Article

Machine learning and burden analyses highlight novel candidate genes in Parkinson’s disease

2026-01-23

Abstract excerpt

Genome-wide association studies (GWAS) have identified numerous risk loci for Parkinson’s 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 t...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8228ece7-4461-5832-9d69-141fc1a8acb3
DOI: 10.64898/2026.01.22.26344646

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning and burden analyses highlight novel candidate genes in Parkinson’s diseaseDOI 10.64898/2026.01.22.26344646Read

Prioritizing Parkinson’s disease risk genes in genome-wide association lociSemantic proximity Read

Prioritizing Parkinson’s disease risk genes in genome-wide association lociSemantic proximity Read

Large-scale Rare Variant Burden Testing in Parkinson’s Disease Identifies Novel Associations with Genes Involved in Neuro-inflammationSemantic proximity Read

Large-scale rare variant burden testing in Parkinson's disease.Semantic proximity Read

Machine learning identifies six genetic variants and alterations in the Heart Atrial Appendage as key contributors to PD risk predictivitySemantic proximity Read

Machine learning analysis reveals biomarkers for the detection of neurodegenerative diseasesSemantic proximity Read

Rare-Variant Burden Analysis of Dystonia Genes in Parkinson’s DiseaseSemantic proximity Read

Expanding Parkinson’s disease genetics: novel risk loci, genomic context, causal insights and heritable riskSemantic proximity Read

Population-scale burden analysis of rare damaging coding variants identifies novel risk genes for Alzheimer’s disease and related dementias and Parkinson’s disease and related disordersSemantic proximity Read

An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer’s diseaseSemantic proximity Read

Identification of novel variants, genes and pathways potentially linked to Parkinson’s disease using machine learningSemantic proximity Read

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies.Semantic proximity Read