News · 2026-11-12

DeepMind's AlphaGenome 2 finds 30% of causal variants on its own immune-cell benchmark, up from 16%

Google DeepMind said on November 11, 2026 that AlphaGenome 2, its updated DNA-sequence model, identified 30% of causal genetic variants on an immune-cell benchmark against 16% for comparison methods. The figure is DeepMind's own computational result; the post and the accompanying paper do not report a laboratory test of it, and we found no independent check.

Key facts

• Headline number: on the TenK10K benchmark, DeepMind says the model found "nearly twice as many causal variants (30% vs 16%) and directional gene effects (43% vs 23%)".
• Who and when: Google DeepMind, blog post of November 11, 2026.
• Training data: about 107 million single-cell profiles from human and mouse; the original model used bulk tissue data.
• Primary source: DeepMind's AlphaGenome 2 post and its paper PDF.

What changed in the model

The first AlphaGenome predicted thousands of tissue tracks in parallel from bulk measurements. AlphaGenome 2 is a conditional model: it predicts for one tissue, cell type or state at a time, with that condition fed in at every stage. It was trained on roughly 107 million single-cell profiles from human and mouse and adds a new readout, DNA methylation. It belongs to the family of genome language models.

The most practical addition is handling cell states the model never saw. A researcher uploads single-cell RNA-sequencing data from "a few dozen cells". The model compresses it into a cell-state vector and conditions on that, with no retraining. DeepMind says it "can match or outperform" models trained directly on those datasets. Sarah Teichmann, co-founder of the Human Cell Atlas at Cambridge, is quoted in the post saying it "adapts directly to new single-cell data without retraining". That is praise DeepMind chose to publish, not an independent evaluation.

What the headline number is, and is not

TenK10K is described as "a large single-cell map of 28 primary immune cell types". The comparison is against "the original AlphaGenome fine-tuned on single-cell data and other specialized tools". That is why Reddit's phrasing, "up from 16%", is loose: the baseline is not the plain original model. The page we could read does not say what counts as "identified" (top-ranked, recall at a threshold, or a fine-mapping cutoff), what defines "causal" in the ground truth, or which specialized tools were beaten. Our inference, not stated by DeepMind, is that such labels usually come from statistical fine-mapping of population data rather than lab edits of each variant. We did not open the paper PDF, so definitions and error bars are unverified.

Even the improved number means roughly seven in ten causal variants are still missed.

The two case studies

In inflammatory bowel disease, the model reproduced a published finding that a distant regulatory variant overactivates the ETS2 gene in gut macrophages (the Nature paper is paywalled and we did not read it). In DeepMind's podcast, the team says the variant sits in a region with no nearby genes, the model predicted it affects a gene 290,000 base pairs away, and a group at the Crick Institute "validated that". They describe it as "retrospectively validating the results of this other study". So the model matched an existing lab result; it did not make a new prediction later confirmed at the bench.

The second example, on unseen mRNA-vaccine data, found a severe-COVID-linked variant disrupts gene activity more strongly in activated T cells. The post reports no experimental check of that prediction. The one experimentally confirmed case the team describes is from the first AlphaGenome: a top-ranked splicing variant in a rare epilepsy case, confirmed with a minigene splicing assay.

Stated limits

On the podcast, the researchers say the tool is for research, "not a clinical tool". Training is biased toward blood and immune cells because that data is abundant, and rare cell states, for instance in lung, are hard; their answer is the plug-in-your-own-data feature. They frame the underlying difficulty as linkage disequilibrium: variants are inherited in chunks, so association studies cannot separate the causal variant from its neighbours, and modeling is meant to help disentangle them.

Access and reception

Access is non-commercial research through the AlphaGenome API, with a private preview for commercial research. Two r/singularity threads carried the news, but we only have their titles. We found no independent expert checks, no external replication, and did not read the Nature paper, so what remains is a strong lab claim that outsiders have not yet had time to test.

Primary source, verified: read the paper →

Ground Truth. (2026, November 12). DeepMind's AlphaGenome 2 finds 30% of causal variants on its own immune-cell benchmark, up from 16%. Ground Truth. https://groundtruth.day/news/alphagenome-2-nearly-doubles-causal-variant-hits-on-an-immune-cell-benchmark.html

@misc{groundtruth:alphagenome-2-nearly-doubles-causal-variant-hits-on-an-immune-cell-benchmark,
  title  = {DeepMind's AlphaGenome 2 finds 30% of causal variants on its own immune-cell benchmark, up from 16%},
  author = {{Ground Truth}},
  year   = {2026},
  month  = {nov},
  url    = {https://groundtruth.day/news/alphagenome-2-nearly-doubles-causal-variant-hits-on-an-immune-cell-benchmark.html}
}

Topics: google-deepmind · alphagenome · genomics · biology · single-cell · science