Abstract
LRRK2 variants are key genetic risk factors for Parkinson’s Disease (PD). We conducted a per-domain rare coding variant burden analysis, including 8,888 PD cases and 69,412 controls. In meta-analysis, the Kinase domain was strongly associated with PD (Exonic: PFDR = 1.61 × 10−22, Non-synonymous: PFDR = 1.54 × 10−23, CADD > 20: PFDR = 3.09 × 10−24). Excluding the p.G2019S variant nullified this effect. Nominal associations were found in the ANK and Roc-COR domains, with potentially protective variants, p.R793M and p.Q1353K.
Introduction
Leucine-rich repeat kinase 2 (LRRK2) protein is part of the endolysosomal-autophagy pathway, where numerous proteins are implicated in PD pathogenesis1. Variants in the LRRK2 gene have been associated with both familial and sporadic forms of PD, yet the specific pathogenic mechanism underlying the association between LRRK2 and PD is unclear2. The most common pathogenic variant, p.G2019S, is associated with increased risk of developing PD of up to 10-fold in certain populations3,4, with evidence for also affecting the prevalence of dystonia in PD5.
The structure of the LRRK2 protein consists of seven domains, including Armadillo repeat (ARM), Ankyrin repeat (ANK), Leucine-rich repeat (LRR), Kinase, Ras-of-complex (Roc) GTPase in tandem with C-terminal of Roc (COR), and WD40 (Fig. 1). The domains can be grouped by functions: (1) ARM, ANK, LRR, and WD40 serve as scaffolding domains, (2) Kinase domain contains the catalytic site for kinase activity, and (3) the Roc-COR bidomain contains the catalytic site for the GTPase6.
A Cryoelectron microscopy structure of LRRK2, PDB code 8FO215. The location of Parkinson-linked missense mutation sites p.R793M and p.Q1353K are indicated. B, C Details of the impact of mutations listed above, as described in the text.
The LRRK2 Kinase and Roc-COR regions are of particular interest as they contain catalytic functions of the protein. Most of the known pathogenic variants are in these two sites7, most of which have been shown to alter kinase enzymatic activity8. For instance, the pathogenic p.G2019S variant is located in the Kinase domain, and leads to increased kinase activity9, suggesting that LRRK2 Kinase inhibition could be a therapeutic strategy for PD8. Another pathogenic variant, p.R1441H, is located in the Roc-COR bidomain region and is associated with altered GTPase function10. While evidence for pathogenicity outside of these two catalytic sites is limited, the PD risk variant p.G2835R, located in the WD40 domain, has been shown to increase disease risk by two-fold in East Asians11, with in vitro evidence for increase of LRRK2 Kinase activity12.
The association between LRRK2 rare variants and PD risk burden has been established13. However, the association of specific LRRK2 domains burden in PD risk is not known. Domain-level analysis could aid in the identification of novel variants and provide functional insight. In this paper, across six cohorts, we meta-analyzed the PD burden of LRRK2 rare variants using the optimized sequence kernel association test (SKAT-O) per-domain in 8,888 PD cases and 69,412 controls (detailed in Supplementary Data 1). We then conducted structural analyzes of variants of potential significance to understand how certain variants could be altering the structure, function, and interactions of the protein.
The average coverage across the four cohorts sequenced locally at McGill exceeded 652X, with over 99% of nucleotides achieving coverage greater than 30× (Supplementary Data 2). Across the four functional categories (exonic, non-synonymous, Combined Annotation Dependent Depletion (CADD) score ≥ 20, and loss-of-function (LOF)) and after quality control steps were applied, in our local cohorts, we analyzed 105 unique variants from the McGill University cohort, 65 unique variants from the Columbia University cohort, 47 unique variants from the Sheba Medical Center cohort, and 113 unique variants from the Pavlov First State Medical University and Institute of Human Brain cohort. In addition to this list of variants, from the external cohorts, 454 unique variants from United Kingdom Biobank (UKBB) and 96 unique variants from Accelerating Medicines Partnership–Parkinson Disease (AMP-PD) were also analyzed. The complete list of these variants is provided in Supplementary Data 3 and 4 for the local and external cohorts, respectively.
The per-domain burden analysis showed a post-False Discovery Rate (FDR) significance in the LRRK2 Kinase domain regions in the SKAT-O results across five of the six cohorts (Supplementary Data 5) and the MetaSKAT results of the meta-analysis (See Table 1 and, for more detail, Supplementary Data 6). We then performed SKAT-O and MetaSKAT analyzes after excluding the p.G2019S variant, resulting in a complete elimination of association in the LRRK2 Kinase domain (Table 2). In this domain, the p.G2019S variant was the driver of association, and in its absence, the aggregate of the remaining variants in the region could not drive a significant association for PD risk.
In other domains, the associations did not survive the correction for multiple comparisons. However, nominal significance was observed in: (1) the ARM domain in the Pavlov cohort (Exonic: P = 0.04), (2) the ANK domain in the McGill cohort (Non-synonymous: P = 0.03) and the meta-analysis (Non-synonymous: P = 0.03), (3) the Roc-COR domain region in UKBB (Exonic: P = 0.01; Non-synonymous: P = 0.01; CADD ≥ 20: P = 0.04) and the meta-analysis (Exonic: P = 0.04; Non-synonymous: P = 0.04).
We nominated two variants p.R793M and p.Q1353K to be potentially protective, based on their frequencies in patients and controls. While these variants showed intriguing patterns, we did not observe statistically significant odds ratios (OR) values to affirm our observations. The nominated variants with their sample and frequency data across all cohorts and The Genome Aggregation Database (gnomAD) non-Finnish European are detailed in Table 3.
In our variant-level OR calculations, we did not observe any p-value significance except for p.G2019S (See Supplementary Data 3 and 4). Certain variants in UKBB were also marked as statistically significant in continuity corrected OR calculations; however, this significance is attributed to the disproportionate 1:20 case-to-control ratio in UKBB, which biases the corrected results. Consequently, UKBB variants were disregarded in the nomination process.
The ANK domain showed nominal significance in the burden analysis in the McGill cohort and the meta-analysis. Located in this region, the rare variant p.R793M was nominated for its presence in 9 controls and none in patients in the McGill cohort, suggesting potential protective association. The exclusion of p.R793M from the burden analysis resulted in the loss of the nominal significance in the ANK domain (See Supplementary Data 5 and 6), showing that the p.R793M was driving the nominal regional association of its domain in McGill cohort and the meta-analysis.
Secondly, LRRK2 Roc-COR bidomain showed nominal significance in the meta-analysis, which potentially implicates the p.Q1353K variant, as it was reported only in 8 controls in the McGill University. It was also reported in AMP-PD in 1 case and 2 controls. This variant was noted previously before in the McGill University cohort14 with no significant variant-level post-correction associations. At domain level, we were also unable to observe post-correction significance. In addition, the exclusion of p.Q1353K from the burden analysis did not nullify any nominal association, which limits the evidence for its protective role in PD to being solely observational (See Supplementary Data 5 and 6).
The influence of these two variants was also studied structurally to better understand how they may affect the function of the LRRK2 protein and its domains.
LRRK2 can be recruited to biological membranes of organelles, where it can be activated by RAB29 binding to the ARM domain15. RAB29 binding is accompanied by formation of tetramer, where two LRRK2 molecules occupy the center and adopt an active Kinase conformation, whereas the two peripheral protomers are bound to RAB29 and adopt an inactive Kinase conformation. These conformations are regulated by allosteric interactions between the different domains (inactive full-length protomer shown in Fig. 1A). Furthermore, oligomerization and allosteric changes also regulate interactions with microtubules6,16. To gain insight into the functional effects of missense variants in LRRK2, we analyzed them in the structure of human LRRK2 in complex with the small GTPase RAB29.
The first observation is that the identified protective variants are not at oligomerization interfaces. The p.R793M variant, which is potentially protective, is in the ANK, near the interface with the Kinase and WD40 domains (Fig. 1B). Arginine is a positively charged residue, and thus the change to Methionine would reduce polar interactions with the Kinase domain, and thus could modulate the coupling between RAB29 or microtubule binding and Kinase activation. The p.Q1353K variant, again potentially protective, is in the ROC GTPase domain and is located near the nucleotide binding site (Fig. 1C). The variant would introduce a positive charge, which could create a new interaction with p.E1492 on the opposite side, thus affecting the GTPase activity. These variants may protect from PD by increasing the threshold for activation through allostery, thus reducing the overall activity of the protein (without abolishing it). The p.R793M variant was indeed reported to decrease MLi-2-induced microtubule association, while not significantly affecting the intrinsic Kinase activity12.
In our per-domain analysis, we showed, as expected, a significant association between the Kinase domain of LRRK2 and PD. We did not find any secondary signal after excluding the p.G2019S variant in the Kinase domain. However, it is important to note that there are other, much rarer variants in this domain that are known to cause PD and were too rare in our tested populations to be identified, such as p.I2020T17. While other associations in other domains did not survive the FDR corrections, nominal associations were observed (See Supplementary Data 5 and 6), with the ANK and Roc-COR domain region rare variants showing domain-level nominal significance in the meta-analysis.
In closer inspection, we nominated two potentially protective variants: p.R793M and p.Q1353K, based on evidence from the burden tests, population frequencies, and number of samples carrying them in cases vs. controls. We further examined the potential structural effects of these variants in simulations, yet we cannot conclusively determine whether they have a protective role in PD. The well-established major risk variant LRRK2 p.G2019S has been shown to increase Kinase activity. This is supportive of a gain-of-function mechanism for the association between LRRK2 and PD risk. It has been previously suggested that inhibiting Kinase activity could help alleviate PD pathology8, and safety of an LRRK2 kinase-inhibiting drug has been tested in a human clinical trial18.
LRRK2 interacts with a variety of Rab-based GTPase proteins19, which are increasingly implicated in autophagy-related disorders. Recently, the RAB32 protein has been identified as a novel PD susceptibility gene20. LRRK2 interacts with RAB32 through its ANK domain21. Rare variants in LRRK2 ARM and ANK domains could influence the affinities of binding with the Rab-family of proteins. However, the evidence as to how this could influence PD risk is unclear. Amongst the two variants we have nominated, cell assays studying the effect of p.R793M could be insightful in showing whether this variant protects against PD pathobiology.
Our study was limited by various factors. Different quality control standards were applied for targeted sequencing, whole-exome sequencing, and whole-genome sequencing data, using varying thresholds for depth of coverage and quality assessment as per platform recommendations. This variability could potentially lead to discrepancies in the enrichment of variants between different cohorts. Particularly, the UKBB dataset was subject to lower GQ and depth of coverage thresholds. Another constraint was the absence of a conventional power calculation in our statistical analysis. Unlike methods that rely on clear effect size estimates, SKAT-O primarily evaluates genetic effects in terms of variance components, which do not readily translate into parameters suitable for standard power estimation. As a result, we could not accurately gauge the statistical power. The lack of diversity in the populations that were included presented another limitation. All of our six cohorts mainly consisted of European populations, which may have led to us missing important LRRK2 variants. These included the risk-inducing p.R1628P and p.G2835R variants, located in the Roc-COR and WD40 domain regions, respectively, and predominantly found in East Asian populations4,11. Specifically, the p.G2835R was absent across all our cohorts. For future studies, we recognize the importance of incorporating more diverse ethnic populations to allow for a more comprehensive set of variants and to improve our ability to detect regional risk associations, such as at gene-level or domain-level, as is the case in this study. In addition, variants that are known to be risk factors for PD, such as p.N2081D, were excluded since their MAF in Europeans is higher than 0.01.
Overall, our study tests the potential roles of each domain of the LRRK2 protein, yet insight on the potential roles of the different LRRK2 domains in PD remains limited, with the nomination of two potentially protective variants that require further work to substantiate any functional implications.
Methods
Study population
In this study, we include six large cohorts with a total of 8888 PD patients and 69,412 controls (further detailed in Supplementary Data 1), post-quality control. Clinical diagnosis was performed by movement disorder specialists following the UK brain bank criteria22 or the movement disorders clinical diagnostic criteria23. Four distinct cohorts were collected and sequenced at McGill University: the McGill cohort (comprising participants from Quebec, Canada, and Montpellier, France), the Columbia University cohort (New York, NY), the Sheba Medical Center cohort (Israel), and the Pavlov First State Medical University and Institute of Human Brain cohort (Saint-Petersburg, Russia). The McGill cohort included participants from Quebec, Canada, recruited partly through the Quebec Parkinson Network (QPN)24, and from France. The Columbia cohort, collected in New York, represented a mixed ancestry group, including individuals of European descent, Ashkenazi Jewish (AJ) background, and a smaller proportion of Hispanic and Black participants, as described previously25. The Sheba cohort, recruited in Israel, consisted of participants with AJ ancestry, as reported previously26 as well as non-Ashkenazi Jewish ancestry. The Pavlov and Human Brain cohort, recruited in Russia, primarily included patients of European descent. Additionally, two external patient cohorts from UKBB and AMP-PD, which mainly consist of European samples, were accessed and analyzed (See Supplementary Data 1).
Data sequencing
We sequenced the four cohorts (McGill, Columbia, Sheba, and Pavlov) at McGill University via a next-generation targeted panel sequencing approach whereby the coding and non-coding regions of LRRK2 were targeted using molecular inversion probes (MIPs) as previously described27. The probes used to target LRRK2 can be found in Supplementary Data 7 along with the full protocol at https://github.com/gan-orlab/MIP_protocol. The Illumina NovaSeq 6000 SP PE100 platform at the Genome Quebec Innovation Centre was used for the sequencing of the library. The sequenced data were aligned to the hg19 reference genome using the Burrows-Wheeler Aligner28.
As for the external cohorts, the UKBB whole-exome sequencing data was sequenced using the Illumina NovaSeq 6000 platform S2 and S4, and the AMP-PD whole-genome sequencing data was sequenced using the Illumina HiSeq X Ten platform, both being aligned to the hg38 reference genome29,30. We realigned our local cohorts from hg19 to hg38 to have consistency across all cohorts using CrossMap31. Finally, we extracted the part of the genome encoding for the LRRK2 gene at chr12:40224997- 40369285 for quality control and analysis.
Data quality control
We applied quality control steps at both sample and variant levels. In all cohorts, any first- and second-degree relatives were removed, keeping only unrelated samples. In UKBB, we applied an additional step to remove non-European samples based on UKBB data field 21,000. This step was also applied for AMP-PD where non-European samples were identified and excluded using admixture analysis, where we employed reference populations to detect genetic ancestry and ensure the exclusion of samples of non-European ancestry. Later, we used the genome analysis toolkit (GATK) for filtering based on minimum genotype quality (GQ) score and depth of coverage32. For the four cohorts sequenced at McGill University, GATK was used to apply thresholds of GQ > 25 and depth of coverage > 30x and to remove variants with genotyping rate of less than 90%. The sample depth of coverage data was averaged for LRRK2 for each of the four cohorts sequenced at McGill University. For UKBB whole-exome sequencing data, using GATK, thresholds were set for GQ > 20, depth of coverage > 10x, and genotyping rate > 95%, leaving only high-quality samples and variants. Applying the same stringent quality control parameters used for the McGill cohorts was not feasible for the UKBB due to differences in sequencing methods, and as it would lead to substantial loss of data for the latter. AMP-PD whole-genome sequencing data were pre-filtered by AMP-PD with steps detailed on their website (https://amp-pd.org/whole-genome-data) and in a previous study30. Across all cohorts, a minor allele frequency (MAF) threshold of 0.01 was applied to include only rare variants using plink v1.933, and multi-allelic variants were excluded using bcftools34. Exceptionally, we kept p.G2019S pathogenic variant in all cohorts (it has MAF of >0.01 in AJ) to understand the effect of its presence vs. absence on the LRRK2 Kinase burden for PD.
Domain demarcation
For domain-specific analysis, we demarcated the domains of LRRK2 based on the domain annotation resources available online at Ensembl35 (https://www.ensembl.org): PANTHER, Superfamily, Gene3D, Smart, Pfam, and Prosite36,37,38,39,40,41. The widest range was taken based on the aggregation of domain annotation data, and the genomic intervals were established for the following six domain regions: ARM, ANK, LRR, Roc-COR, Kinase, and WD40 (See Supplementary Data 8).
Variant annotation
As the study was particularly interested in studying LRRK2 domains, variants that were only located within these domain regions were included. We annotated variants using ANNOVAR42 with functional information, including the deleteriousness scores based on the Combined Annotation Dependent Depletion (CADD) v1.643. For further population-level insights, The Genome Aggregation Database (gnomAD) v4.1 was also used in the annotation process to match variants with MAF data across various populations44.
Burden analysis
To understand how functionally distinct variant groups could drive association differentially, we stratified variants into four categories: (1) Exonic: all exonic variants, (2) non-synonymous: all non-synonymous protein-change variants, (3) CADD ≥ 20: top 1% of potentially deleterious variants, and (4) Loss-of-function (LOF): all stopgain, frameshift and splice-site variants on splicing acceptor and donor sites (+1/−1 and +2/−2 positions). The four categories of LRRK2 variants were analyzed per-domain for their burden for PD via SKAT-O45. Further, MetaSKAT46 was used to meta-analyze all six cohorts. We included sex and age as covariates to account for potential confounding effects associated with these variables. The minimize the likelihood of false-positive findings, false discovery rate (FDR) corrections were applied to the results47.
Variant nomination
Restricting our investigation to domains that achieved at least nominal significance in either cohort-specific or meta-analytic burden tests, we conducted an observational assessment of non-synonymous variant frequencies among cases and controls. Variants were shortlisted and additionally evaluated with an Odds Ratio (ORs) analysis, both with and without continuity corrections. The continuity correction was applied by adding 0.5 to both cases and controls. Based on our observations, variants were nominated for subsequent structural analysis.
In silico structural analysis
The atomic coordinates of human LRRK2 were downloaded from the Protein Data Bank (ID 8FO2). The figure was generated using PyMol v.3.0.4.
Data availability
The datasets used in this study include those from the AMP-PD Knowledge Platform (https://www.amp-pd.org) and the UKBB, both of which require institutional approvals for access. Additionally, data from local cohorts sequenced at McGill University are currently restricted to internal use and collaborations as the informed consent forms do not allow for public sharing of genetic data. All data directly relevant to this study’s analysis, including the list of analyzed variants and demographics of the study population, are provided in the supplementary files of this article.
Code availability
The bioinformatics pipelines and scripts used in this study can be found in the study’s GitHub repository at: https://github.com/gan-orlab/LRRK2_per_domain.
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Acknowledgements
We would like to express our gratitude to the participants from various cohorts for their contributions to this study. This research was conducted using the NeuroHub infrastructure and was supported in part by funding from the Canada First Research Excellence Fund, awarded through the Healthy Brains, Healthy Lives initiative at McGill University, as well as by Calcul Québec and the Digital Research Alliance of Canada. Additionally, the study utilized data from the UK Biobank under Application Number 45551. Data for this article were also obtained from the Accelerating Medicines Partnership® (AMP®) Parkinson’s Disease (AMP PD) Knowledge Platform. For the latest information on the study, please visit https://www.amp-pd.org. The AMP® PD program is a public-private collaboration managed by the Foundation for the National Institutes of Health and supported by the National Institute of Neurological Disorders and Stroke (NINDS) alongside partners such as the Aligning Science Across Parkinson’s (ASAP) initiative; Celgene Corporation (a subsidiary of Bristol-Myers Squibb); GlaxoSmithKline (GSK); The Michael J. Fox Foundation for Parkinson’s Research; Pfizer Inc.; Sanofi US Services Inc.; Verily Life Sciences; and AbbVie. ACCELERATING MEDICINES PARTNERSHIP and AMP are registered trademarks of the U.S. Department of Health and Human Services. Genetic data for this study were also drawn from the Fox Investigation for New Discovery of Biomarkers (BioFIND), the Harvard Biomarker Study (HBS), the Parkinson’s Progression Markers Initiative (PPMI), the Parkinson’s Disease Biomarkers Program (PDBP), the International LBD Genomics Consortium (iLBDGC), and the STEADY-PD III trial. BioFIND is supported by The Michael J. Fox Foundation for Parkinson’s Research (MJFF) with backing from the National Institute for Neurological Disorders and Stroke (NINDS). The BioFIND Investigators did not participate in reviewing the data analysis or the content of this manuscript. More information on the BioFIND study is available at michaeljfox.org/news/biofind. The Harvard Biomarker Study (HBS) is a collaboration of HBS investigators (a full list can be found at https://www.bwhparkinsoncenter.org/biobank/), funded through both philanthropic donations and NIH and non-NIH sources. The HBS investigators have not reviewed the data analysis or the content of this manuscript. PPMI is sponsored by The Michael J. Fox Foundation for Parkinson’s Research, with support from a consortium of scientific partners (a full list of partners is available at https://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors). The PPMI investigators have not reviewed the data analysis or content of this manuscript. Additional details on the PPMI study can be found at www.ppmi-info.org. The Parkinson’s Disease Biomarker Program (PDBP) consortium is funded by the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health. A full list of PDBP investigators is available at https://pdbp.ninds.nih.gov/policy. The PDBP investigators have not reviewed the data analysis or the content of this manuscript. The STEADY-PD3 trial (Study of Isradipine as a Disease-Modifying Agent in Early Parkinson’s Disease, Phase 3) is funded by the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health, with additional support from The Michael J. Fox Foundation and the Parkinson Study Group. More information about the trial is available at https://clinicaltrials.gov/ct2/show/study/NCT02168842. The STEADY-PD3 investigators have not reviewed the data analysis or content of this manuscript. Genome sequencing data for the Lewy body dementia case-control cohort were generated at the Intramural Research Program of the U.S. National Institutes of Health, with support from the National Institute on Aging (program #: 1ZIAAG000935) and the National Institute of Neurological Disorders and Stroke (program #: 1ZIANS003154). This study was financially supported by grants from the Galen and Hilary Weston Foundation, the Michael J. Fox Foundation, the Canada First Research Excellence Fund (CFREF), awarded to McGill University for the Healthy Brains for Healthy Lives initiative (HBHL), and Parkinson Canada. The Columbia University cohort is supported by the Parkinson's Foundation, the National Institutes of Health (K02NS080915 and UL1 TR000040), and the Brookdale Foundation. ZGO is supported by the Fonds de recherche du Québec - Santé (FRQS) Chercheurs-boursiers award and is a William Dawson Scholar. Access to some of the participants in this study was made possible through the Quebec Parkinson’s Network (http://rpq-qpn.ca/en/). SCP is supported by the Canadian Institutes of Health Research Canada Graduate Scholarships – Master's (CGS-M) scholarship.
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S.C.P. was responsible for the conception, organization, and execution of the research project, the design, acquisition, and analysis of the data, and writing the first draft of the manuscript. K.S. contributed to the conception and organization of the research project, the design of the analysis, and provided manuscript review and critique. E.Y., J.A.R., J.A., F.A., D.S., C.W., O.M., Y.D., N.D., L.G., S.H.B., I.M., A.T., A.E., and S.P. were involved in the acquisition of data for the research project, provided critique of the analysis, and reviewed and revised the manuscript. R.N.A. contributed to the conception and execution of the research project, provided critique for the analysis, and reviewed the manuscript. E.A.F. was responsible for reviewing the analysis and manuscript critique. J.F.T. contributed to the analysis of the data, provided critique for the analysis, and reviewed the manuscript. Z.G.O. contributed to the conception and organization of the research project, the design of the analysis, and provided manuscript review and critique. All authors read and approved the final manuscript.
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Z.G.O. received consultancy fees from Lysosomal Therapeutics Inc. (LTI), Idorsia, Prevail Therapeutics, Ono Therapeutics, Denali, Handl Therapeutics, Neuron23, Bial Biotech, Bial, UCB, Capsida, Vanqua Bio, Congruence Therapeutics, Takeda, Jazz Pharmaceuticals, Guidepoint, Lighthouse, and Deerfield.
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Parlar, S.C., Senkevich, K., Yu, E. et al. LRRK2 rare-variant per-domain genetic burden in Parkinson’s Disease: association confined to the kinase domain. npj Parkinsons Dis. 11, 102 (2025). https://doi.org/10.1038/s41531-025-00934-z
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DOI: https://doi.org/10.1038/s41531-025-00934-z
