcedo, Sofia Kyriazopoulou Panagiotopoulou, Delasa Aghamirzaie, Evin Padhi, Ziming Weng, Shan Dong, Damian Smedley, Mark Caulfield, Anne O’Donnell-Luria, Heidi L. Rehm, Stephan J. Sanders, Anshul Kundaje, Stephen B. Montgomery, Mark T. Ross, Kyle Kai-How Farh
Issue&Volume: 2025-05-29
Abstract: Only a minority of patients with rare genetic diseases are currently diagnosed by exome sequencing, suggesting that additional unrecognized pathogenic variants may reside in non-coding sequence. Here, we describe PromoterAI, a deep neural network that accurately identifies non-coding promoter variants which dysregulate gene expression. We show that promoter variants with predicted expression-altering consequences produce outlier expression at both RNA and protein levels in thousands of individuals, and that these variants experience strong negative selection in human populations. We observe that clinically relevant genes in rare disease patients are enriched for such variants and validate their functional impact through reporter assays. Our estimates suggest that promoter variation accounts for 6% of the genetic burden associated with rare diseases.
DOI: ads7373
Source: https://www.science.org/doi/10.1126/science.ads7373