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Machine learning and burden analyses highlight novel genes in Parkinson's Disease

2026-01-23 genetic and genomic medicine Title + abstract only
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Genome-wide association studies (GWAS) have identified numerous risk loci for Parkinsons disease, yet identifying specific causal genes remains a major challenge due to non-coding associations and complex linkage disequilibrium. Here, we present a systematic framework integrating machine learning-based gene prioritization with high-resolution rare variant burden analysis. Using an XGBoost machine-learning model trained on 285 multi-omic features, including brain-specific eQTLs and single-cell ex...

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