Evidence map›Paper›PMID 42779988›Full record

ArticlemedRxiv : the preprint server for health sciences2026

AlphaGenome Atlas:

Jun Cheng, Kyle R Taylor, Lauren Nicolaisen, Joshua Pan, Clare Bycroft, Matteo Perino, Tom Ward, Gareth Hawkes, Laura E Covill, Melanie Weilert and 33 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

43 authors.

Jun ChengGoogle DeepMind; London, UK.ORCID 0000-0001-5573-9791
Kyle R TaylorGoogle DeepMind; London, UK.ORCID 0000-0002-4586-8300
Lauren NicolaisenGoogle DeepMind; London, UK.ORCID 0009-0008-2974-9222
Joshua PanGoogle DeepMind; London, UK.ORCID 0000-0002-7745-1497
Clare BycroftGoogle DeepMind; London, UK.ORCID 0000-0002-1139-0732
Matteo PerinoGoogle DeepMind; London, UK.ORCID 0000-0001-7173-0252
Tom WardGoogle DeepMind; London, UK.ORCID 0009-0004-6318-7279
Gareth HawkesDepartment of Clinical and Biomedical Sciences, University of Exeter Medical School; Exeter, UK.ORCID 0000-0002-3367-789X
Laura E CovillProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, MA, USA.ORCID 0000-0002-5086-9877
Melanie WeilertStowers Institute for Medical Research; Kansas City, MO, USA.ORCID 0000-0001-8683-4580
Raina W ThomasGoogle DeepMind; London, UK.ORCID 0000-0001-8027-3460
Natasha LatyshevaGoogle DeepMind; London, UK.ORCID 0000-0003-3119-0567
Maile J HirschmannBroad Institute of MIT and Harvard; Cambridge, MA, USA.ORCID 0009-0004-2972-6582
Xi Dawn ChenBroad Institute of MIT and Harvard; Cambridge, MA, USA.ORCID 0000-0001-8127-6014
Robin N BeaumontDepartment of Clinical and Biomedical Sciences, University of Exeter Medical School; Exeter, UK.ORCID 0000-0003-0750-8248
V Kartik ChundruDepartment of Clinical and Biomedical Sciences, University of Exeter Medical School; Exeter, UK.ORCID 0000-0002-6348-5565
Michael N WeedonDepartment of Clinical and Biomedical Sciences, University of Exeter Medical School; Exeter, UK.ORCID 0000-0002-6174-6135
Simon BourdareauStowers Institute for Medical Research; Kansas City, MO, USA.ORCID 0000-0001-9150-5327
Hoyin ChuComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center; New York, NY, USA.ORCID 0000-0001-8630-3667
Dhavanthi HariharanGoogle DeepMind; London, UK.ORCID 0009-0005-8087-4430
Thais KagoharaGoogle DeepMind; London, UK.ORCID 0009-0009-2151-5285
Lucas TenórioGoogle DeepMind; London, UK.ORCID 0009-0009-4706-577X
Yosuke UshigomeGoogle DeepMind; London, UK.ORCID 0009-0005-9182-7816
Courtney A ShearerGoogle DeepMind; London, UK.ORCID 0009-0006-8489-7434
Barbara IkicaGoogle Research; Zürich, Switzerland.ORCID 0000-0002-2367-8610
Ada FangGoogle DeepMind; London, UK.ORCID 0009-0003-7957-1905
Mouad NaciriGoogle DeepMind; London, UK.ORCID 0000-0002-8066-3298
Victoria JohnstonGoogle DeepMind; London, UK.ORCID 0009-0000-3883-4308
Richard GreenGoogle DeepMind; London, UK.ORCID 0009-0000-0760-864X
Lai Hong WongGoogle DeepMind; London, UK.ORCID 0000-0001-8653-2734
Vincent DutordoirGoogle DeepMind; London, UK.ORCID 0009-0001-1252-5723
Anne MottramGoogle DeepMind; London, UK.ORCID 0009-0007-3016-3053
Adam GayosoGoogle DeepMind; London, UK.ORCID 0000-0001-9537-0845
Eirini ArvanitiGoogle DeepMind; London, UK.ORCID 0000-0003-3045-9309
Guido NovatiGoogle DeepMind; London, UK.ORCID 0000-0003-0681-4892
Heidi L RehmProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard; Cambridge, MA, USA.ORCID 0000-0002-6025-0015
Fei ChenBroad Institute of MIT and Harvard; Cambridge, MA, USA.ORCID 0000-0003-2308-3649
Caleb A LareauComputational and Systems Biology Program, Memorial Sloan Kettering Cancer Center; New York, NY, USA.ORCID 0000-0003-4179-4807
Caroline F WrightDepartment of Clinical and Biomedical Sciences, University of Exeter Medical School; Exeter, UK.ORCID 0000-0003-2958-5076
Anne O'Donnell-LuriaThe Manton Center for Orphan Disease Research, Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School; Boston, MA, USA.ORCID 0000-0001-6418-9592
Julia ZeitlingerStowers Institute for Medical Research; Kansas City, MO, USA.ORCID 0000-0002-5172-3335
Pushmeet KohliGoogle DeepMind; London, UK.ORCID 0000-0002-7466-7997
Žiga AvsecGoogle DeepMind; London, UK.ORCID 0000-0002-7790-8936

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Broad Institute Mendelian Genomic Research CenterU01HG011755 · NHGRI · BROAD INSTITUTE, INC. · PI Anne O'Donnell-Luria, MICHAEL E TALKOWSKI · 2021 to 2026
$14.6M
Charting somatic evolution via single-cell multiomicsR00HG012579 · NHGRI · SLOAN-KETTERING INST CAN RESEARCH · PI LAREAU, CALEB ANDREW · 2023 to 2025
$747k
NCI NIH HHS P30 CA008748NHGRI NIH HHS R00 HG012579NHGRI NIH HHS U01 HG011755Wellcome Trust
6 · The paper itself

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.

Identifiers

PMID42779988
PMCPMC13596691

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.