John C. Butts

This study functionally characterizes 221,412 trait-associated variants using MPRA in 5 cell types, identifying 12,025 regulatory variants and revealing widespread epistasis and non-TF mechanisms.

This study introduces MPAC, a machine learning model that predicts the cis-regulatory impact of non-coding variants, demonstrating its accuracy across diverse applications and datasets.

This paper designs synthetic cis-regulatory elements (CREs) that outperform natural ones in driving cell type-specific gene expression using deep learning and experimental validation.

The paper identifies causal 3'UTR variants linked to human disease and evolution using a new high-throughput assay and functional analysis.