# AlphaGenome

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bioRxiv Oct 19, 2026
Genomic foundation models fail to add common-variant discoveries: standard annotations still lead across 27 UK Biobank traits
Across 27 UK Biobank traits, variant scores from genomic foundation models (GFMs) did not yield more common-variant loci, either alone or added to standard functional annotations. AlphaGenome and Borzoi alone raised locus discovery by median 1.8% and 3.8%, below baselineLD's 5.1%, while GPN-Star reduced it. Combined with baselineLD, locus counts changed by a median of −0.1% to 0.2%, suggesting the models largely recapture regulatory information already in the standard annotations.
Across 27 UK Biobank traits, variant scores from genomic foundation models (GFMs) did not yield more common-variant loci, either alone or added to standard functional annotations. AlphaGenome and Borzoi alone raised locus discovery by median 1.8% and 3.8%, below baselineLD's 5.1%, while GPN-Star reduced it. Combined with baselineLD, locus counts changed by a median of −0.1% to 0.2%, suggesting the models largely recapture regulatory information already in the standard annotations.
University of Cambridge University of Bonn
Andrew J. Bass（First author, Corresponding author）Chris Wallace（Corresponding author）
Surrogate Functional False Discovery Rate (sfFDR)Genomic Foundation Models Common Variant GWAS Discovery AlphaGenome Borzoi
Nature News Sep 21, 2026
Turning a paper into a conversational agent: reading the Paper2Agent report
According to a Nature news report, Paper2Agent reads a paper's main text, code and data sets, deposits them on an MCP server, and has a team of AI agents autonomously build tools that apply the paper's methods, producing a paper-specific agent that can be questioned in plain language as a 'virtual corresponding author'; the report says it created an agent for the AlphaGenome paper in about 45 minutes at US$14 of computing cost, that the agent answered genetics questions with near-perfect accuracy and outscored other top biomedical AI agents including Biomni, and that it was used to re-examine which causal gene explains a single-letter DNA change linked to 'bad' cholesterol, arriving at a different gene from the one pinpointed in the original paper.
According to a Nature news report, Paper2Agent reads a paper's main text, code and data sets, deposits them on an MCP server, and has a team of AI agents autonomously build tools that apply the paper's methods, producing a paper-specific agent that can be questioned in plain language as a 'virtual corresponding author'; the report says it created an agent for the AlphaGenome paper in about 45 minutes at US$14 of computing cost, that the agent answered genetics questions with near-perfect accuracy and outscored other top biomedical AI agents including Biomni, and that it was used to re-examine which causal gene explains a single-letter DNA change linked to 'bad' cholesterol, arriving at a different gene from the one pinpointed in the original paper.
Stanford University
Paper2Agent AI agent MCP server AlphaGenome
medRxiv Sep 16, 2026
Genomic foundation model-derived disruption profiling links somatic mutations to cancer biology and clinical outcomes
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
Using AlphaGenome and AlphaMissense to quantify the disruption imposed by somatic mutations across 8,800 patients and 33 cancer types from The Cancer Genome Atlas, this study found that recurrent hotspot mutations showed substantially larger predicted protein-level effects while non-hotspot mutations exhibited larger regulatory effects across most cancer types; aggregating variant-level predictions into patient-gene disruption profiles capturing transcriptional activity, chromatin accessibility, transcription factor binding, and splicing yielded gene- and modality-specific profiles that reflected tissue of origin, cancer type, and microsatellite-instability status while retaining information beyond tumor mutational burden; among patients lacking recurrent hotspot mutations, higher predicte
The University of Texas at Austin
Akshatha Nayak（First author）Ilias Georgakopoulos‐Soares（Corresponding author）
AlphaGenome AlphaMissense patient-gene disruption profiles cancer genomics

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Genomic foundation models fail to add common-variant discoveries: standard annotations still lead across 27 UK Biobank traits

Turning a paper into a conversational agent: reading the Paper2Agent report

Genomic foundation model-derived disruption profiling links somatic mutations to cancer biology and clinical outcomes