Genome-wide association study of copy number variations in Parkinson's disease
Landoulsi, Z.; Ashok Kumar Sreelatha, A.; Schulte, C.; Bobbili, D. R.; Montanucci, L.; Leu, C.; Niestroj, L. M.; Hassanin, E.; Domenighetti, C.; Sugier, P. E.; Radivojkov Blagojevic, M.; Lichtner, P.; Portugal, B.; Edsall, C.; Kruger, J.; Hernandez, D. G.; Blauwendraat, C.; Mellick, G. D.; Zimprich, A.; Pirker, W.; Tan, M.; Rogaeva, E.; Lang, A.; Koks, S.; Taba, P.; Lesage, S.; Brice, A.; Corvol, J. C.; Chartier Harlin, M. C.; Mutez, E.; Brockmann, K.; Deutschlander, A. B.; Hadjigeorgiou, G. M.; Dardiotis, E.; Stefanis, L.; Simitsi, A. M.; Valente, E. M.; Petrucci, S.; Straniero, L.; Zecchinel
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ObjectiveTo investigate the impact of copy number variations (CNVs) on Parkinsons disease (PD) pathogenesis using genome-wide data and explore their role in sporadic PD. MethodsWe analyzed CNV data from 11,035 PD patients (including 2,731 early-onset PD (EOPD)) and 8,901 controls from the COURAGE-PD consortium using a sliding window CNV-GWAS and genome-wide burden analysis. The independent dataset from the Global Parkinson Genetics Program (GP2) consisted of 23,089 cases and 18,824 controls were used to validate our initial findings. ResultsThe exploratory dataset identifies multiple CNV regions associated with PD risk. The nominated CNV loci were not confirmed in an independent dataset, except that only a deletion in the PRKN gene, a well-established EOPD locus, remained genome-wide significant and robustly supported. CNV burden analysis showed a higher prevalence of CNVs in PD-related genes in patients compared to controls (OR=1.56 [1.18-2.09], p=0.0013), with PRKN showing the highest burden (OR=1.47 [1.10-1.98], p=0.026). Patients with CNVs in PRKN had an earlier disease onset. Burden analysis with controls and EOPD patients showed similar results. InterpretationThe largest CNV-based GWAS on PD highlights both the promise and pitfalls of array-based CNV detection in PD and underscores the relevance of whole-genome sequencing approaches in resolving the role of CNV in PD. The array-based findings are prone towards false positive findings that might arise either from platform limitations and/or cohort biases. Future studies require improved genotyping resolution and rigorous cross-cohort validation to reliably assess CNV contributions to PD risk.
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