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 Markers Initiative (PPMI) dataset was used for development and evaluation, we included 102 controls, 214 patients with PD, and 43 patients with prodromal PD (n = 359), with 223 males (age range 31–82) An additional testing on neurologically healthy participants from the UK Biobank for validation was done as well, with 16,861 participants (Male n = 7,747, age range: 44–82).
The causal deep learning model identified several significant causal relationships: the rs4073221 variant in SATB1 affects the right putamen volume (p-value = 6.8x10-5) and the T408M (rs75548401) variant in GBA1 influences the right pars triangularis volume (p-value = 1x10-13), aligning with known PD pathophysiology. Complex variant analysis of LRRK2 G2019S and GBA1 E365K showed individual-level volumetric changes. Similar trends were found in the UK Biobank and PPMI datasets, demonstrating reasonable generalization.
The proposed causal deep learning framework reveals promising results for investigating genetic-brain architectures in PD. It demonstrates feasibility for further imaging genomics studies in PD and other neurological disorders.
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