Predicting expression-altering promoter mutations with deep learning.
| Title: | Predicting expression-altering promoter mutations with deep learning. |
|---|---|
| Authors: | Jaganathan K; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Ersaro N; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Novakovsky G; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Wang Y; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; James T; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Schwartzentruber J; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Fiziev P; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Kassam I; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Cao F; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Hawe J; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Cavanagh H; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Lim A; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Png G; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; McRae J; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Banerjee A; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Kumar A; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Ulirsch J; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Zhang Y; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Aguet F; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Wainschtein P; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Sundaram L; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Salcedo A; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Panagiotopoulou SK; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Aghamirzaie D; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Padhi E; Department of Pathology, Stanford University, Stanford, CA, USA.; Weng Z; Department of Pathology, Stanford University, Stanford, CA, USA.; Dong S; Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.; Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK.; Smedley D; William Harvey Research Institute, Queen Mary University of London, London, UK.; Caulfield M; William Harvey Research Institute, Queen Mary University of London, London, UK.; O'Donnell-Luria A; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.; Rehm HL; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.; Center for Genomic Medicine and Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.; Sanders SJ; Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.; Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK.; New York Genome Center, New York, NY, USA.; Kundaje A; Department of Genetics, Stanford University, Stanford, CA, USA.; Department of Computer Science, Stanford University, Stanford, CA, USA.; Montgomery SB; Department of Pathology, Stanford University, Stanford, CA, USA.; Department of Genetics, Stanford University, Stanford, CA, USA.; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.; Ross MT; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA.; Farh KK; Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA. |
| Source: | Science (New York, N.Y.) [Science] 2025 Aug 07; Vol. 389 (6760), pp. eads7373. Date of Electronic Publication: 2025 Aug 07. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 0404511 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9203 (Electronic) Linking ISSN: 00368075 NLM ISO Abbreviation: Science Subsets: MEDLINE |
| Imprint Name(s): | Publication: : Washington, DC : American Association for the Advancement of Science; Original Publication: New York, N.Y. : [s.n.] 1880- |
| MeSH Terms: | Rare Diseases*/genetics ; Deep Learning* ; Gene Expression Regulation* ; Mutation* ; Promoter Regions, Genetic*; Humans ; Exome Sequencing |
| Abstract: | 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. |
| Entry Date(s): | Date Created: 20250529 Date Completed: 20250807 Latest Revision: 20250808 |
| Update Code: | 20260130 |
| DOI: | 10.1126/science.ads7373 |
| PMID: | 40440429 |
| Database: | MEDLINE |
Journal Article