AlphaGenome 2: Deciphering how cells read DNA to better understand disease

AlphaGenome 2: Deciphering how cells read DNA to better understand disease

Our next-generation DNA sequence model bridges single-cell biology and human genetics, predicting how genetic variants affect hundreds of human cell types.

Today, we are announcing AlphaGenome 2, our next-generation model that predicts how DNA sequences and their variations impact specific human cell types, giving scientists a powerful new tool to uncover the cellular drivers of disease.

Cells are the building blocks of life. Every cell in our body carries the same DNA blueprint, yet each cell type and state—like a heart muscle cell or an immune cell fighting an infection—switches different genes on or off to do its job. Global efforts like the Human Cell Atlas are mapping this gene expression for millions of cells, but it is still an unsolved challenge to predict how subtle DNA variations affect this expression in cells, which is a key driver of human traits and disease.

Last year, we launched AlphaGenome to predict how DNA variants affect molecular activity across broad tissues. We expanded its reach with AlphaGenome Atlas, a platform containing predictions for all 9 billion possible single-letter changes in the human genome. These tools help researchers prioritize and interpret variant effects, driving discoveries in rare diseases, complex traits, and fundamental biology.

By zooming in from broad tissue averages to individual cell types and cell states, and redesigning the model’s architecture, AlphaGenome 2 represents a step-change improvement in variant predictive performance. Importantly, it allows researchers to predict variant effects in disease-relevant cells that would be missed in broad-tissue models, as well as predictions for cell and disease states that the model hasn’t seen before.

AlphaGenome 2 identifies nearly twice as many variants that change gene expression in cells, compared to our previous state-of-the-art model. Trained on more than 100 million individual cell profiles across 68 tissues, the model turns static cell atlases into a predictive engine for human genetics. AlphaGenome 2 is available today for non-commercial research via the AlphaGenome API, and in private preview for commercial research.

Redesigning AlphaGenome for specific cell types

The first AlphaGenome model took a million-letter DNA sequence and predicted properties across thousands of experiments from many tissues all at once. While efficient, this parallel approach spread the model’s capacity thin and made it hard to capture subtle differences between closely related cell types.

To improve this, we fundamentally redesigned how cell types are treated by the model. Instead of predicting in parallel, AlphaGenome 2 is a conditional model that predicts DNA effects for one condition - defined as a tissue, cell type or state - at a time. Alongside the million-letter DNA sequence, it uses the target biological condition as input at every stage of its architecture. This allows it to learn how the same stretch of DNA is read differently across cell types.

A conditional model also allows cross-modality imputation, enabling AlphaGenome 2 to predict missing experimental data. The model separates a cell’s biological state from its outputs, so if a scientist inputs just one measurement, like single-cell gene activity, AlphaGenome 2 can then predict unmeasured properties, such as chromatin accessibility. This can then help researchers prioritize which experiments to do next.

We trained AlphaGenome 2 on both bulk and single-cell data from human and mouse samples, including over 107 million individual cell profiles. Moving beyond the bulk tissue averages of the original model, AlphaGenome 2 can resolve activity inside specialized cells—such as distinct heart muscle or blood immune cells—to help uncover crucial DNA variants that act only in specific cell types.

The model predicts the same core experimental readouts of DNA function as AlphaGenome — such as gene expression, histone modifications and chromatin accessibility — but also DNA methylation, a widely requested readout.

State-of-the-art cell-type predictions and variant recall

AlphaGenome 2 is significantly better at predicting gene regulation in specific cells, helping researchers pinpoint disease-relevant variants.

- Improved cell type specificity On genome regions outside its training data, AlphaGenome 2 explains significantly more of how gene expression differs across cell types within a tissue—tracking continuous changes in gene expression, such as when human blood stem cells mature into red blood cells and immune lineages.

- Improved variant effect prediction On the TenK10K dataset, a large single-cell map of 28 primary immune cell types, AlphaGenome 2 reliably identified nearly twice as many causal variants (30% vs. 16%) and directional gene effects (43% vs. 23%) as the original AlphaGenome fine-tuned on single-cell data and other specialized tools. This precision helps researchers find overlooked candidate variants for new discoveries.

- Pinpointing disease-relevant variants A recent landmark study showed that a variant in a distant regulatory switch drives IBD by overactivating the ETS2 gene in gut immune cells (macrophages). Making predictions across various immune cell types in the gut, AlphaGenome 2 reproduced this finding computationally. It singled out the causal variant and showed that it selectively boosts the ETS2 gene in gut macrophages, leaving other cell types and genes largely unaffected.

Analyzing new cell types and states without retraining

AlphaGenome 2 can also generalize to entirely new single-cell experiments without needing to be retrained. Scientists can upload a single-cell RNA-seq snapshot of gene activity from just a few dozen cells; this gene activity is then compressed into a cell state representation, before being used to condition the model on unseen states. AlphaGenome 2 can match or outperform models trained or fine-tuned directly on those datasets.

This enables AlphaGenome 2 to predict variant effects and gene regulation for new cell states, such as those that occur from disease or vaccination. For example, given unseen single-cell data from a COVID-19 mRNA vaccine study, AlphaGenome 2 distinguished vaccine-activated immune T cells from resting ones. It also revealed that a variant linked to severe COVID-19 disrupts gene activity more strongly when cells are in this activated state — an effect that would be missed in unactivated cells.

Bridging human genetics and single-cell biology

One way to better understand disease is by linking human genetics to cell biology. An important part of building this link requires connecting the thousands of trait and disease-associated variants found in genome-wide association studies (GWAS) to the growing single-cell maps of the human body.

To help researchers make these connections without running large-scale computations themselves, we precomputed AlphaGenome 2 predictions for trait-associated variants across hundreds of human cell and tissue conditions, tackling two major challenges:

- Mapping enhancer–gene links in real human cell types: Many disease-linked variants sit in distant regulatory switches called enhancers, making their effects hard to trace. Because experimental CRISPR screens mostly use stable, lab-grown cell lines, scientists often lack these wiring diagrams for specific cells in the human body. AlphaGenome 2 extends enhancer-gene mapping to primary human cell types — such as brain astrocytes, which support and protect neurons — helping researchers connect regulatory variants to their target genes in the specific cells where disease unfolds.

- Pinpointing causal GWAS variants and the cells where they act: Genome-wide association studies flag broad stretches of DNA linked to disease, but researchers can’t always then find the exact causal variant, target gene, and cell type with high confidence. Across a number of complex traits, ranging from height to type 2 diabetes, AlphaGenome 2’s precomputed variant scores help pinpoint the specific variants and cell types most likely to be involved. When confident, the model also accurately predicts whether a variant turns a gene’s activity up or down — a critical clue for understanding how that gene contributes to disease.

Looking ahead with the scientific community

As scientists map more of the human body at the single-cell level, AI models will continue to become more powerful. Through our ongoing collaborations, including with researchers at the Wellcome Sanger Institute, we are working to profile an even wider spectrum of human cell types, developmental lineages, and perturbed cellular states. We look forward to seeing how researchers from across the community use AlphaGenome 2 and its improved cell-type specific predictions to uncover how genetic variants act across cells, and what that reveals about basic biology and disease.

AlphaGenome 2, and interactive Colab tutorials for expression conditioning are available today for non-commercial research via the AlphaGenome API and in private preview for commercial research. And soon, we will release AlphaGenome 2 Skills in Google Antigravity, our agentic development platform. The combination of specialized models, like AlphaGenome 2, with powerful agentic science tools like Gemini 4 in Antigravity, will help scientists accelerate research and make new discoveries.

We are hugely grateful to our community of trusted testers for their feedback on AlphaGenome 2, as well as the many individuals and researchers that helped create the datasets used to train it. We'd love to spotlight interesting AlphaGenome 2 use cases the community can learn from. Get in touch via this form by 30 November; selected projects may be eligible for additional quota and credits. We also want to be guided by community input to shape our future roadmap, please share your feedback and ideas in our community forum.

Please note that AlphaGenome and AlphaGenome 2 are intended for research use only and have not been validated or approved for clinical or diagnostic purposes.

Acknowledgements

The core research work was done thanks to the contributions of Adam Gayoso, Anne Mottram, Alexander Karollus, Guido Novati, Pavol Drotár, John Reid, Laetitia Meng-Papaxanthos, Adam Kosiorek, Eirini Arvaniti, Vincent Dutordoir, Soham De, Tom Ward, Lai Hong Wong, Matteo Perino, Kyle Taylor, Jun Cheng, Natasha Latysheva, Joshua Pan, Dhavanthi Hariharan, Clare Bycroft, Lauren Nicolaisen, Demis Hassabis, Pushmeet Kohli and Žiga Avsec

We wish to thank the following people for their support and feedback including Gabriella Stanton, Charlie Taylor, Raina Thomas, Lucas Tenório, Antonio García, Joseph Powell, Angli Xue, Trygve Bakken, Nelson Johansen, Junyue Cao, Ziyu Lu, Jacob Hepkema, Leopold Parts, Irina Voineagu, Souradeep Basu, Michal Zielinski, Jeff Cao, Ioannis Sarropoulos, Sarah Teichmann, Amanda Stafford, Eliseo Papa.

Finally we wish to thank Evie Gray, Adriana Fernandez Lara, Alex Wilkins, Danielle Breen, Dimple Vijaykumar, Muinat Abdul, Chris Kwaku-Abdullahi, Kathryn Seager, Ryan Smith, Ross West and Gaby Pearl for their expertise in communicating this work.