Evaluating sequence-to-function deep learning models for ancestry-stratified regulatory variant effect prediction using multi-ancestry blood eQTLs (opens in new tab)

Background: Sequence-to-function (S2F) deep learning models are increasingly used to prioritize non-coding regulatory variants, but their behavior across ancestrally diverse populations remains unclear. Because both training data and reference resources are heavily European-centered, multi-ancestry benchmarks are needed to determine whether S2F scores capture regulatory effects consistently across populations with different allele-frequency and LD patterns. Methods: We evaluated Borzoi and Al...