Predicting expression-altering promoter mutations with deep learning

利用深度学习预测改变表达的启动子突变

关键词：

来源：

Science

来源地址：

https://www.science.org/doi/10.1126/science.ads7373

资源所属：

马铃薯基因组设计育种

类型：

前沿资讯

语种：

英语

原文发布日期：

2025-05-29

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.