DeepMind's AlphaGenome 2 advances genetic variant prediction in specific cell types, aiding disease research with improved precision.
DeepMind has unveiled AlphaGenome 2, a next-generation DNA sequence model designed to predict how genetic variants impact specific human cell types and states. Announced on November 11, 2026, the model represents a significant advancement in bridging single-cell biology and human genetics, offering researchers a powerful tool to uncover the cellular mechanisms driving disease.
AlphaGenome 2 builds on the foundation of its predecessor by moving beyond broad tissue averages to focus on individual cell types and states. This redesign allows the model to predict variant effects in disease-relevant cells that broad-tissue models might overlook. According to DeepMind, AlphaGenome 2 identifies nearly twice as many variants that alter gene expression in cells compared to the original AlphaGenome model.
The model was trained on over 100 million individual cell profiles across 68 tissues, leveraging both bulk and single-cell data from human and mouse samples. This extensive training enables AlphaGenome 2 to transform static cell atlases into a predictive engine for human genetics, offering insights into specialized cells such as heart muscle cells and immune cells.
A key innovation in AlphaGenome 2 is its conditional model architecture. Unlike the original model, which predicted properties across thousands of experiments simultaneously, AlphaGenome 2 focuses on one biological condition at a time. This approach allows the model to learn how the same DNA sequence is interpreted differently across various cell types and states.
Additionally, the model supports cross-modality imputation, enabling it to predict unmeasured properties such as chromatin accessibility from single-cell gene activity data.
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