TY - JOUR
T1 - Excess of singleton loss-of-function variants in Parkinson's disease contributes to genetic risk
AU - Bobbili, Dheeraj Reddy
AU - Banda, Peter
AU - Krüger, Rejko
AU - May, Patrick
N1 - Funding Information:
This work was supported by grants from the Luxembourg National Research Fund (FNR) within the National Centre for Excellence in Research on Parkinson's disease (NCER-PD to PB, RK, PM) and PEARL program (FNR/P13/6682797 to RK), the EU Joint Program-Neurodegenerative Diseases (JPND; COURAGE -PD to RK, DRB, PM), the German Research Council (DFG; KR2119/8-1 to RK) and by the European Union's Horizon2020 research and innovation program under grant agreement no. 692320 (WIDESPREAD; CENTRE-PD to RK and PM).
Funding Information:
Funding This work was supported by grants from the luxembourg national research Fund (Fnr) within the national centre for excellence in research on Parkinson’s disease (ncer-PD to PB, rK, PM) and Pearl program (Fnr/ P13/6682797 to rK), the eU Joint Program-neurodegenerative Diseases (JPnD; cOUrage-PD to rK, DrB, PM), the german research council (DFg; Kr2119/8-1 to rK) and by the european Union’s horizon2020 research and innovation program under grant agreement no. 692320 (WiDesPreaD; cenTre-PD to rK and PM).
Publisher Copyright:
©
PY - 2020/9/1
Y1 - 2020/9/1
N2 - 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.
AB - 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.
KW - Parkinson-s disease
KW - complex traits
KW - genetics
UR - http://www.scopus.com/inward/record.url?scp=85081639379&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/32054687
U2 - 10.1136/jmedgenet-2019-106316
DO - 10.1136/jmedgenet-2019-106316
M3 - Article
C2 - 32054687
AN - SCOPUS:85081639379
SN - 0022-2593
VL - 57
SP - 617
EP - 623
JO - Journal of Medical Genetics
JF - Journal of Medical Genetics
IS - 9
ER -