Genome-wide association study of glucocerebrosidase activity modifiers.

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Emma N. Somerville, Lynne Krohn, Konstanin Senkevich, Eric Yu, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4425669/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Molecular Neurobiology → Version 1 posted 13 You are reading this latest preprint version Abstract One of the most common genetic risk factors for Parkinson’s disease (PD) are variants in GBA1 , which encodes the lysosomal enzyme glucocerebrosidase (GCase). GCase deficiency has been associated with an increased PD risk, but not all individuals with low GCase activity are carriers of GBA1 mutations, suggesting other factors may be acting as modifiers. We aimed to discover common variants associated with GCase activity, as well as replicate previously reported associations, by performing a genome-wide association study using two independent cohorts: a Columbia University cohort consisting of 697 PD cases and 347 controls and the Parkinson’s Progression Markers Initiative (PPMI) cohort consisting of 357 PD cases and 163 controls. As expected, GBA1 variants have the strongest association with decreased activity, led by p.N370S (beta = -4.36, se = 0.32, p = 5.05e-43). We also identify a novel association in the GAA locus (encoding for acid alpha-glucosidase, beta = -0.96, se = 0.17, p = 5.23e-09) that may be the result of an interaction between GCase and acid alpha-glucosidase based on various interaction analyses. Lastly, we show that several PD-risk loci are potentially associated with GCase activity. Further research will be needed to replicate and validate our findings and to uncover the functional connection between acid alpha-glucosidase and GCase. Genome-wide association study glucocerebrosidase Parkinson’s disease GBA1 lysosomal metabolism Figures Figure 1 Figure 2 Figure 3 1. Introduction Parkinson’s disease (PD) is a complex neurodegenerative disorder, characterized by accumulation of alpha synuclein in Lewy bodies and a progressive loss of dopaminergic neurons in the substantia nigra [ 1 ]. Although the exact mechanisms remain unknown, there is a clear role for genetic factors in PD. One gene of particular interest is GBA1 . In addition to being one of the most common genetic risk factors associated with PD, it is also associated with a faster rate of motor and non-motor progression [ 2 ]. GBA1 encodes for glucocerebrosidase (GCase), a lysosomal hydrolase whose biallelic deficiency causes the lysosomal storage disorder (LSD) known as Gaucher disease [ 3 ]. Heterozygous GBA1 variants associated with reduced GCase activity are important risk factors for both PD and dementia with Lewy bodies (DLB), and a substantial portion of patients with these disorders have reduced GCase activity albeit not carrying GBA1 variants [ 4 , 5 ]. This observation suggests that other factors, genetic or environmental, may modify GCase activity. One such factor may be TMEM175 variants, which have been associated with reduced GCase activity in patient data [ 6 ] and in cell models [ 7 ]. Additionally, variants in LRRK2 have also been linked to modified GCase activity, although the directionality of the effect is still unclear [ 8 , 4 , 9 , 10 ]. Understanding the genetic variation influencing GCase activity could be informative in several ways. First, it can provide a better understanding of the mechanisms underlying the role GCase plays in PD. Furthermore, it would allow for proper adjustments or stratification in future research and clinical trials. In the present study, we aimed to identify genetic factors associated with GCase activity by performing a genome-wide association study (GWAS) including a total of 1054 PD cases and 510 healthy controls. A secondary analysis was conducted to identify PD-associated variants that may act as modifiers of GCase activity. 2. Methods 2.1 Study Population The study population consisted of two separate cohorts with available genotype and GCase activity data: 1) a cohort of 697 PD cases and 347 controls collected from Columbia University in New York, and 2) a cohort of 357 PD cases and 163 controls from the Parkinson’s Progression Markers Initiative (PPMI). Cohort demographics can be found in Table 1 . Both cohorts have been previously described [ 4 , 9 ]. All subjects were of European descent, confirmed with principal component analysis. Informed consent forms were signed by all participants prior to entering their respective studies and the study protocol was approved by the institutional review boards. Table 1 Cohort demographics for individuals with available genotype and GCase data. Cohort N Cases N Controls Status Males (%) Females (%) Mean Age (± SD) Columbia 697 347 cases 65.1 34.9 65.74 (11.05) controls 38.3 61.7 64.25 (10.05) PPMI 357 163 cases 66.7 33.3 61.85 (9.49) controls 67.5 32.5 60.79 (11.34) N, number; SD, standard deviation; PPMI, Parkinson’s Progression Markers Initiative. 2.2 Enzyme Activity Enzymatic activity for GCase, acid alpha-glucosidase (GAA), acid sphingomyelinase (ASM), alpha-galactosidase A (GLA), and galactosylceramidase (GALC) was measured from dried blood spots in the Columbia cohort, the protocol for which has been previously described [ 11 , 12 ]. To summarize, enzyme activity was measured by Sanofi laboratories using liquid chromatography-tandem mass spectrometry (LC-MS/MS) from dried blood spots, using a multiplex assay. The dried blood spots were incubated in a reaction cocktail with substrates for the enzymes and buffer. Enzyme activity measurements were determined by calculating the amount of product obtained after the incubation, under the assumption that this is a direct indication of the activity of the enzymes. Lysosomal enzyme activity for the PPMI cohort was measured from frozen whole blood, which was slowly thawed and the processed similarly to the Columbia cohort. GCase outliers were identified as those with activity measurements lying outside +/- 1.5 times the inter quartile range (IQR), and were subsequently removed in both cohorts. 2.3 Genome-Wide Association Study Genotyping was performed with the OmniExpress GWAS array for the Columbia cohort and the NeuroX array for the PPMI cohort, according to manufacturer’s protocols (Illumina Inc.). Quality control for individual and variant data was completed as previously described ( https://github.com/neurogenetics/GWAS-pipeline ). In brief, samples that were heterozygosity outliers (inclusion criteria of -0.15 < = F 95%), had mismatched genetic and reported sex, or were identified as European ancestry outliers based on HapMap3 principal component analysis (PCA) in plink v1.9 were removed [ 13 ]. Additionally, we removed samples with relatedness closer than 3rd degree relatives (pihat > 0.125). Individual SNPs were excluded on the basis of variant missingness (> 95%), differences in missingness between cases and controls (p < 1e-04), haplotype missingness (p < 1e-04), and deviation from Hardy-Weinberg equilibrium in controls (p < 1e-04). Imputation was performed on filtered data with the Michigan Imputation Server using the Haplotype Reference Consortium reference panel r1.1 2016 and default settings [ 14 ]. Linear regressions of GCase activity were performed in plink v1.9 using hard-call variants (R 2 > 0.8) and a minor allele frequency (MAF) threshold of > 0.01. We manually added the PD-relevant variant GBA1 p.N370S to the Columbia analyses, as well as GBA1 p.T369M and LRRK2 p.G2019S after filtering, due to all being under the minor allele threshold. The Columbia cohort included adjustments for age, sex, disease status, Ashkenazi Jewish ancestry, LRRK2 p.G2019S status, and lysosomal enzyme activities (GAA, GLA, GALC, and ASM) to examine the isolated effect on GCase activity. The p.G2019S mutation was included as a covariate due it being a known GCase modifier [ 4 , 9 , 8 ]. Lysosomal enzymes were adjusted for because they have been shown to correlate with one another in a previous study [ 15 ]. The PPMI analysis included the same adjustments, with the addition of white blood cell count (WBC) as previously suggested [ 9 ]. Fixed-effect meta-analyses of PPMI and Columbia cohorts were performed with METAL in plink v1.9. Principal components (PCs) were calculated using PCA with plink v1.9 and the top 10 PCs were included as covariates for each cohort. Conditional and joint analyses were performed with GTCA-COJO to identify independent variants after adjusting for lead SNPs [ 16 ]. ANOVA, linear regressions with interaction term, and interaction plots were created in R v4.3.1 [ 17 ]. Linkage disequilibrium Manhattan plots were created using LocusZoom [ 18 ]. 2.4 Data Statement All code for analyses used in this project can be found at https://github.com/gan-orlab/GCase_GWAS . PPMI data used for this study can be obtained by qualified researchers upon completion of a data access application ( https://www.ppmi-info.org/access-data-specimens/download-data ). Columbia cohort data can be obtained by request. The GCase GWAS summary statistics can be found on the GWAS catalog ( https://www.ebi.ac.uk/gwas/ ). 3. Results 3.1 GAA and GBA1 loci are associated with GCase activity. After quality control and imputation, a total of 556 cases, 284 controls and 13327381 variants in the Columbia cohort and 328 cases, 144 controls and 941882 variants in the PPMI cohort were available for analysis. Mean GCase activity was similar between Columbia (mean = 11.1 umol/l/h, sd = +/- 3.14) and PPMI (mean = 11.63 umol/l/h, sd = +/- 2.59). We performed a GWAS to identify potential associations between common variants and GCase activity in both cohorts separately, followed by a meta-analysis. We looked at the genomic inflation factors and QQ plots of each analysis to test for systematic bias, and found them to be acceptable (Columbia λ = 0.99, PPMI λ = 1.04, Supplementary Fig. 1). The strongest associated locus in Columbia was in the GBA1 locus, as expected (Fig. 1 A). This signal is driven by p.N370S (rs76763715, beta = -4.21, se = 0.35, p = 4.55e-31), which is in linkage disequilibrium (LD) with the lead SNP in the locus (rs745550122, beta = -4.46, se = 0.35, p = 6.61e-34, R 2 = 0.78, D` = 0.91). A second signal can be seen in chromosome 17 in the GAA locus, with the strongest associated variants comprising a high-LD region (Supplementary Fig. 2) and the lead SNP having a negative direction of effect (beta = -0.96, se = 0.17, p = 7.55e-9). Applying conditional and joint analyses revealed no secondary independent associations in either signal. The GBA1 signal was replicated in the PPMI cohort, with p.N370S once again driving the association (Fig. 1 B, beta = -5.05, se = 0.76, p = 1.25e-10). The GAA locus does surpass nominal significance in PPMI, although the lead SNP has an opposite direction of effect compared to the lead SNP from Columbia (Table 2 ). The meta-analysis likewise showed associations in the GBA1 and GAA loci, but did not result in any additional associations (Fig. 1 C). The GBA1 variants p.T369M (rs75548401) and p.E326K (rs2230288) were also associated with GCase activity, although not at GWAS significance (Table 3 ). Both variants had similar effect sizes, directions, and p-values across both analyses. Table 2 Comparison of lead GAA locus variants in Columbia and PPMI cohorts. SNP Cohort beta se p-value 17:78061141:T:G Columbia -1.009 0.18 1.15e-08 PPMI -0.29 0.19 0.14 17:78096483:A:C Columbia 0.39 0.162 0.017 PPMI 0.60 0.18 7.44e-4 SNP, single nucleotide polymorphism; se, standard error; PPMI, Parkinson’s Progression Markers Initiative. Table 3 GBA1 p.T369M and p.E326K association statistics for Columbia and PPMI cohorts. Variant Cohort beta se p-value p.T369M Columbia -3.28 0.78 2.79e-05 PPMI -2.91 0.68 2.11e-05 Meta-analysis -3.067 0.51 1.787e-09 p.E326K Columbia -1.62 0.59 0.0063 PPMI -1.43 0.5 0.0047 Meta-analysis -1.51 0.38 8.027e-05 se, standard error; PPMI, Parkinson’s Progression Markers Initiative. Interestingly, analysis in the Columbia cohort without adjusting for acid alpha-glucosidase activity eliminates any association from the GAA locus (Fig. 2 A). Adding adjustments for common PD-relevant GBA1 variants p.N370S, p.E326K and p.T369M results in both the GBA1 and GAA peaks dropping below GWAS-level significance (Fig. 2 B). To better understand if these outcomes could be due to an interaction between the acid alpha-glucosidase and GCase enzymes, we constructed an interaction plot of enzyme activities by genotype of the top GAA SNP that was common in both cohorts (Fig. 2 B). The genotype of the lead SNP in the GAA locus (17:78061141:T:G) appears to impact the interaction of GCase and acid alpha-glucosidase enzyme activities in Columbia, with correlations becoming progressively weaker in an additive manner with the addition of the T allele (Fig. 3). This trend is not as clear in controls, which could be due to the lower number of samples in this group. The effect is also not apparent in PPMI, reflective of the lack of association in this cohort. To further investigate the presence of an interaction, both an ANOVA and a linear regression using an interaction term were performed. The ANOVA used the absolute difference between GCase and acid alpha-glucosidase activity, which did significantly vary by the genotype status of the lead GAA variant (df = 2, Sum Sq = 130, Mean Sq = 64.75, F = 13.62, p = 1.52e-06). The linear regression was performed using the same covariates as the previous analysis, with the addition of a GAA *GCase interaction term. The results indicated that the T/T genotype status was significant (beta = -0.25, se = 0.13, p = 0.048), supporting the presence of an interaction effect between GAA genotype status, acid alpha-glucosidase activity, and GCase activity. To examine if the GAA locus has any role in PD, we investigated the association of this region with PD risk and various measurements of progression using GWAS summary statistics from previous studies. We identified 26, 4, 5, 79, and 6 variants that were nominally associated in the GWASs of PD risk, age at onset, UPDRS III, MoCA, and MMSE scores, respectively (Supplementary Table 1), although no variants passed Bonferroni correction for multiple comparisons [ 19 – 21 ]. There were no GAA locus variants even nominally associated in the GWASs of motor, cognitive, and composite progression [ 22 ]. Of the associated variants, a total of 8, 2, 33, and 1 variants in the GWASs of PD risk, age at onset, MoCA, and MMSE scores were also nominally associated with GCase activity in the present meta-analysis. 3.2 PD-related variants are associated with GCase activity. We investigated PD-related variants nominated from the largest PD GWAS to date to determine if any were associated with GCase activity in our cohorts (Nalls et al., 2019). There were 17, 7 and 14 loci below nominal significance in the main GWAS for Columbia, PPMI and the meta-analysis, respectively (Table 4 ). Of these, only p.N370S (rs76763715) and rs35749011 (in strong LD with p.E326K) in the GBA1 locus, as well as rs13117519 in the ANK2 locus passed multiple testing correction in the meta-analysis. We then attempted to replicate previously reported associations in TMEM175 and LRRK2 with GCase activity, by investigating these loci in a simplified meta-analysis using only age, sex, disease status, 10 PCs, and PD-relevant GBA1 SNPs including p.N370S, p.E326K, and p.T369M as covariates (Supplementary Fig. 3). There were 3 intronic variants in LRRK2 that passed nominal significance (rs116911375, beta = -1.7, se = 0.79, p = 0.03; rs191242488, beta = -1.041, se = 0.52, p = 0.044; 12:40668909:T:G, beta = -1.041, se = 0.52, p = 0.044), and no variants in TMEM175 . No variants passed multiple testing correction. The nominally significant variants do not appear to be in LD with p.G2019S or p.M1646T. Although these variants are not associated with GCase activity in our analysis, p.G2019S and p.M1646T do show a consistent positive direction of effect with what has been demonstrated in previous research (Table 5 ) [ 4 , 9 , 10 ]. The difference seen in our results compared to the nominal association of p.M1646T found in a very similar analysis in these cohorts performed by Sosero et al. [ 10 ] is due to the removal of GCase outliers in our study. With the inclusion of outliers, we also see an association of this variant in our meta-analysis (b = 1.16, se = 0.38, p = 0.002, rs35303786). Table 4 Parkinson’s disease risk SNPs associated with changes in GCase activity. Cohort SNP rsID Beta SE MAF P Locus Columbia 1:155205634:T:C rs76763715 -4.21 0.35 0.041 4.55E-31 GBA1 20:6006041:C:T rs77351827 0.63 0.19 0.16 0.00083 CRLS1 6:112243291:A:G rs997368 -0.49 0.16 0.24 0.0017 FYN 4:114369065:C:T rs13117519 0.53 0.19 0.16 0.006 ANK2 4:90666041:C:T rs356203 0.37 0.14 0.41 0.008 SNCA 4:90626111:G:A rs356182 0.36 0.14 0.37 0.011 SNCA 1:155135036:G:A rs114138760 -1.61 0.64 0.012 0.012 GBA1 21:38852361:G:A rs2248244 0.39 0.16 0.26 0.012 DYRK1A 4:90636630:G:A rs5019538 0.33 0.14 0.33 0.022 SNCA 17:43933307:T:C rs7221167 -0.3 0.14 0.4 0.029 MAPT 14:37989270:T:C rs12147950 -0.3 0.14 0.43 0.032 MIPOL1 4:90607126:C:G rs356228 0.28 0.13 0.46 0.034 SNCA 18:31304318:G:T rs1941685 -0.29 0.14 0.47 0.037 ASXL3 3:28705690:T:C rs6808178 0.3 0.14 0.36 0.039 LINC00693 13:97865021:T:C rs4771268 -0.32 0.16 0.28 0.044 MBNL2 14:95194760:G:C rs4905237 0.38 0.19 0.15 0.047 SERPINA13P PPMI 1:155205634:T:C rs76763715 -5.048 0.76 0.0087 1.25E-10 GBA1 1:155135036:G:A rs35749011 -1.43 0.5 0.02 0.0047 GBA1 1:154898185:G:C rs114138760 -1.61 0.62 0.014 0.0097 GBA1 4:114369065:C:T rs13117519 0.48 0.19 0.19 0.01 ANK2 1:171719769:C:T rs11578699 0.47 0.19 0.19 0.012 VAMP4 14:88464264:G:T rs979812 -0.48 0.2 0.45 0.018 GALC 6:32578772:C:A rs504594 -0.46 0.22 0.14 0.035 HLA-DRB1 Meta 1:155205634:T:C rs76763715 -4.36 0.32 0.036 5.05E-43 GBA1 1:155135036:G:A rs35749011 -1.50 0.39 0.017 0.00014 GBA1 4:114369065:C:T rs13117519 0.50 0.13 0.17 0.00016 ANK2 20:6006041:C:T rs77351827 0.45 0.14 0.15 0.0013 CRLS1 4:90666041:C:T rs356203 0.31 0.1 0.41 0.0028 SNCA 4:90636630:G:A rs5019538 0.3 0.11 0.33 0.0041 SNCA 6:112243291:A:G rs997368 -0.34 0.12 0.22 0.0053 FYN 14:88464264:G:T rs979812 -0.34 0.13 0.44 0.0068 GALC 4:90626111:G:A rs356182 0.2784 0.1037 0.3811 0.007286 SNCA 1:154898185:G:C rs114138760 -1.606 0.618 0.0138 0.009358 GBA1 21:38852361:G:A rs2248244 0.3915 0.1556 0.2634 0.01187 DYRK1A 18:31304318:G:T rs1941685 -0.2872 0.1376 0.4749 0.03687 ASXL3 4:90607126:C:G rs356228 0.2054 0.1005 0.4607 0.04097 SNCA 2:135438789:G:A rs4954162 0.2489 0.1246 0.2193 0.04585 TMEM163 14:95194760:G:C rs4905237 0.3794 0.1904 0.1481 0.0463 SERPINA13P SNP, single nucleotide polymorphism; se, standard error; MAF, minor allele frequency; PPMI, Parkinson’s Progression Markers Initiative. Table 5 LRRK2 p.G2019S and p.M1646T association statistics for Columbia and PPMI cohorts. Variant Cohort beta se p-value p.G2019S Columbia 0.64 0.4 0.11 PPMI -0.053 1.12 0.96 Meta-analysis 0.56 0.56 0.14 p.M1646T Columbia 0.41 0.45 0.36 PPMI 0.82 0.54 0.13 Meta-analysis 0.58 0.58 0.094 se, standard error; PPMI, Parkinson’s Progression Markers Initiative. 4. Discussion In the present GWAS, we identified variants in the GAA locus as potential modifiers of GCase activity, in addition to the known associations with GBA1 variants. GAA encodes acid alpha-glucosidase, and deficiency in this enzyme leads to the LSD Pompe disease [ 23 ]. There is no known association between Pompe disease and PD, and the variants in the GAA locus are not associated with PD risk, age at onset, or various measures of progression. This is an important observation, since it may suggest that changes in GCase activity alone are insufficient to cause PD, or that the effects of GAA variants on GCase activity are too small to have a meaningful clinical effect. It was hypothesized that the mechanism underlying GBA1 -associated PD is an imbalance, or disturbance in the flux within the lysosomal glycosphingolipid metabolism pathway, rather than reduced GCase activity on its own [ 24 ]. This is supported by the identification of other genes in this pathway that are associated with PD, such as SMPD1 , GALC , ARSA and ASAH1 [ 25 – 27 , 12 , 28 – 32 ]. Previous work has also demonstrated that lysosomal enzymes are highly correlated, especially for GCase, acid alpha-glucosidase, and alpha galactosidase A which is encoded by GLA [ 15 ]. Taken into consideration with the interaction effect observed in the present study, there seems to be mounting evidence in favor of this hypothesis. The mechanism behind the interaction of GCase and acid alpha-glucosidase is still unknown. The products of GCase are lipids and glucose, while acid alpha-glucosidase hydrolyzes glycogen into glucose [ 33 ]. Our observation of genotype-dependent correlation between GCase and acid alpha-glucosidase activities may therefore indicate that the GAA locus variants associated with GCase activity could maintain equilibrium in this pathway, as they affect both GCase and alpha-glucosidase activities. This hypothesis requires additional genetic, enzymatic, and functional studies. We further examined the association between known PD-associated loci and GCase activity. As to be expected, we found three PD variants in the GBA1 locus to be associated with GCase activity. We also found an additional association with the ANK2 locus. Phosphorylation in the ANK2 region has been proposed to play a role in PD neurodegeneration through the inhibition of organelle autophagy, but its connection to GCase activity is not clear from the current literature and will need further research [ 34 ]. There are also multiple notable associations with ties to GCase activity that may have only failed multiple testing correction due to sample size. One such locus is GALC , encoding the enzyme galactosylceramidase which, similarly to GCase, breaks down large sphingolipids into lipids and ceramide. The association of GALC variants with GCase activity may suggest that a disturbance in lysosomal metabolism could be a causal mechanism in PD, which is also supported by previous research which found galactosylceramide and GCase to be correlated [ 15 ]. We also found nominal associations with multiple SNCA variants, complimenting previous associations found between levels of alpha synuclein aggregates and Gcase activity [ 35 – 37 ]. A third interesting locus that fell just shy of the multiple testing correction threshold was FYN . This gene encodes a kinase that has been shown to phosphorylate alpha-synuclein, which is a key step towards its aggregation into Lewy bodies [ 38 ]. We did not replicate previously reported associations with LRRK2 or TMEM175 variants, which is likely due to additional adjustments in our study and the removal of GCase outliers compared to previous studies. Studies in larger cohorts will be required to confirm the associations of these PD loci. This study has several limitations. The first limitation is a lack of power. Our discovery and replication analyses utilized the largest cohorts with enzyme activity measurements that we could find at the time of performing the study and allowed us to complete the first GWAS of GCase activity, but only enabled us to discover common variants with moderate effect sizes or rare variants with large effect sizes. Due to this, there are likely more modulators of GCase activity yet to be uncovered. A second limitation is the use of only individuals of European and Ashkenazi Jewish ancestry, as there was insufficient data to carry out the study in other populations. Next, our study had minor differences in age and notable differences in sex, particularly in the Columbia cohort, between cases and controls. Both age and sex were adjusted for in all analyses to account for these differences. An additional limitation is the method of enzyme measurement used. Lysosomal activity was measured using dried blood spots, which is not guaranteed to capture the true enzyme activity within a functioning lysosomal environment. These results will strengthen if replicated in future studies that use lysosomal-specific methods of enzyme activity, in cells more representative of disease pathogenesis. In conclusion, we found a novel potential association in the GAA locus associated with GCase activity, which may represent an interaction effect between GCase and acid alpha-glucosidase and could be indicative of a homeostatic relationship between lysosomal enzymes. We also support previously suggested connections of multiple PD-related genes with GCase activity, such as GALC and SNCA , as well as identifying a novel potential association with ANK2 . These findings could be significant for improving our understanding of how GCase deficiency is related to overall lysosomal dysfunction, and how GCase functions in relation to other lysosomal enzymes. Additionally, associated loci can be taken into account in future research and clinical trials to help control for genetic influences on GCase activity. Due to the novelty of our results and lack of confident association in our replication cohort, further genetic and functional research will be required to validate our findings. Declarations We would like to thank the research participants for contributing to this study. We thank Meron Teferra for her assistance. 5.1 Funding This study was financially supported through grants from the Michael J. Fox Foundation (MJFF, No. 020700), the Canadian Consortium on Neurodegeneration in Aging (CCNA, No. 049-14), and the Canada First Research Excellence Fund (CFREF, No. 247908), awarded to McGill University for the Healthy Brains for Healthy Lives initiative (HBHL). The Columbia cohort was funded by the National Institutes of Health (No. K02NS080915, and No. UL1 TR000040, formerly the National Center for Research Resources, Grant No. UL1 RR024156). PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson's, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Jazz Pharmaceuticals, Johnson & Johnson Innovative Medicine, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuron23, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager. The G-Can (GBA1-Canada) Initiative, an open-science collaborative initiative aimed at addressing GBA1 mutation based Parkinson's disease, has made contributions to this research. G-Can is supported by The Hilary & Galen Weston Foundation, Silverstein Foundation, and J. Sebastian van Berkom and Ghislaine Saucier. Z.G.O. is supported by the Fonds de recherche du Québec—Santé (FRQS) Chercheurs-boursiers award and is a William Dawson Scholar. K.S. is supported by a post-doctoral fellowship from the Canada First Research Excellence Fund (CFREF), awarded to McGill University for the Healthy Brains for Healthy Lives initiative (HBHL). E.N.S. gratefully acknowledges funding support for this research from Parkinson Canada. 5.2 Competing Interests 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 Guidepoint, Lighthouse and Deerfield. 5.3 Author Contributions E.N.S. conducted all analyses, interpreted data, developed the methodology, and wrote the manuscript. L.K. developed the methodology and contributed to data analysis and result interpretation. K.S. and E.Y. contributed to data analysis and result interpretation. J.A., F.A., J.A.R., D.S., S.F., C.W., and S.P.S. performed data collection and curation. R.N.A. developed the methodology and performed data collection and curation. Z.G.O. developed the research idea, developed the methodology, contributed to result interpretation, and supervised the study. All authors read, approved, and contributed to editing the final manuscript. 5.4 Data Availability Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data). For up-to-date information on the study, visit www.ppmi-info.org. Columbia cohort data can be obtained by request. The GCase GWAS summary statistics can be found on the GWAS catalog (https://www.ebi.ac.uk/gwas/). All code for analyses used in this project can be found at https://github.com/gan-orlab/GCase_GWAS. PPMI data used for this study can be obtained by qualified researchers upon completion of a data access application (https://www.ppmi-info.org/access-data-specimens/download-data). 5.5 Ethics Approval and Consent to Participate Informed consent forms were signed by all participants prior to entering their respective studies and the study protocol was approved by the institutional review boards. 5.6 Consent to Publish All authors have read and consented to publish this manuscript. References Armstrong MJ, Okun MS (2020) Diagnosis and Treatment of Parkinson Disease: A Review. JAMA 323(6):548–560. 10.1001/jama.2019.22360 Gan-Or Z, Liong C, Alcalay RN (2018) GBA-Associated Parkinson's Disease and Other Synucleinopathies. Curr Neurol Neurosci Rep 18(8):44. 10.1007/s11910-018-0860-4 Hruska KS, LaMarca ME, Scott CR, Sidransky E (2008) Gaucher disease: mutation and polymorphism spectrum in the glucocerebrosidase gene (GBA). 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activity, GLA activity, GALC activity, and the top 10 PCs, b) the PPMI cohort with adjustments for age, sex, disease status, \u003cem\u003eLRRK2\u003c/em\u003e G2019S genotype, ASM activity, GAA activity, GLA activity, GALC activity, white blood cell count, and the top 10 PCs, and c) the meta-analysis of these two analyses.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4425669/v1/5319b9b844008c530c26c0c4.png"},{"id":57949168,"identity":"e97263c4-cc74-4690-92b6-1b0a067a21a9","added_by":"auto","created_at":"2024-06-07 20:43:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94362,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot of log adjusted p-values at each genomic position in the Columbia cohort with adjustments for a) age, sex, disease status, Ashkenazi Jewish status, \u003cem\u003eLRRK2\u003c/em\u003e p.G2019S, ASM activity, GLA activity, GALC activity, and the top 10 PCs, and b) age, sex, disease status, Ashkenazi Jewish status, \u003cem\u003eLRRK2\u003c/em\u003e p.G2019S, \u003cem\u003eGBA1\u003c/em\u003e p.N370S, \u003cem\u003eGBA1\u003c/em\u003ep.T369M, \u003cem\u003eGBA1\u003c/em\u003e p.E326K, ASM activity, GAA activity, GLA activity, GALC activity, and the top 10 PCs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4425669/v1/a806bab0d98688414c4e3028.png"},{"id":57949169,"identity":"c6791f49-2775-4e20-8281-4f8c94d3b60d","added_by":"auto","created_at":"2024-06-07 20:43:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213194,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction plots of GCase and acid alpha-glucosidase activity colored by 17:78061141:T:G genotype in a) Columbia and b) PPMI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4425669/v1/e9637afbf492b018b5932d13.png"},{"id":81987703,"identity":"6b26789a-e285-4b58-bf74-7aaebd636379","added_by":"auto","created_at":"2025-05-05 16:05:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1295678,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4425669/v1/4ab01814-1f82-43b0-86d3-342475152751.pdf"},{"id":57949684,"identity":"a601c611-7535-42bd-89d6-0204bcc3fd83","added_by":"auto","created_at":"2024-06-07 20:51:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":804963,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4425669/v1/44d995c01861457c2df147c9.docx"},{"id":57949171,"identity":"a84fded3-0e31-4833-b665-1d697ddbc121","added_by":"auto","created_at":"2024-06-07 20:43:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19607,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4425669/v1/b36779dd6e5a147d56a5ed51.xlsx"}],"financialInterests":"Competing interest reported. 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 Guidepoint, Lighthouse and Deerfield.","formattedTitle":"Genome-wide association study of glucocerebrosidase activity modifiers.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a complex neurodegenerative disorder, characterized by accumulation of alpha synuclein in Lewy bodies and a progressive loss of dopaminergic neurons in the substantia nigra [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although the exact mechanisms remain unknown, there is a clear role for genetic factors in PD. One gene of particular interest is \u003cem\u003eGBA1\u003c/em\u003e. In addition to being one of the most common genetic risk factors associated with PD, it is also associated with a faster rate of motor and non-motor progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eGBA1\u003c/em\u003e encodes for glucocerebrosidase (GCase), a lysosomal hydrolase whose biallelic deficiency causes the lysosomal storage disorder (LSD) known as Gaucher disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Heterozygous \u003cem\u003eGBA1\u003c/em\u003e variants associated with reduced GCase activity are important risk factors for both PD and dementia with Lewy bodies (DLB), and a substantial portion of patients with these disorders have reduced GCase activity albeit not carrying \u003cem\u003eGBA1\u003c/em\u003e variants [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This observation suggests that other factors, genetic or environmental, may modify GCase activity. One such factor may be \u003cem\u003eTMEM175\u003c/em\u003e variants, which have been associated with reduced GCase activity in patient data [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and in cell models [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, variants in \u003cem\u003eLRRK2\u003c/em\u003e have also been linked to modified GCase activity, although the directionality of the effect is still unclear [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the genetic variation influencing GCase activity could be informative in several ways. First, it can provide a better understanding of the mechanisms underlying the role GCase plays in PD. Furthermore, it would allow for proper adjustments or stratification in future research and clinical trials. In the present study, we aimed to identify genetic factors associated with GCase activity by performing a genome-wide association study (GWAS) including a total of 1054 PD cases and 510 healthy controls. A secondary analysis was conducted to identify PD-associated variants that may act as modifiers of GCase activity.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThe study population consisted of two separate cohorts with available genotype and GCase activity data: 1) a cohort of 697 PD cases and 347 controls collected from Columbia University in New York, and 2) a cohort of 357 PD cases and 163 controls from the Parkinson\u0026rsquo;s Progression Markers Initiative (PPMI). Cohort demographics can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Both cohorts have been previously described [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. All subjects were of European descent, confirmed with principal component analysis. Informed consent forms were signed by all participants prior to entering their respective studies and the study protocol was approved by the institutional review boards.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCohort demographics for individuals with available genotype and GCase data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN Cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMales (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFemales (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean Age (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eColumbia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ecases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e65.74 (11.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003econtrols\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.25 (10.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePPMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ecases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.85 (9.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003econtrols\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.79 (11.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eN, number; SD, standard deviation; PPMI, Parkinson\u0026rsquo;s Progression Markers Initiative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Enzyme Activity\u003c/h2\u003e \u003cp\u003eEnzymatic activity for GCase, acid alpha-glucosidase (GAA), acid sphingomyelinase (ASM), alpha-galactosidase A (GLA), and galactosylceramidase (GALC) was measured from dried blood spots in the Columbia cohort, the protocol for which has been previously described [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To summarize, enzyme activity was measured by Sanofi laboratories using liquid chromatography-tandem mass spectrometry (LC-MS/MS) from dried blood spots, using a multiplex assay. The dried blood spots were incubated in a reaction cocktail with substrates for the enzymes and buffer. Enzyme activity measurements were determined by calculating the amount of product obtained after the incubation, under the assumption that this is a direct indication of the activity of the enzymes. Lysosomal enzyme activity for the PPMI cohort was measured from frozen whole blood, which was slowly thawed and the processed similarly to the Columbia cohort. GCase outliers were identified as those with activity measurements lying outside +/- 1.5 times the inter quartile range (IQR), and were subsequently removed in both cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Genome-Wide Association Study\u003c/h2\u003e \u003cp\u003e Genotyping was performed with the OmniExpress GWAS array for the Columbia cohort and the NeuroX array for the PPMI cohort, according to manufacturer\u0026rsquo;s protocols (Illumina Inc.). Quality control for individual and variant data was completed as previously described (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/neurogenetics/GWAS-pipeline\u003c/span\u003e\u003cspan address=\"https://github.com/neurogenetics/GWAS-pipeline\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e In brief, samples that were heterozygosity outliers (inclusion criteria of -0.15\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;F \u0026lt;= 0.15), call rate outliers (missingness\u0026thinsp;\u0026gt;\u0026thinsp;95%), had mismatched genetic and reported sex, or were identified as European ancestry outliers based on HapMap3 principal component analysis (PCA) in plink v1.9 were removed [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, we removed samples with relatedness closer than 3rd degree relatives (pihat\u0026thinsp;\u0026gt;\u0026thinsp;0.125). Individual SNPs were excluded on the basis of variant missingness (\u0026gt;\u0026thinsp;95%), differences in missingness between cases and controls (p\u0026thinsp;\u0026lt;\u0026thinsp;1e-04), haplotype missingness (p\u0026thinsp;\u0026lt;\u0026thinsp;1e-04), and deviation from Hardy-Weinberg equilibrium in controls (p\u0026thinsp;\u0026lt;\u0026thinsp;1e-04). Imputation was performed on filtered data with the Michigan Imputation Server using the Haplotype Reference Consortium reference panel r1.1 2016 and default settings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Linear regressions of GCase activity were performed in plink v1.9 using hard-call variants (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8) and a minor allele frequency (MAF) threshold of \u0026gt;\u0026thinsp;0.01. We manually added the PD-relevant variant \u003cem\u003eGBA1\u003c/em\u003e p.N370S to the Columbia analyses, as well as \u003cem\u003eGBA1\u003c/em\u003e p.T369M and \u003cem\u003eLRRK2\u003c/em\u003e p.G2019S after filtering, due to all being under the minor allele threshold. The Columbia cohort included adjustments for age, sex, disease status, Ashkenazi Jewish ancestry, \u003cem\u003eLRRK2\u003c/em\u003e p.G2019S status, and lysosomal enzyme activities (GAA, GLA, GALC, and ASM) to examine the isolated effect on GCase activity. The p.G2019S mutation was included as a covariate due it being a known GCase modifier [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lysosomal enzymes were adjusted for because they have been shown to correlate with one another in a previous study [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The PPMI analysis included the same adjustments, with the addition of white blood cell count (WBC) as previously suggested [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Fixed-effect meta-analyses of PPMI and Columbia cohorts were performed with METAL in plink v1.9. Principal components (PCs) were calculated using PCA with plink v1.9 and the top 10 PCs were included as covariates for each cohort. Conditional and joint analyses were performed with GTCA-COJO to identify independent variants after adjusting for lead SNPs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. ANOVA, linear regressions with interaction term, and interaction plots were created in R v4.3.1 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Linkage disequilibrium Manhattan plots were created using LocusZoom [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Statement\u003c/h2\u003e \u003cp\u003eAll code for analyses used in this project can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/gan-orlab/GCase_GWAS\u003c/span\u003e\u003cspan address=\"https://github.com/gan-orlab/GCase_GWAS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PPMI data used for this study can be obtained by qualified researchers upon completion of a data access application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ppmi-info.org/access-data-specimens/download-data\u003c/span\u003e\u003cspan address=\"https://www.ppmi-info.org/access-data-specimens/download-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Columbia cohort data can be obtained by request. The GCase GWAS summary statistics can be found on the GWAS catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 \u003cem\u003eGAA\u003c/em\u003e and \u003cem\u003eGBA1\u003c/em\u003e loci are associated with GCase activity.\u003c/h2\u003e \u003cp\u003eAfter quality control and imputation, a total of 556 cases, 284 controls and 13327381 variants in the Columbia cohort and 328 cases, 144 controls and 941882 variants in the PPMI cohort were available for analysis. Mean GCase activity was similar between Columbia (mean\u0026thinsp;=\u0026thinsp;11.1 umol/l/h, sd = +/- 3.14) and PPMI (mean\u0026thinsp;=\u0026thinsp;11.63 umol/l/h, sd = +/- 2.59).\u003c/p\u003e \u003cp\u003eWe performed a GWAS to identify potential associations between common variants and GCase activity in both cohorts separately, followed by a meta-analysis. We looked at the genomic inflation factors and QQ plots of each analysis to test for systematic bias, and found them to be acceptable (Columbia λ\u0026thinsp;=\u0026thinsp;0.99, PPMI λ\u0026thinsp;=\u0026thinsp;1.04, Supplementary Fig.\u0026nbsp;1). The strongest associated locus in Columbia was in the \u003cem\u003eGBA1\u003c/em\u003e locus, as expected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). This signal is driven by p.N370S (rs76763715, beta = -4.21, se\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;4.55e-31), which is in linkage disequilibrium (LD) with the lead SNP in the locus (rs745550122, beta = -4.46, se\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;6.61e-34, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.78, D` = 0.91). A second signal can be seen in chromosome 17 in the \u003cem\u003eGAA\u003c/em\u003e locus, with the strongest associated variants comprising a high-LD region (Supplementary Fig.\u0026nbsp;2) and the lead SNP having a negative direction of effect (beta = -0.96, se\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;7.55e-9). Applying conditional and joint analyses revealed no secondary independent associations in either signal. The \u003cem\u003eGBA1\u003c/em\u003e signal was replicated in the PPMI cohort, with p.N370S once again driving the association (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, beta = -5.05, se\u0026thinsp;=\u0026thinsp;0.76, p\u0026thinsp;=\u0026thinsp;1.25e-10). The \u003cem\u003eGAA\u003c/em\u003e locus does surpass nominal significance in PPMI, although the lead SNP has an opposite direction of effect compared to the lead SNP from Columbia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The meta-analysis likewise showed associations in the \u003cem\u003eGBA1\u003c/em\u003e and \u003cem\u003eGAA\u003c/em\u003e loci, but did not result in any additional associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The \u003cem\u003eGBA1\u003c/em\u003e variants p.T369M (rs75548401) and p.E326K (rs2230288) were also associated with GCase activity, although not at GWAS significance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Both variants had similar effect sizes, directions, and p-values across both analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of lead \u003cem\u003eGAA\u003c/em\u003e locus variants in Columbia and PPMI cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e17:78061141:T:G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e17:78096483:A:C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.44e-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSNP, single nucleotide polymorphism; se, standard error; PPMI, Parkinson\u0026rsquo;s Progression Markers Initiative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eGBA1\u003c/em\u003e p.T369M and p.E326K association statistics for Columbia and PPMI cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ep.T369M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.79e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeta-analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.787e-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ep.E326K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeta-analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.027e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ese, standard error; PPMI, Parkinson\u0026rsquo;s Progression Markers Initiative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInterestingly, analysis in the Columbia cohort without adjusting for acid alpha-glucosidase activity eliminates any association from the \u003cem\u003eGAA\u003c/em\u003e locus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Adding adjustments for common PD-relevant \u003cem\u003eGBA1\u003c/em\u003e variants p.N370S, p.E326K and p.T369M results in both the \u003cem\u003eGBA1\u003c/em\u003e and \u003cem\u003eGAA\u003c/em\u003e peaks dropping below GWAS-level significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). To better understand if these outcomes could be due to an interaction between the acid alpha-glucosidase and GCase enzymes, we constructed an interaction plot of enzyme activities by genotype of the top \u003cem\u003eGAA\u003c/em\u003e SNP that was common in both cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The genotype of the lead SNP in the \u003cem\u003eGAA\u003c/em\u003e locus (17:78061141:T:G) appears to impact the interaction of GCase and acid alpha-glucosidase enzyme activities in Columbia, with correlations becoming progressively weaker in an additive manner with the addition of the T allele (Fig.\u0026nbsp;3). This trend is not as clear in controls, which could be due to the lower number of samples in this group. The effect is also not apparent in PPMI, reflective of the lack of association in this cohort. To further investigate the presence of an interaction, both an ANOVA and a linear regression using an interaction term were performed. The ANOVA used the absolute difference between GCase and acid alpha-glucosidase activity, which did significantly vary by the genotype status of the lead \u003cem\u003eGAA\u003c/em\u003e variant (df\u0026thinsp;=\u0026thinsp;2, Sum Sq\u0026thinsp;=\u0026thinsp;130, Mean Sq\u0026thinsp;=\u0026thinsp;64.75, F\u0026thinsp;=\u0026thinsp;13.62, p\u0026thinsp;=\u0026thinsp;1.52e-06). The linear regression was performed using the same covariates as the previous analysis, with the addition of a \u003cem\u003eGAA\u003c/em\u003e*GCase interaction term. The results indicated that the T/T genotype status was significant (beta = -0.25, se\u0026thinsp;=\u0026thinsp;0.13, p\u0026thinsp;=\u0026thinsp;0.048), supporting the presence of an interaction effect between \u003cem\u003eGAA\u003c/em\u003e genotype status, acid alpha-glucosidase activity, and GCase activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo examine if the \u003cem\u003eGAA\u003c/em\u003e locus has any role in PD, we investigated the association of this region with PD risk and various measurements of progression using GWAS summary statistics from previous studies. We identified 26, 4, 5, 79, and 6 variants that were nominally associated in the GWASs of PD risk, age at onset, UPDRS III, MoCA, and MMSE scores, respectively (Supplementary Table\u0026nbsp;1), although no variants passed Bonferroni correction for multiple comparisons [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. There were no \u003cem\u003eGAA\u003c/em\u003e locus variants even nominally associated in the GWASs of motor, cognitive, and composite progression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Of the associated variants, a total of 8, 2, 33, and 1 variants in the GWASs of PD risk, age at onset, MoCA, and MMSE scores were also nominally associated with GCase activity in the present meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 PD-related variants are associated with GCase activity.\u003c/h2\u003e \u003cp\u003eWe investigated PD-related variants nominated from the largest PD GWAS to date to determine if any were associated with GCase activity in our cohorts (Nalls et al., 2019). There were 17, 7 and 14 loci below nominal significance in the main GWAS for Columbia, PPMI and the meta-analysis, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Of these, only p.N370S (rs76763715) and rs35749011 (in strong LD with p.E326K) in the \u003cem\u003eGBA1\u003c/em\u003e locus, as well as rs13117519 in the \u003cem\u003eANK2\u003c/em\u003e locus passed multiple testing correction in the meta-analysis. We then attempted to replicate previously reported associations in \u003cem\u003eTMEM175\u003c/em\u003e and \u003cem\u003eLRRK2\u003c/em\u003e with GCase activity, by investigating these loci in a simplified meta-analysis using only age, sex, disease status, 10 PCs, and PD-relevant \u003cem\u003eGBA1\u003c/em\u003e SNPs including p.N370S, p.E326K, and p.T369M as covariates (Supplementary Fig.\u0026nbsp;3). There were 3 intronic variants in \u003cem\u003eLRRK2\u003c/em\u003e that passed nominal significance (rs116911375, beta = -1.7, se\u0026thinsp;=\u0026thinsp;0.79, p\u0026thinsp;=\u0026thinsp;0.03; rs191242488, beta = -1.041, se\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;=\u0026thinsp;0.044; 12:40668909:T:G, beta = -1.041, se\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;=\u0026thinsp;0.044), and no variants in \u003cem\u003eTMEM175\u003c/em\u003e. No variants passed multiple testing correction. The nominally significant variants do not appear to be in LD with p.G2019S or p.M1646T. Although these variants are not associated with GCase activity in our analysis, p.G2019S and p.M1646T do show a consistent positive direction of effect with what has been demonstrated in previous research (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The difference seen in our results compared to the nominal association of p.M1646T found in a very similar analysis in these cohorts performed by Sosero et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] is due to the removal of GCase outliers in our study. With the inclusion of outliers, we also see an association of this variant in our meta-analysis (b\u0026thinsp;=\u0026thinsp;1.16, se\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.002, rs35303786).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease risk SNPs associated with changes in GCase activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ersID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003e\u003cb\u003eColumbia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:155205634:T:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers76763715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.55E-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGBA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20:6006041:C:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers77351827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCRLS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:112243291:A:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers997368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eFYN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:114369065:C:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers13117519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eANK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90666041:C:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers356203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90626111:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers356182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:155135036:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers114138760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGBA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21:38852361:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2248244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eDYRK1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90636630:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers5019538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17:43933307:T:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers7221167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMAPT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14:37989270:T:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers12147950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMIPOL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90607126:C:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers356228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18:31304318:G:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1941685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eASXL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3:28705690:T:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers6808178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLINC00693\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13:97865021:T:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4771268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eMBNL2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14:95194760:G:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4905237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSERPINA13P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003ePPMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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colname=\"c3\"\u003e \u003cp\u003ers979812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGALC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:32578772:C:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers504594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.05E-43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGBA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:155135036:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers35749011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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colname=\"c3\"\u003e \u003cp\u003ers77351827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCRLS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90666041:C:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers356203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e 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\u003cp\u003e14:88464264:G:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers979812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGALC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90626111:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers356182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:154898185:G:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers114138760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eGBA1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21:38852361:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers2248244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eDYRK1A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18:31304318:G:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers1941685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eASXL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4:90607126:C:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers356228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSNCA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2:135438789:G:A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4954162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTMEM163\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14:95194760:G:C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ers4905237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSERPINA13P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSNP, single nucleotide polymorphism; se, standard error; MAF, minor allele frequency; PPMI, Parkinson\u0026rsquo;s Progression Markers Initiative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLRRK2\u003c/em\u003e p.G2019S and p.M1646T association statistics for Columbia and PPMI cohorts.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ep.G2019S\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeta-analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ep.M1646T\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeta-analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ese, standard error; PPMI, Parkinson\u0026rsquo;s Progression Markers Initiative.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the present GWAS, we identified variants in the \u003cem\u003eGAA\u003c/em\u003e locus as potential modifiers of GCase activity, in addition to the known associations with \u003cem\u003eGBA1\u003c/em\u003e variants. \u003cem\u003eGAA\u003c/em\u003e encodes acid alpha-glucosidase, and deficiency in this enzyme leads to the LSD Pompe disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There is no known association between Pompe disease and PD, and the variants in the \u003cem\u003eGAA\u003c/em\u003e locus are not associated with PD risk, age at onset, or various measures of progression. This is an important observation, since it may suggest that changes in GCase activity alone are insufficient to cause PD, or that the effects of \u003cem\u003eGAA\u003c/em\u003e variants on GCase activity are too small to have a meaningful clinical effect. It was hypothesized that the mechanism underlying \u003cem\u003eGBA1\u003c/em\u003e-associated PD is an imbalance, or disturbance in the flux within the lysosomal glycosphingolipid metabolism pathway, rather than reduced GCase activity on its own [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This is supported by the identification of other genes in this pathway that are associated with PD, such as \u003cem\u003eSMPD1\u003c/em\u003e, \u003cem\u003eGALC\u003c/em\u003e, \u003cem\u003eARSA\u003c/em\u003e and \u003cem\u003eASAH1\u003c/em\u003e [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Previous work has also demonstrated that lysosomal enzymes are highly correlated, especially for GCase, acid alpha-glucosidase, and alpha galactosidase A which is encoded by \u003cem\u003eGLA\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Taken into consideration with the interaction effect observed in the present study, there seems to be mounting evidence in favor of this hypothesis. The mechanism behind the interaction of GCase and acid alpha-glucosidase is still unknown. The products of GCase are lipids and glucose, while acid alpha-glucosidase hydrolyzes glycogen into glucose [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our observation of genotype-dependent correlation between GCase and acid alpha-glucosidase activities may therefore indicate that the \u003cem\u003eGAA\u003c/em\u003e locus variants associated with GCase activity could maintain equilibrium in this pathway, as they affect both GCase and alpha-glucosidase activities. This hypothesis requires additional genetic, enzymatic, and functional studies.\u003c/p\u003e \u003cp\u003eWe further examined the association between known PD-associated loci and GCase activity. As to be expected, we found three PD variants in the \u003cem\u003eGBA1\u003c/em\u003e locus to be associated with GCase activity. We also found an additional association with the \u003cem\u003eANK2\u003c/em\u003e locus. Phosphorylation in the \u003cem\u003eANK2\u003c/em\u003e region has been proposed to play a role in PD neurodegeneration through the inhibition of organelle autophagy, but its connection to GCase activity is not clear from the current literature and will need further research [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. There are also multiple notable associations with ties to GCase activity that may have only failed multiple testing correction due to sample size. One such locus is \u003cem\u003eGALC\u003c/em\u003e, encoding the enzyme galactosylceramidase which, similarly to GCase, breaks down large sphingolipids into lipids and ceramide. The association of \u003cem\u003eGALC\u003c/em\u003e variants with GCase activity may suggest that a disturbance in lysosomal metabolism could be a causal mechanism in PD, which is also supported by previous research which found galactosylceramide and GCase to be correlated [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We also found nominal associations with multiple \u003cem\u003eSNCA\u003c/em\u003e variants, complimenting previous associations found between levels of alpha synuclein aggregates and Gcase activity [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A third interesting locus that fell just shy of the multiple testing correction threshold was \u003cem\u003eFYN\u003c/em\u003e. This gene encodes a kinase that has been shown to phosphorylate alpha-synuclein, which is a key step towards its aggregation into Lewy bodies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. We did not replicate previously reported associations with \u003cem\u003eLRRK2\u003c/em\u003e or \u003cem\u003eTMEM175\u003c/em\u003e variants, which is likely due to additional adjustments in our study and the removal of GCase outliers compared to previous studies. Studies in larger cohorts will be required to confirm the associations of these PD loci.\u003c/p\u003e \u003cp\u003eThis study has several limitations. The first limitation is a lack of power. Our discovery and replication analyses utilized the largest cohorts with enzyme activity measurements that we could find at the time of performing the study and allowed us to complete the first GWAS of GCase activity, but only enabled us to discover common variants with moderate effect sizes or rare variants with large effect sizes. Due to this, there are likely more modulators of GCase activity yet to be uncovered. A second limitation is the use of only individuals of European and Ashkenazi Jewish ancestry, as there was insufficient data to carry out the study in other populations. Next, our study had minor differences in age and notable differences in sex, particularly in the Columbia cohort, between cases and controls. Both age and sex were adjusted for in all analyses to account for these differences. An additional limitation is the method of enzyme measurement used. Lysosomal activity was measured using dried blood spots, which is not guaranteed to capture the true enzyme activity within a functioning lysosomal environment. These results will strengthen if replicated in future studies that use lysosomal-specific methods of enzyme activity, in cells more representative of disease pathogenesis.\u003c/p\u003e \u003cp\u003eIn conclusion, we found a novel potential association in the \u003cem\u003eGAA\u003c/em\u003e locus associated with GCase activity, which may represent an interaction effect between GCase and acid alpha-glucosidase and could be indicative of a homeostatic relationship between lysosomal enzymes. We also support previously suggested connections of multiple PD-related genes with GCase activity, such as \u003cem\u003eGALC\u003c/em\u003e and \u003cem\u003eSNCA\u003c/em\u003e, as well as identifying a novel potential association with \u003cem\u003eANK2\u003c/em\u003e. These findings could be significant for improving our understanding of how GCase deficiency is related to overall lysosomal dysfunction, and how GCase functions in relation to other lysosomal enzymes. Additionally, associated loci can be taken into account in future research and clinical trials to help control for genetic influences on GCase activity. Due to the novelty of our results and lack of confident association in our replication cohort, further genetic and functional research will be required to validate our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eWe would like to thank the research participants for contributing to this study. We thank Meron Teferra for her assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.1 Funding\u003c/p\u003e\n\u003cp\u003eThis study was financially supported through grants from the Michael J. Fox Foundation (MJFF, No. 020700), the Canadian Consortium on Neurodegeneration in Aging (CCNA, No. 049-14), and the Canada First Research Excellence Fund (CFREF, No. 247908), awarded to McGill University for the Healthy Brains for Healthy Lives initiative (HBHL). The Columbia cohort was funded by the National Institutes of Health (No. K02NS080915, and No. UL1 TR000040, formerly the National Center for Research Resources, Grant No. UL1 RR024156). PPMI \u0026ndash; a public-private partnership \u0026ndash; is funded by the Michael J. Fox Foundation for Parkinson\u0026rsquo;s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson\u0026apos;s, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Jazz Pharmaceuticals, Johnson \u0026amp; Johnson Innovative Medicine, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuron23, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager. The G-Can (GBA1-Canada) Initiative, an open-science collaborative initiative aimed at addressing GBA1 mutation based Parkinson\u0026apos;s disease, has made contributions to this research. G-Can is supported by The Hilary \u0026amp; Galen Weston Foundation, Silverstein Foundation, and J. Sebastian van Berkom and Ghislaine Saucier. Z.G.O. is supported by the Fonds de recherche du Qu\u0026eacute;bec\u0026mdash;Sant\u0026eacute; (FRQS) Chercheurs-boursiers award and is a William Dawson Scholar. K.S. is supported by a post-doctoral fellowship from the Canada First Research Excellence Fund (CFREF), awarded to McGill University for the Healthy Brains for Healthy Lives initiative (HBHL). E.N.S. gratefully acknowledges funding support for this research from Parkinson Canada.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.2 Competing Interests\u003c/p\u003e\n\u003cp\u003eZ.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 Guidepoint, Lighthouse and Deerfield.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.3 Author Contributions\u003c/p\u003e\n\u003cp\u003eE.N.S. conducted all analyses, interpreted data, developed the methodology, and wrote the manuscript. L.K. developed the methodology and contributed to data analysis and result interpretation. K.S. and E.Y. contributed to data analysis and result interpretation. J.A., F.A., J.A.R., D.S., S.F., C.W., and S.P.S. performed data collection and curation. R.N.A. developed the methodology and performed data collection and curation. Z.G.O. developed the research idea, developed the methodology, contributed to result interpretation, and supervised the study. All authors read, approved, and contributed to editing the final manuscript.\u003c/p\u003e\n\u003cp\u003e5.4 Data Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData used in the preparation of this article were obtained from the Parkinson\u0026rsquo;s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data). For up-to-date information on the study, visit www.ppmi-info.org. Columbia cohort data can be obtained by request. The GCase GWAS summary statistics can be found on the GWAS catalog (https://www.ebi.ac.uk/gwas/). All code for analyses used in this project can be found at https://github.com/gan-orlab/GCase_GWAS. PPMI data used for this study can be obtained by qualified researchers upon completion of a data access application (https://www.ppmi-info.org/access-data-specimens/download-data).\u003c/p\u003e\n\u003cp\u003e5.5 Ethics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eInformed consent forms were signed by all participants prior to entering their respective studies and the study protocol was approved by the institutional review boards.\u003c/p\u003e\n\u003cp\u003e5.6 Consent to Publish\u003c/p\u003e\n\u003cp\u003eAll authors have read and consented to publish this manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArmstrong MJ, Okun MS (2020) Diagnosis and Treatment of Parkinson Disease: A Review. 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Biochem Biophys Res Commun 280(4):1085\u0026ndash;1092. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1006/bbrc.2000.4253\u003c/span\u003e\u003cspan address=\"10.1006/bbrc.2000.4253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genome-wide association study, glucocerebrosidase, Parkinson’s disease, GBA1, lysosomal metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4425669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4425669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOne of the most common genetic risk factors for Parkinson\u0026rsquo;s disease (PD) are variants in \u003cem\u003eGBA1\u003c/em\u003e, which encodes the lysosomal enzyme glucocerebrosidase (GCase). GCase deficiency has been associated with an increased PD risk, but not all individuals with low GCase activity are carriers of \u003cem\u003eGBA1\u003c/em\u003e mutations, suggesting other factors may be acting as modifiers. We aimed to discover common variants associated with GCase activity, as well as replicate previously reported associations, by performing a genome-wide association study using two independent cohorts: a Columbia University cohort consisting of 697 PD cases and 347 controls and the Parkinson\u0026rsquo;s Progression Markers Initiative (PPMI) cohort consisting of 357 PD cases and 163 controls. As expected, \u003cem\u003eGBA1\u003c/em\u003e variants have the strongest association with decreased activity, led by p.N370S (beta = -4.36, se\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;5.05e-43). We also identify a novel association in the \u003cem\u003eGAA\u003c/em\u003e locus (encoding for acid alpha-glucosidase, beta = -0.96, se\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;5.23e-09) that may be the result of an interaction between GCase and acid alpha-glucosidase based on various interaction analyses. Lastly, we show that several PD-risk loci are potentially associated with GCase activity. Further research will be needed to replicate and validate our findings and to uncover the functional connection between acid alpha-glucosidase and GCase.\u003c/p\u003e","manuscriptTitle":"Genome-wide association study of glucocerebrosidase activity modifiers.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 20:43:31","doi":"10.21203/rs.3.rs-4425669/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-09T14:36:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T04:00:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-04T05:59:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2655003261501078676967062115032026191","date":"2024-08-28T02:20:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174382228732404922339719413429075909671","date":"2024-08-25T23:16:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-11T10:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117514662051417066666105469903617919338","date":"2024-06-02T13:24:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312489877019621783494124389555374736398","date":"2024-05-31T15:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213003652626546239434340110083396055928","date":"2024-05-30T13:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-30T11:37:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T13:05:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T13:05:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2024-05-15T14:02:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e131eb5a-b84e-48ff-b8d5-222571ba5300","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T16:00:15+00:00","versionOfRecord":{"articleIdentity":"rs-4425669","link":"https://doi.org/10.1007/s12035-025-04996-1","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2025-04-29 15:57:22","publishedOnDateReadable":"April 29th, 2025"},"versionCreatedAt":"2024-06-07 20:43:31","video":"","vorDoi":"10.1007/s12035-025-04996-1","vorDoiUrl":"https://doi.org/10.1007/s12035-025-04996-1","workflowStages":[]},"version":"v1","identity":"rs-4425669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4425669","identity":"rs-4425669","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}