AlphaGenome Atlas: in silico mutagenesis of the entire human genome improves prioritization and interpretation of non-coding variants
Year: 2026
Type: preprint
Abstract: A major challenge in genomics is deciphering the functional consequences of non-coding genetic variation. Here we present AlphaGenome Atlas, a comprehensive resource that enables the joint interpretation and prioritization of variant effects across the entire human genome. Using AlphaGenome, we predicted the regulatory effects across thousands of molecular phenotypes for every possible human single nucleotide variant and many observed indels. These predictions were then used to derive a unified and interpretable AlphaGenome Variant Impact (AVI) score and to map cis-regulatory motifs across the genome. AVI achieved state-of-the-art performance across diverse benchmarks with improved prioritization of deleterious non-coding variants. Application of the combined Atlas resource helped solve an epileptic encephalopathy rare disease case, increased the statistical power to detect rare non-coding variants driving population-level phenotypes, and enhanced the mechanistic interpretation of these variants. Thus, AlphaGenome Atlas improves the prioritization and molecular interpretation of non-coding variants with genetic and clinical significance.
Source: medRxiv
Authors: Lead authors:, Jun Cheng, Kyle R. Taylor, Lauren Nicolaisen, Joshua Pan, Clare Bycroft, Matteo Perino, Tom Ward, Gareth Hawkes, Laura E. Covill, Melanie Weilert, Raina W. Thomas, Natasha Latysheva, Contributing authors:, Maile J. Hirschmann, Xi Dawn Chen, Robin N Beaumont, V Kartik Chundru, Michael N Weedon, Simon Bourdareau, Hoyin Chu, Dhavanthi Hariharan, Thais Kagohara, Lucas Tenório, Yosuke Ushigome, Courtney A. Shearer, Barbara Ikica, Ada Fang, Mouad Naciri, Victoria Johnston, Richard Green, Lai Hong Wong, Vincent Dutordoir, Anne Mottram, Adam Gayoso, Eirini Arvaniti, Guido Novati, Supervising authors:, Heidi L. Rehm, Fei Chen, Caleb A. Lareau, Caroline F Wright, Anne O’Donnell-Luria, Julia Zeitlinger, Pushmeet Kohli, Žiga Avsec
Institutions: Google DeepMind (United Kingdom), Google (United Kingdom), University of Exeter, Broad Institute, Boston Children's Hospital, Harvard University, Manton Center for Orphan Disease Research, Massachusetts Institute of Technology, Stowers Institute for Medical Research, Memorial Sloan Kettering Cancer Center, Google (Switzerland), Massachusetts General Hospital, University of Kansas Medical Center
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: New York Stem Cell Foundation, Stowers Institute for Medical Research, Wellcome Trust, National Institute for Health and Care Research, Department of Health and Social Care, National Institutes of Health, Manton Center for Orphan Disease Research, Boston Children's Hospital, Medical Research Council, National Human Genome Research Institute, National Cancer Institute
Awards: G3292477496, Primary Annotated Resources to Advance Discovery In Genomic Medicine (PARADIGM), G3450814, Using whole genome sequencing to identify non-coding elements associated with diabetes and related traits across ancestries, Deciphering the Non-Coding Genome using Millions of Diverse Whole-Genome Sequences, G8407828443, Primary Annotated Resources to Advance Discovery In Genomic Medicine (PARADIGM), G9000103404, Cancer Center Support Grant, Charting somatic evolution via single-cell multiomics, Broad Institute Mendelian Genomic Research Center
OpenAlex ID: W7213745552