Deep genomic models of allele-specific measurements

Allele-specific quantification of sequencing data, such as gene expression, allows for a causal investigation of how DNA sequence variations influence cis gene regulation. Current methods for analyzing allele-specific measurements for causal analysis rely on statistical associations between genetic variation across individuals and allelic imbalance. Instead, we propose DeepAllele, a novel deep learning sequence-to-function model using paired allele-specific input, designed to learn sequence features that predict subtle changes in gene regulation between alleles. Our approach is especially suited for datasets with few individuals with unambiguous phasing, such as F1 hybrids and other controlled genetic crosses. We apply our framework to three types of allele-specific measurements in immune cells from F1 hybrid mice, illustrating that as the complexity of the underlying biological mechanisms increases from TF binding to gene expression, the relative effectiveness of model’s architecture becomes more pronounced. Furthermore, we show that the model’s learned cis -regulatory grammar aligns with known biological mechanisms across a significantly larger number of genomic regions compared to baseline models. In summary, our work presents a computational framework to leverage genetic variation to uncover functionally-relevant regulatory motifs, enhancing causal discovery in genomics.