[en] Heterozygous variants in the glucocerebrosidase GBA1 gene are an increasingly recognized risk factor for Parkinson's disease (PD). Due to the GBAP1 pseudogene, which shares 96% sequence homology with the GBA1 coding region, accurate variant calling by array-based or short-read sequencing methods remains a major challenge in understanding the genetic landscape of GBA1-associated PD. We analyzed 660 patients with PD, 100 patients with Parkinsonism and 808 healthy controls from the Luxembourg Parkinson's study, sequenced using amplicon-based long-read DNA sequencing technology. We found that 12.1% (77/637) of PD patients carried GBA1 variants, with 10.5% (67/637) of them carrying known pathogenic variants (including severe, mild, risk variants). In comparison, 5% (34/675) of the healthy controls carried GBA1 variants, and among them, 4.3% (29/675) were identified as pathogenic variant carriers. We found four GBA1 variants in patients with atypical parkinsonism. Pathogenic GBA1 variants were 2.6-fold more frequently observed in PD patients compared to controls (OR = 2.6; CI = [1.6,4.1]). Three novel variants of unknown significance (VUS) were identified. Using a structure-based approach, we defined a potential risk prediction method for VUS. This study describes the full landscape of GBA1-related parkinsonism in Luxembourg, showing a high prevalence of GBA1 variants as the major genetic risk for PD. Although the long-read DNA sequencing technique used in our study may be limited in its effectiveness to detect potential structural variants, our approach provides an important advancement for highly accurate GBA1 variant calling, which is essential for providing access to emerging causative therapies for GBA1 carriers.
Research center :
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) Luxembourg Centre for Systems Biomedicine (LCSB): Clinical & Experimental Neuroscience (Krüger Group) LIH - Luxembourg Institute of Health ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Neurology Genetics & genetic processes
Author, co-author :
PACHCHEK, Sinthuja ✱; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Translational Neuroscience
LANDOULSI, Zied ✱; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Bioinformatics Core
PAVELKA, Lukas ; University of Luxembourg ; Parkinson Research Clinic, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg ; Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg
Schulte, Claudia; Department of Neurodegeneration, Center of Neurology, Hertie Institute for Clinical Brain Research, German Center for Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany
Buena-Atienza, Elena; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany ; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany
Gross, Caspar ; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany ; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany
Hauser, Ann-Kathrin; Department of Neurodegeneration, Center of Neurology, Hertie Institute for Clinical Brain Research, German Center for Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany
Reddy Bobbili, Dheeraj; LCSB, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
Casadei, Nicolas ; Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany ; NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, Germany
KRÜGER, Rejko ✱; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Translational Neuroscience ; Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg > Parkinson Research Clinic ; Luxembourg Institute of Health, Strassen, Luxembourg
NCER-PD Consortium
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson's study.
Publication date :
23 November 2023
Journal title :
NPJ Parkinson's Disease
eISSN :
2373-8057
Publisher :
Springer Science and Business Media LLC, United States
Hruska, K. S., Lamarca, M. E., Scott, C. R. & Sidransky, E. Gaucher disease: mutation and polymorphism spectrum in the glucocerebrosidase gene (GBA). Hum. Mutat. 29, 567–583 (2008).
Vieira, S. R. L. & Schapira, A. H. V. Glucocerebrosidase mutations and Parkinson disease. J. Neural Transm. 129, 1105–1117 (2022).
Horowitz, M. et al. The human glucocerebrosidase gene and pseudogene: structure and evolution. Genomics 4, 87–96 (1989).
Do, J., Mckinney, C., Sharma, P. & Sidransky, E. Glucocerebrosidase and its relevance to Parkinson disease. Mol. Neurodegener. 14, 36 (2019).
Graham, O. E. E. et al. Nanopore sequencing of the glucocerebrosidase (GBA) gene in a New Zealand Parkinson’s disease cohort. Parkinson. Relat. Disord. 70, 36–41 (2020).
Zimran, A. & Horowitz, M. RecTL: a complex allele of the glucocerebrosidase gene associated with a mild clinical course of Gaucher disease. Am. J. Med. Genet. 50, 74–78 (1994).
Hipp, G. et al. The Luxembourg Parkinson’s study: a comprehensive approach for stratification and early diagnosis. Front. Aging Neurosci. 10, 326 (2018).
Korlach, J. et al. Real-time DNA sequencing from single polymerase molecules. Methods Enzymol. 472, 431–455 (2010).
Blauwendraat, C. et al. NeuroChip, an updated version of the NeuroX genotyping platform to rapidly screen for variants associated with neurological diseases. Neurobiol. Aging 57, 247.e9–247.e13 (2017).
Lill, C. M. et al. Launching the movement disorders society genetic mutation database (MDSGene). Mov. Disord. J. Mov. Disord. Soc. 31, 607–609 (2016).
Toffoli, M. et al. Comprehensive short and long read sequencing analysis for the Gaucher and Parkinson’s disease-associated GBA gene. Commun. Biol. 5, 670 (2022).
Höglinger, G. et al. GBA-associated PD: chances and obstacles for targeted treatment strategies. J. Neural Transm. Vienna Austria 1996 129, 1219–1233 (2022).
Qiao, W. et al. Long-read single molecule real-time full gene sequencing of cytochrome P450-2D6. Hum. Mutat. 37, 315–323 (2016).
Buermans, H. P. J. et al. Flexible and scalable full-length CYP2D6 long amplicon PacBio sequencing. Hum. Mutat. 38, 310–316 (2017).
Borràs, D. M. et al. Detecting PKD1 variants in polycystic kidney disease patients by single-molecule long-read sequencing. Hum. Mutat. 38, 870–879 (2017).
Frans, G. et al. Conventional and single-molecule targeted sequencing method for specific variant detection in IKBKG while bypassing the IKBKGP1 pseudogene. J. Mol. Diagn. 20, 195–202 (2018).
Ruskey, J. A. et al. Increased yield of full GBA sequencing in Ashkenazi Jews with Parkinson’s disease. Eur. J. Med. Genet. 62, 65–69 (2019).
Gan-Or, Z. et al. Differential effects of severe vs mild GBA mutations on Parkinson disease. Neurology 84, 880–887 (2015).
Petrucci, S. et al. GBA-related Parkinson’s disease: dissection of genotype-phenotype correlates in a large Italian cohort. Mov. Disord. 35, 2106–2111 (2020).
Jesús, S. et al. GBA variants influence motor and non-motor features of Parkinson’s disease. PloS One 11, e0167749 (2016).
Olszewska, D. A. et al. Association between glucocerebrosidase mutations and Parkinson’s disease in Ireland. Front. Neurol. 11, 527 (2020).
Duran, R. et al. The glucocerobrosidase E326K variant predisposes to Parkinson’s disease, but does not cause Gaucher’s disease. Mov. Disord. 28, 232–236 (2013).
Ran, C. et al. Strong association between glucocerebrosidase mutations and Parkinson’s disease in Sweden. Neurobiol. Aging 45, 212.e5–212.e11 (2016).
Davis, M. Y. et al. Association of GBA mutations and the E326K polymorphism with motor and cognitive progression in Parkinson disease. JAMA Neurol. 73, 1217–1224 (2016).
Berge-Seidl, V. et al. The GBA variant E326K is associated with Parkinson’s disease and explains a genome-wide association signal. Neurosci. Lett. 658, 48–52 (2017).
Picillo, M. et al. Progressive supranuclear palsy-like phenotype in a GBA E326K mutation carrier. Mov. Disord. Clin. Pract. 4, 444–446 (2017).
Blauwendraat, C. et al. Parkinson’s disease age at onset genome-wide association study: defining heritability, genetic loci, and α-synuclein mechanisms. Mov. Disord. 34, 866–875 (2019).
Sidransky, E. et al. Multicenter analysis of glucocerebrosidase mutations in Parkinson’s disease. N. Engl. J. Med. 361, 1651–1661 (2009).
Brockmann, K. et al. GBA-associated Parkinson’s disease: reduced survival and more rapid progression in a prospective longitudinal study. Mov. Disord. 30, 407–411 (2015).
Setó-Salvia, N. et al. Glucocerebrosidase mutations confer a greater risk of dementia during Parkinson’s disease course. Mov. Disord. 27, 393–399 (2012).
Krohn, L. et al. GBA variants in REM sleep behavior disorder: a multicenter study. Neurology 95, e1008–e1016 (2020).
Brockmann, K. et al. Association between CSF alpha-synuclein seeding activity and genetic status in Parkinson’s disease and dementia with Lewy bodies. Acta Neuropathol. Commun. 9, 175 (2021).
Litvan, I. et al. SIC Task Force appraisal of clinical diagnostic criteria for Parkinsonian disorders. Mov. Disord. 18, 467–486 (2003).
Pavelka, L. et al. Age at onset as stratifier in idiopathic Parkinson’s disease—effect of ageing and polygenic risk score on clinical phenotypes. NPJ Park. Dis. 8, 102 (2022).
Gustavsson, E. K. et al. Genetic identification in early onset Parkinsonism among Norwegian patients. Mov. Disord. Clin. Pract. 4, 499–508 (2017).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
1000 Genomes Project Consortium. et al. An integrated map of genetic variation from 1092 human genomes. Nature 491, 56–65 (2012).
Leija-Salazar, M. et al. Evaluation of the detection of GBA missense mutations and other variants using the Oxford Nanopore MinION. Mol. Genet. Genom. Med. 7, e564 (2019).
Rhoads, A. & Au, K. F. PacBio sequencing and its applications. Genom. Proteom. Bioinforma. 13, 278–289 (2015).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformation 34, 3094–3100 (2018).
Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformation 32, 3047–3048 (2016).
Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).
Modi, A., Vai, S., Caramelli, D. & Lari, M. The illumina sequencing protocol and the NovaSeq 6000 System. Methods Mol. 2242, 15–42 (2021).
Miller, N. A. et al. A 26-hour system of highly sensitive whole genome sequencing for emergency management of genetic diseases. Genome Med. 7, 100 (2015).
Ji, J. et al. A semiautomated whole-exome sequencing workflow leads to increased diagnostic yield and identification of novel candidate variants. Cold Spring Harb. Mol. Case Stud. 5, a003756 (2019).
Depristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
Stenson, P. D. et al. Human gene mutation database (HGMD): 2003 update. Hum. Mutat. 21, 577–581 (2003).
Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).
Ioannidis, N. M. et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).
Tian, Y. et al. REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification. Sci. Rep. 9, 12752 (2019).
Jian, X., Boerwinkle, E. & Liu, X. In silico prediction of splice-altering single nucleotide variants in the human genome. Nucleic Acids Res. 42, 13534–13544 (2014).
Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).
Wang, K. et al. PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 17, 1665–1674 (2007).
Dierckxsens, N., Li, T., Vermeesch, J. R. & Xie, Z. A benchmark of structural variation detection by long reads through a realistic simulated model. Genome. Biol. 22, 342 (2021).
Sorrentino, E. et al. PacMAGI: a pipeline including accurate indel detection for the analysis of PacBio sequencing data applied to RPE65. Gene 832, 146554 (2022).
Johannesen, K. M. et al. Genotype-phenotype correlations in SCN8A-related disorders reveal prognostic and therapeutic implications. Brain J. Neurol. 145, 2991–3009 (2022).
