Deciphering novel genotypic biomarkers and functional alterations in double strand break repair pathways for Parkinson's disease.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and α-synuclein aggregation, with evidence implicating impaired DNA double-strand break (DSB) repair in disease pathogenesis. This study aimed to systematically evaluate the association between genetic polymorphisms in DSB repair pathway genes, including XRCC4, XRCC5, XRCC6, XRCC7, LIG4, MRE11, RAD50, and NBN, and PD risk, along with corresponding mRNA expression and DNA repair capacity. A total of 123 clinically diagnosed PD patients and 492 age- and sex-matched healthy controls of Taiwanese ancestry were genotyped, and transcriptional and functional assays were performed in 52 healthy controls. Nominally significant associations with PD risk were identified for four SNPs: XRCC6 rs5751129 (p = 3.55 × 10⁻⁵), XRCC4 rs28360071 (p = 0.0181), NBN rs2735383 (p = 0.0213) and RAD50 rs17772583 (p = 0.0411). Among these, XRCC6 rs5751129 remained statistically significant after correction for multiple comparisons. Compared to individuals with the wild-type TT genotype, carriers of the heterozygous variant (TC) and homozygous variant (CC) genotypes exhibited increased PD risks, with odds ratios of 1.86 (95% CI, 1.09–3.17) and 13.82 (95% CI, 2.74–69.54), respectively. Cumulative analysis revealed a dose-dependent increase in PD risk with multiple high-risk genotypes (p for trend = 0.0025). Functionally, the XRCC6 rs5751129 CC variant was associated with reduced mRNA expression (p = 0.0001) and impaired NHEJ and DSB repair capacity (p = 0.0183). These findings suggest that genetic variants in DSB genes XRCC6, XRCC4, NBN and RAD50 are associated with PD susceptibility in a Taiwanese population and provide preliminary functional evidence that XRCC6 variants contribute to compromised DNA repair. Together, the results highlight a critical role of inherited DSB repair deficiencies in PD etiology and suggest potential avenues for personalized risk prediction and prevention.