Objective: Identify potential genetic modifiers of GBA1 risk on Parkinson’s disease (PD).

Background: GBA1 is a common genetic risk factor for PD. GBA1-carrying PD cases (GBA-PD) have distinct clinical and progression features such as increased likelihood to develop dementia and faster progression [1]. Despite this distinct phenotype, the additional genetic factors that contribute to PD development or modify disease presentation specifically in GBA1 carriers remain largely unknown [2].

Method: We analyzed UK Biobank exome data from European ancestry participants to identify GBA1 mutation carriers with and without PD. We predicted pathogenic rare variants (MAF < 0.01) exome-wide using functional annotations, AlphaMissense, and REVEL [3–5]. These predicted pathogenic variants were then incorporated into gene-level burden analyses with Regenie to identify GBA-PD specific risk genes across 17,696 genes [6], while adjusting for age, age-squared, genetic sex, genetic principal components, and GBA1 mutation severity (CADD PHRED score v1.7) [7]. Results were collapsed per-gene using aggregated cauchy association tests [8] (genome-wide significance threshold: p < 2.5 x 10-6).

Results: We identified 345 GBA-PD cases and 20,689 GBA1-carrying controls. Synaptotagmin 10 (SYT10), a gene previously implicated with PD-related mortality and gene-gene interaction with LRRK2 [9,10], showed significant enrichment of predicted pathogenic variants in GBA-PD cases (odds ratio = 30.0, 95% CI = 8.2–109.3, p = 2.6 x 10-7, n-cases = 6, n-controls = 18). SYT10-carrying cases had a median age of diagnosis 7.5 years earlier than other GBA-PD cases (64.5 vs 72 years). Among 10 genes at the suggestive significance threshold (p < 5 x 10-4), we identified lysosomal genes (MFSD12, TAB2) and mitochondrial genes (ACAT1, SAMM50, MRPL16).

Conclusion: Our findings highlight several putative genetic modifiers that may drive GBA-PD risk, with SYT10 showing the strongest association. The earlier age of diagnosis in SYT10-carrying GBA-PD cases suggests potential clinical relevance. Further investigation, including replication in independent datasets and functional validation, will be necessary to confirm these genetic associations and elucidate how they may interact with GBA1 and LRRK2 to modify disease risk and onset time.

References: 1. Blandini F, Cilia R, Cerri S, Pezzoli G, Schapira AHV, Mullin S, et al. Glucocerebrosidase mutations and synucleinopathies: Toward a model of precision medicine. Mov Disord. 2019;34:9–21.
2. Blauwendraat C, Reed X, Krohn L, Heilbron K, Bandres-Ciga S, Tan M, et al. Genetic modifiers of risk and age at onset in GBA associated Parkinson’s disease and Lewy body dementia. Brain. 2020;143:234–48.
3. Cheng J, Novati G, Pan J, Bycroft C, Žemgulytė A, Applebaum T, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science. 2023;381:eadg7492.
4. Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet. 2016;99:877–85.
5. Tabet DR, Kuang D, Lancaster MC, Li R, Liu K, Weile J, et al. Benchmarking computational variant effect predictors by their ability to infer human traits. Genome Biol. 2024;25:172.
6. Mbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet. 2021;53:1097–103.
7. Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res. 2024;52:D1143–54.
8. Liu Y, Chen S, Li Z, Morrison AC, Boerwinkle E, Lin X. ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies. Am J Hum Genet. 2019;104:410–21.
9. Tan MMX, Lawton MA, Pollard MI, Brown E, Real R, Carrasco AM, et al. Genome-wide determinants of mortality and motor progression in Parkinson’s disease. NPJ Parkinsons Dis. 2024;10:113.
10. Aleknonytė-Resch M, Trinh J, Leonard H, Delcambre S, Leitão E, Lai D, et al. Genome-wide case-only analysis of gene-gene interactions with known Parkinson’s disease risk variants reveals link between LRRK2 and SYT10. NPJ Parkinsons Dis. 2023;9:102.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/exome-wide-burden-analysis-identifies-syt10-as-a-genetic-modifier-of-gba-pd-in-uk-biobank/