Resolving the gene targets of non-coding genetic variation is the major bottleneck in translating genome wide association studies into mechanistic understanding of complex diseases such as coronary artery disease (CAD). Combining new Transformer-based Machine Learning (ML) approaches trained on cardiovascular epigenetics with high-resolution, allele-specific genomic and transcriptomic technologies we create a highly scalable platform to simultaneously resolve causal variants, cell-type of action, output gene, and direction of effect. When applied to CAD genetics, our ML predicts causal variants from 20,747 candidate SNPs across 9 vessel cell-types and identifies disrupted transcription factor binding motifs using ML feature attributions. We investigate 94 of the top predictions in endothelial cells using Micro Capture-C, revealing the importance of fluid shear stress and TGF-beta signaling pathways. We exploit allelic skew in heterozygous cells to demonstrate both variant causality and effect direction, demonstrating this platform can be used to rapidly resolve non-coding genetics in complex disease.
Real-Time Segmentation and Classification of Birdsong Syllables for Learning Experiments
Songbirds are essential animal models for studying neuronal and behavioral mechanisms of learned vocalizations. Bengalese finch (Lonchura striata domestica) songs contain a limited number of