Investigating causal relations between brain morphology and genetic risk variants in Parkinson's disease.

ABSTRACT: Imaging genomics for Parkinson's disease (PD) research aims to integrate genetic and imaging biomarkers to explore how genetic alterations influence brain morphology and function. However, traditional methods have been largely correlative, limiting their utility. Recent advances in machine learning offer potential for exploring causal relationships, although these have not yet been applied to investigate genetic variants and brain phenotypes in PD. Thus, we employ a causal deep learning approach for genotype-phenotype analysis in PD using a novel method to assess the causal impact of genetic risk variants on brain structures. A masked causal normalizing flow model was adapted to evaluate genetic variants associated with PD and their effects on brain structures. The Parkinson's Progression

SUBMITTER: Dagasso G 

PROVIDER: S-EPMC12816864 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature