Pathway Modeling of Genomic and Tissue-Specific Transcriptomic Architecture Identifies Personalized Mechanisms of Atrial Fibrillation Risk
- 1University of Pennsylvania
Abstract
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a major cause of cardioembolic stroke. Although polygenic risk scores (PRS) are well characterized to quantify inherited susceptibility for AF, they provide limited insight into the pathways and tissues underlying genetic risk, which are critical to uncover for individual risk prediction. In this study, we develop a pathway-level multi-omics representation learning framework that converts individual genetic profiles into interpretable biological features by integrating GWAS-derived pathway burden scores with tissue-specific transcriptomic pathway signals. We constructed machine learning models to assess population-level AF risk prediction performance across genomic and transcriptomic tissue contexts; the pathway-based global attention models substantially improved risk prediction performance over PRS and other baselines (AUROC improved from 0.601 to 0.738). Transformer and graph neural network frameworks then assessed individual-level pathway interpretability, revealing heterogeneous contributions from electrical signaling, cardiac development, and DNA repair pathways to AF risk. This added interpretability highlights the potential of this pathway approach to enable more mechanistically informed risk stratification than static PRS by capturing underlying heterogeneity. To independently assess whether prioritized pathways reflected cardiac regulatory biology, we compared pathway rankings with transcriptional effects predicted by the AlphaGenome foundation model. Variants in highly ranked pathways showed significantly greater predicted effects on expression in atrial and ventricular tissues (FDR = 0.032) relative to controls, providing orthogonal evidence that the model identifies biologically relevant mechanisms. Overall, this work reframes polygenic risk from a single measure of susceptibility to tissue-informed pathway mechanisms, providing a framework for interpretable genomic stratification in complex diseases.
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BibTeX
@misc{venkatesh2026pathway,
title = {{Pathway Modeling of Genomic and Tissue-Specific Transcriptomic Architecture Identifies Personalized Mechanisms of Atrial Fibrillation Risk}},
author = {R. Venkatesh and R. Deo and T. Cappola and Penn Medicine BioBank and M. D. Ritchie and D. Kim},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.25.26361369},
url = {https://www.medrxiv.org/content/10.64898/2026.08.25.26361369v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Pathway Modeling of Genomic and Tissue-Specific Transcriptomic Architecture Identifies Personalized Mechanisms of Atrial Fibrillation Risk
AU - R. Venkatesh
AU - R. Deo
AU - T. Cappola
AU - Penn Medicine BioBank
AU - M. D. Ritchie
AU - D. Kim
PY - 2026
DA - 2026/08/25
PB - medRxiv
DO - 10.64898/2026.08.25.26361369
UR - https://www.medrxiv.org/content/10.64898/2026.08.25.26361369v1
AB - Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a major cause of cardioembolic stroke. Although polygenic risk scores (PRS) are well characterized to quantify inherited susceptibility for AF, they provide limited insight into the pathways and tissues underlying genetic risk, which are critical to uncover for individual risk prediction. In this study, we develop a pathway-level multi-omics representation learning framework that converts individual genetic profiles into interpretable biological features by integrating GWAS-derived pathway burden scores with tissue-specific transcriptomic pathway signals. We constructed machine learning models to assess population-level AF risk prediction performance across genomic and transcriptomic tissue contexts; the pathway-based global attention models substantially improved risk prediction performance over PRS and other baselines (AUROC improved from 0.601 to 0.738). Transformer and graph neural network frameworks then assessed individual-level pathway interpretability, revealing heterogeneous contributions from electrical signaling, cardiac development, and DNA repair pathways to AF risk. This added interpretability highlights the potential of this pathway approach to enable more mechanistically informed risk stratification than static PRS by capturing underlying heterogeneity. To independently assess whether prioritized pathways reflected cardiac regulatory biology, we compared pathway rankings with transcriptional effects predicted by the AlphaGenome foundation model. Variants in highly ranked pathways showed significantly greater predicted effects on expression in atrial and ventricular tissues (FDR = 0.032) relative to controls, providing orthogonal evidence that the model identifies biologically relevant mechanisms. Overall, this work reframes polygenic risk from a single measure of susceptibility to tissue-informed pathway mechanisms, providing a framework for interpretable genomic stratification in complex diseases.
ER -