Excess of singleton loss-of-function variants in Parkinson's disease contributes to genetic risk. Issue 9 (13th February 2020)

• Record Type: Journal Article
• Authors: Bobbili, Dheeraj Reddy; Banda, Peter; Krüger, Rejko; May, Patrick
• Is Part Of: Journal of medical genetics. Volume 57:Issue 9(2020)
• Page Start: 617
• Page End: 623
• Publication Date: 2020-02-13
• Subjects: Parkinson-s disease -- genetics -- complex traits; Medical genetics -- Periodicals; 616.042
• DOI: 10.1136/jmedgenet-2019-106316
• Languages: English
• ISSNs: 1468-6244

Abstract

Abstract : Background: Parkinson's disease (PD) is a neurodegenerative disorder with complex genetic architecture. Besides rare mutations in high-risk genes related to monogenic familial forms of PD, multiple variants associated with sporadic PD were discovered via association studies. Methods: We studied the whole-exome sequencing data of 340 PD cases and 146 ethnically matched controls from the Parkinson's Progression Markers Initiative (PPMI) and performed burden analysis for different rare variant classes. Disease prediction models were built based on clinical, non-clinical and genetic features, including both common and rare variants, and two machine learning methods. Results: We observed a significant exome-wide burden of singleton loss-of-function variants (corrected p=0.037). Overall, no exome-wide burden of rare amino acid changing variants was detected. Finally, we built a disease prediction model combining singleton loss-of-function variants, a polygenic risk score based on common variants, and family history of PD as features and reached an area under the curve of 0.703 (95% CI 0.698 to 0.708). By incorporating a rare variant feature, our model increased the performance of the state-of-the-art classification model for the PPMI dataset, which reached an area under the curve of 0.639 based on common variants alone. Conclusion: The main finding of this study is to highlight the contribution of singleton loss-of-function variants to the complex genetics of PD and that disease risk prediction models combining singleton and common variants can improve models built solely on common variants.