Google DeepMind Unveils AlphaGenome 2 for Cell-Specific Variant Prediction
Google DeepMind unveiled AlphaGenome 2, a model designed to predict how genetic variants affect specific cell types and states. The company says it was trained on more than 100 million individual cell profiles spanning 68 tissues and identifies nearly twice as many variants that can alter gene expression as its predecessor, though these performance claims are company-reported. Google is making the model available for non-commercial research through the AlphaGenome API, with a limited private preview for commercial research.
AlphaGenome 2 extends AI-based genetic analysis from broader genome predictions toward cell-specific effects, giving researchers a finer-grained way to connect population genetic variants with cell biology.
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.