Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease

Nature Genetics | Volume 56 | January 2024 | 27–36 27 nature genetics https://doi.org/10.1038/s41588-023-01584-8Article Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease Jonggeol Jeffrey Kim 1,2,167 , Dan Vitale1,3,4,167, Diego Véliz Otani5,6,167, Michelle Mulan Lian7,8,167, Karl Heilbron9, the 23andMe Research Team*, Hirotaka Iwaki1,3,4, Julie Lake1, Caroline Warly Solsberg 10,11,12, Hampton Leonard1,3,4, Mary B. Makarious 1,13,14, Eng-King Tan 15, Andrew B. Singleton1,4, Sara Bandres-Ciga1,4, Alastair J. Noyce2, the Global Parkinson’s Genetics Program (GP2)*, Cornelis Blauwendraat 1,4,168 , Mike A. Nalls 1,3,4,168 , Jia Nee Foo7,8,168 & Ignacio Mata16,168 Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations. Parkinson’s disease (PD) is a neurodegenerative disease pathologically defined by Lewy body inclusions in the brain and the death of dopaminergic neurons in the midbrain. The identification of genetic risk factors is imperative for mitigating the global burden of PD, one of the fastest growing age-related neurodegenerative diseases. A large PD genome-wide association study (GWAS) meta-analysis uncovered 90 independent genetic risk variants in individuals of European ancestry1. Similarly, large-scale PD GWAS meta-analyses of East Asian2 and a single GWAS of Latin American 3 individuals have each identified two risk loci that were not previously identified in Europeans. For PD, there Received: 26 August 2022 Accepted: 20 October 2023 Published online: 28 December 2023 Check for updates 1Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. 2Preventive Neurology Unit, Centre for Prevention Diagnosis and Detection, Wolfson Institute of Population Health, Queen Mary University of London, London, UK. 3Data Tecnica International, Washington, DC, USA. 4Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA. 5Neurogenetics Research Center, Instituto Nacional de Ciencias Neurológicas, Lima, Peru. 6Institute for Genome Sciences, University of Maryland, Baltimore, MD, USA. 7Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore. 8Genome Institute of Singapore, Agency for Science, Technology and Research, A*STAR, Singapore, Singapore. 923andMe, Inc., Sunnyvale, CA, USA. 10Pharmaceutical Sciences and Pharmacogenomics, UCSF, San Francisco, CA, USA. 11Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA. 12Memory and Aging Center, UCSF, San Francisco, CA, USA. 13Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. 14UCL Movement Disorders Centre, University College London, London, UK. 15Department of Neurology, National Neuroscience Institute, Duke NUS Medical School, Singapore, Singapore. 16Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA. 167These authors contributed equally: Jonggeol Jeffrey Kim, Dan Vitale, Diego Veliz-Otani, Michelle Mulan Lian. 168These authors jointly supervised this work: Cornelis Blauwendraat, Mike A. Nalls, Jia Nee Foo, Ignacio Mata. *A list of authors and their affiliations appears at the end of the paper. e-mail: [email protected]; [email protected]; [email protected]; jianee.f[email protected].sg; [email protected] Nature Genetics | Volume 56 | January 2024 | 27–36 28 Article https://doi.org/10.1038/s41588-023-01584-8 PESCA v0.3 (ref. 10) was run for the main European and East Asian meta-analyses and all loci identified in the main analysis were explored (Supplementary Table 6). PESCA uses ancestry-matched LD estimates to infer whether the causal variants are population-specific or shared between two populations. Variants identified as shared between the populations may be more likely to be causal. In addition, we expect higher posterior probability (PP) for shared causal variants in the loci identified by MAMA, even if they have not previously been identified in the single-ancestry study. The lead SNP in the RIMS1 locus (rs12528068) had a high PP for being a shared causal variant (PP = 0.972) despite being significant in the European study1 but not in the East Asian study2. We also observed that the novel lead variants for MTF2 (rs35940311), PIK3CA (rs11918587), EP300 (rs4820434) and PPP6R2 (rs60708277) had higher PP estimates for being shared causal variants across both populations (PPshared = 0.757, 0.214, 0.769, 0.946) than for being causal variants in a single population (PP EUR <0.080, PPEAS < 0.001). However, it is important to note that the sample size discrepancy between the European and East Asian data impacts our power to detect population-specific causal variants at any of these loci. We found 17 suggestive loci that failed to meet our stringent significance threshold but had P < 5 × 10 −8 in a fixed-effects meta-analysis and P < 1 × 10−6 in the random-effects meta-analysis (Supplementary Table 4). Fourteen of these regions were novel loci. Two loci near JAK1 and HS1BP3 were exclusively found in the 23andMe Latin American and African cohorts. The lead SNPs (rs578139575 and rs73919910) for these loci are non-coding and very rare in European populations but are more common in Africans and Latin Americans (gnomAD v3.1.2 minor allele frequencies in EUR: 0.02%, 0.23%; AFR: 1.64%, 8.84%; AMR: 0.41%, 1.91%). If confirmed, these loci would confer a strong effect on PD risk (beta: −1.3, −0.54). These loci merit further studies in the African and Latin American populations. Fine-mapping identifies six credible sets with single variants Fine-mapping was also performed using MR-MEGA, which uses ancestry heterogeneity to increase fine-mapping resolution. We identified 23 loci that had fewer than 5 variants within the 95% credible set. Of these, MR-MEGA nominated a single putative causal variant with >95% PP in 6 loci: TMEM163, TMEM175, SNCA, CAMK2D, HIP1R and LSM7 (Table 3 and Supplementary Tables 7 and 8). Our results affirmed previous results showing the TMEM175 p.M393T coding variant as the likely causal variant 11 . The putative variants HIP1R have strong evidence for regulome binding (RegulomeDB rank ≤ 2). In particular the HIP1R variant rs10847864 is located in a transcription start site that is active in substantia nigra tissue (chromatin state windows: chr12:123326200.123327200) and astrocytes in the spinal cord and the brain (chromatin state windows: chr12:123326400.123326600). Outside of the credible sets containing a single variant, we identified missense variants in two genes: FCGR2A (p.H167R, PP = 0.145) and SLC18B1 (p.S30P, PP = 0.780). are now large-scale efforts to sequence and analyze genomic data in underrepresented populations with the goal of both identifying novel associated loci, fine-mapping known loci and addressing the inequality that exists in current precision medicine efforts 4,5 . Here we performed a large-scale multi-ancestry meta-analysis (MAMA) of PD GWASs by including individuals from four ancestral populations: European, East Asian, Latin American and African. This effort can serve as a guide for future genetic analyses to increase ancestral representation. Meta-analyses identify 66 known and 12 novel loci In addition to results from previously described European 1 , East Asian 2 and Latin American3 studies, we also used FinnGen and additional datasets for East Asian, Latin American and African cohorts from 23andMe, Inc (Table 1, Fig. 1 and Supplementary Table 1). In total, we included 49,049 PD cases, 18,618 proxy cases (first-degree relative with PD) and 2,458,063 neurologically-healthy controls. Genetic covariance intercepts from linkage disequilibrium (LD) score regression6 within ancestries were close to zero or near the 95% confidence interval, implying that there is no sample overlap between the cohorts (Supplementary Table 1). After the data were harmonized and mapped to genome build hg19, MAMAs were conducted using a random-effects model and meta-regression of multi-ethnic genetic association (MR-MEGA)7. The random-effects model had greater power to detect homogenous allelic effects 7 . MR-MEGA uses axes of genetic variation as covariates in its meta-regression analysis and had greater power to detect heterogeneous effects across the different cohorts. MR-MEGA also distinguishes ancestral heterogeneity (differences in effect estimates due to ancestry-level genetic variation) from residual heterogeneity using axes of genetic variation generated from the allele frequencies across the different cohorts. Combining results from the random-effects model and MR-MEGA, we found 12 novel PD risk loci and 66 hits in known risk loci from single-ancestry GWAS (Table 2, Fig. 2 and Supplementary Tables 2–5) that met the Bonferroni-corrected alpha of 5 × 10 −9 , a more stringent threshold chosen to account for the larger number of haplotypes resulting from the ancestrally diverse datasets 8 . Of the 78 risk loci identified, 69 were significant in the random-effects model, whereas 3 were only significant in MR-MEGA. Eight of the novel loci found by the random-effect method showed homogeneous effects across the four different ancestries. An additional novel locus (FASN) identified by the random-effect method showed homogeneous effects in all available populations, but note that this variant failed quality control in both East Asian datasets. The other three loci, identified exclusively in MR-MEGA, showed ancestrally heterogeneous effects. All three loci (IRS2, MYLK2 and USP25) showed evidence of significant ancestral heterogeneity (P ANC-HET < 0.05) but no significant residual heterogeneity (PRES-HET > 0.148), supporting the idea that the signals are due to population structural differences rather than other confounding factors (Fig. 3). For the IRS2 locus (lead SNP rs1078514, P ANC-HET = 5.3 × 10−3) the Finnish cohort has an opposite effect direction compared to the meta-analysis effect estimate (Supplementary Fig. 4). Similarly, the MYLK2 locus has the African effect estimate most different from the meta-analysis effect estimate (lead SNP rs6060983, P ANC-HET = 0.035), suggesting different effects between populations. Although this is a novel single-trait GWAS locus, its lead SNP was previously discovered as a potential pleiotropic locus in a multi-trait conditional/conjunctional false discovery rate (FDR) study between schizophrenia and PD 9 . Lastly, the USP25 locus had the most significant ancestral heterogeneity (lead SNP rs1736020, P ANC-HET = 4.74 × 10 −5 ) and its effects were specific to European and African cohorts, albeit in different directions. When looking at the nearest protein coding gene to each novel lead SNP and their probability of being loss-of-function intolerant (pLI) score, we found that 7 out of 12 genes had a pLI score of 0.99 or 1. Genes with low pLI scores were found both in loci with (MYLK2) and without (SYBU, PIGL and PPP6R2) significant ancestry heterogeneity. Table 1 | Cohort descriptions Study Ancestral population Cases/proxy/controls Nalls et al.1European (EUR) 37,688/18,618/1,411,006 Foo et al.2East Asian (EAS) 6,724/0/24,851 LARGE-PD 3 Latin American (AMR) 807/0/690 FinnGen Release 4 European-Finnish (EUR) 1,587/0/94,096 23andMe—African African (AFR) 288/0/193,985 23andMe—East Asian East Asian (EAS) 322/0/151,905 23andMe—Latino Latin American (AMR) 1,633/0/581,530 MAMA 49,049/18,618/2,458,063 Nature Genetics | Volume 56 | January 2024 | 27–36 29 Article https://doi.org/10.1038/s41588-023-01584-8 Gene set analysis finds enrichment in brain tissues We used the Functional Mapping and Annotation (FUMA) software 12,13 to functionally annotate the random-effect results. We generated a custom 1000 Genome reference panel that reflected the ancestry proportions of our dataset and ran multi-marker analysis of genomic annotation (MAGMA)14 for gene ontology, tissue level and single-cell expression data. We tested 16,992 gene ontology sets in MSigDB v7.0 (ref. 15) and used conditional analysis to discard redundant terms or identify gene sets that must be interpreted together. We found that 40 gene sets were significantly enriched with conditional analysis identifying 13 gene sets that share their signals with at least one other gene set (Supplementary Table 9). This is a substantial increase from previous 10 gene sets in the European meta-analysis performed by Nalls and colleagues 1 . Only two gene ontology terms that were significant in the Nalls et al. meta-analysis were also significant in the multi-ancestry results after multiple test correction: ‘curated geneset: Ikeda MIR30 Targets Up’ (P FDR = 0.018) and ‘cellular component: vacuolar membrane’ (PFDR = 0.047). In addition, ontology terms in immune system pathways (microglial cell proliferation, macrophage proliferation, natural killer T cell differentiation: PFDR < 0.04), mitochondria (response to mitochondrial depolarization: PFDR = 0.028), vesicles (vesicle uncoating, Gene ontology enrichment Colocalization with eQTL 0 2 4 6 8 10 124.2124.1 124.3 124.4 124.5 0.8 0.2 0.4 0.6 r2 Fine-mapping Downstream analyses Goal: Interpret the meta-analysis results and identify potential targets and biological mechanisms rs11650438 0 100 200 0 2 4 6 8 Brain mmQTL –log10(P) Brain mmQTL –log10(P) PD MAMA –log10(P) PD MAMA –log10(P) rs11650438 0 2 4 6 8 0 100 200 15.9 16.0 16.1 16.2 chr17 (Mb) CENPV 0 5 10 15 –log10(P) 5 10 15 European 39,275 cases 18,618 proxy cases 1.5M controls East Asian 7,046 cases 176,756 controls Latino 2,440 cases 582,220 controls African 288 cases 193,985 controls Study participants Goal: Collate the largest and most diverse set of participants in Parkinson’s disease genomics Multiancestry genome-wide meta-analysis Goal: Identify common SNPs that are associated with Parkinson’s disease risk that are applicable across different ancestries MR-MEGA (heterogeneous) Random effect (homogeneous) ++++ +–+– Fig. 1 | MAMA study design. Top panel: four ancestry groups used in the metaanalysis. Middle panel: MAMA and the two methods used. Random-effect (top) is better suited for risk variants with homogeneous effect direction across different ancestries, whereas MR-MEGA (bottom) can identify risk variants with heterogeneous effects due to population stratification introduced by ancestry differences. The densely dashed lines indicate Bonferroni adjusted suggestive threshold of two-sided P < 1 × 10−6, and the loosely dashed lines indicate Bonferroni adjusted significant threshold of two-sided P < 5 × 10−9. Bottom panel: downstream analyses and their examples. Created with Biorender.com. Nature Genetics | Volume 56 | January 2024 | 27–36 30 Article https://doi.org/10.1038/s41588-023-01584-8 phagolysosome assembly, regulation of autophagosome maturation: P FDR < 0.03) and tau protein (tau protein kinase activity: P FDR = 0.034) were significant. At the tissue level, the genes of interest were enriched in all brain cell types, as well as pituitary tissue (Supplementary Fig. 9), consistent with the results from Nalls et al.1. When analyzing single-cell RNA-sequencing data, there was no expression enrichment across 88 brain cell types in mouse brain data when cross-referenced with DropViz16 (Supplementary Fig. 10). There was also no enrichment of any specific cell types in the substantia nigra tissue in DropViz (Supplementary Fig. 10). However, in human midbrain data17, dopaminergic (DA1) and GABAergic (GABA) neurons were enriched (Supplementary Fig. 10). eQTLs and SMR nominate 25 putative genes near novel loci We also searched the GTEx v8 (ref. 18) brain tissue eQTLs and multi-ancestry eQTL meta-analysis of the brain 19 to correlate novel loci with gene expression data (Supplementary Tables 10 and 11). To correlate potential putative genes with PD risk, we searched the significant-eQTL genes and genes near the loci with previously completed summary-based Mendelian randomization (SMR)20 results in European-only data. When comparing the SNPs in novel loci with multi-ancestry brain eQTLs19, 28 genes were significant (Supplementary Fig. 8 and Supplementary Tables 10 and 11). SMR found 25 genes in four novel loci associated with PD risk (Table 2 and Supplementary Table 12). Interestingly, PPP6R2 and CENPV expression changes in substantia nigra were associated with PD risk. PPP6R2 encodes protein phosphatase 6 regulatory subunit 2, a regulatory protein for protein phosphatase 6 catalytic subunit (PPP6C), which is involved in the vesicle-mediated transport pathway. Centromere protein V (CENPV) is involved in centromere formation and cell division. Discussion This study is a large-scale GWAS meta-analysis of PD that incorporates multiple diverse ancestry populations. From the joint cohort analysis, we identified 66 independent risk loci near previously known PD risk regions and 12 potentially novel risk loci. Of the putative novel loci, nine had homogeneous effects and three had heterogeneous effects across the different cohorts. We found 17 additional suggestive loci using fixedeffects meta-analysis threshold at P < 5 × 10 −8 and random-effects metaanalysis threshold at P < 1 × 10−6. We fine-mapped 23 loci by leveraging the diverse ancestry populations. We highlighted tissues and cell types associated with PD risk, which were consistent with previous findings 1 . Finally we used SMR to nominate 25 putative genes near our novel loci. Novel loci contained genes in pathways previously implicated in PD. The MTF2 and PPP6R2 loci contain the genes TMED5 and PPP6R2. Protein TMED5 localizes to Golgi body21 and PPP6C, regulated by PPP6R2, is part of the vesicular transport pathways (https://reactome. org/content/detail/R-HSA-199977)22, both of which are implicated in PD pathogenesis23–28. eQTL and SMR analysis showed association between expression changes for PPP6R2 and CENPV in substantia nigra and PD risk. Because substantia nigra deterioration is a hallmark pathogenic feature of PD, PPP6R2 and CENPV merit additional investigation. Within a known locus, a new independent signal was found in RILPL2 (rs28659953). Protein RILPL2 interacts with LRRK2-phosphorylated Rab10 to block primary cilia generation 29 . Genes JAK1 and HS1BP3 are in two suggestive loci that were found only in Latin American and African populations. JAK1 is one of the proteins in the Janus kinase family, which is a critical part of the JAK-STAT pathway and is implicated in cytokine and inflammatory signaling30. JAK1 variants have been implicated in autoimmune diseases such as juvenile idiopathic arthritis and multiple sclerosis31. HS1BP3, also known as essential tremor 2 (ETM2), has been implicated in essential tremor32–34. Based on its sequence, ETM2 may modulate interleukin-2 signaling35. If these loci are confirmed, they would further support the growing appreciation for the role of Table 2 | Meta-analysis results of lead SNPs in the novel loci rsID Nearest coding gene SMR nominated putative genes CHR:BP:A1:A2 BETA(RE) SE P(RE) P(MR-MEGA) P(ANC-HET) P(RES-HET) gnomAD EUR AF gnomAD EAS AF gnomAD AMR AF gnomAD AFR AF pLI rs11164870 MTF2 CCDC18 1:93552187:C:G 0.054 0.009 1.15 × 10−10 2.64 × 10−9 0.229 0.928 39.0% 35.1% 45.2% 85.0% 1 rs6806917 PIK3CA KCNMB3 3:178861417:T:C −0.070 0.011 1.65 × 10−10 3.43 × 10−9 0.215 0.762 82.0% 89.9% 77.5% 57.8% 1 rs16843452 ADD1 ADD1, NOP14-AS1, NOP14 4:2849168:T:C −0.068 0.012 4.11 × 10−9 3.19 × 10−7 0.747 0.687 18.5% 47.4% 18.2% 8.9% 0.99 rs6469271 SYBU SYBU 8:110644774:T:C −0.056 0.010 3.62 × 10−9 2.04 × 10−7 0.590 0.954 77.5% 59.3% 74.7% 61.5% 0 rs1078514 IRS2 None 13:110463168:T:C 0.068 0.026 4.82 × 10−3 2.30 × 10−95.30 × 10−30.261 33.3% 39.2% 40.6% 10.7% 0.99 rs28648524 USP8 TRPM7 15:50787409:A:T 0.064 0.010 6.45 × 10−10 2.58 × 10−8 0.406 0.661 78.1% 53.7% 76.5% 79.8% 1 rs11650438 PIGL ADORA2B, ZSWIM7, PIGL, TTC19, NCOR1, CENPV, TRPV2 17:16234260:A:G 0.050 0.009 2.93 × 10−91.46 × 10−7 0.528 0.288 46.9% 17.8% 48.5% 64.0% 0 rs4485435 FASN None 17:80045086:C:G 0.082 0.014 2.61 × 10−9N/A N/A N/A 17.3% 12.1% 34.8% 30.3% 1 rs6060983 MYLK2 None 20:30420924:T:C 0.069 0.037 0.0322 3.86 × 10−90.035 0.149 69.3% 99.0% 71.8% 29.0% 0.23 rs1736020 USP25 None 21:16812552:A:C 0.006 0.005 0.885 1.12 × 10−94.74 × 10−50.638 43.0% 18.6% 38.6% 13.2% 0.75 rs73174657 EP300 ZC3H7B, POLR3H, CSDC2, PMM1, RANGAP1, MEI1, L3MBTL2, SLC25A17 22:41434158:A:G −0.059 0.010 3.81 × 10−94.90 × 10−7 0.983 0.655 27.2% 6.3% 47.5% 14.2% 1 rs10775809 PPP6R2 PPP6R2 22:50808017:A:T 0.092 0.015 4.09 × 10−10 5.61 × 10−8 0.943 0.903 10.1% 80.3% 80.1% 56.5% 0.16 MR-MEGA could not be run for the lead SNP of the FASN locus, as it was missing in more than three cohorts: Foo et al.2, 23andMe East Asian and 23andMe Latino. No P values were corrected for multiple tests. CHR, chromosome; BP, base pair; A1, effect allele; A2, other allele; BETA(RE), allelic effect in log odds ratio; SE, standard error; P(RE), two-sided P value of association from random effect; P(MR-MEGA): two-sided P value of association from MR-MEGA (chi-squared test with df = 4); P(ANC-HET), P value for the two-sided ancestral heterogeneity test (chi-squared test with df = 3); P(RES-HET): P value for the two-sided residual heterogeneity test (chi-squared test with df = 3); gnomAD [Ancestry] AF, A1 frequency reported for Europeans (EUR), East Asians (EAS), Amerindians (AMR) and Africans (AFR) by gnomAD v3.1.2; pLI, probability of being loss-of-function intolerant score from gnomAD v2.1.1 for the nearest coding gene (score was unavailable for gnomAD v3.1.2); SMR, summary-based Mendelian randomization; N/A, not available. Bolded are all significant P values (P < 5 ×10−9 for the two-sided association tests, P < 0.05 for the heterogeneity tests). Nature Genetics | Volume 56 | January 2024 | 27–36 31 Article https://doi.org/10.1038/s41588-023-01584-8 inflammation in PD36. All of the potentially novel PD loci identified in this analysis will require additional replication and functional validation to elucidate their role in PD pathogenesis. Previous findings in European populations found that polygenic risk scores explained 16–36% of PD heritability1. Although we did not perform similar tests incorporating our novel loci, they may explain additional heritable PD risk. We found that 26 of the 66 detected known PD loci had nominally significant ancestral heterogeneity (P ANC-HET < 0.05) and 10 remained significant after Bonferroni correction (PANC-HET < 0.05/62 MR-MEGA loci) (Fig. 3 and Supplementary Table 3). This heterogeneity may be caused by differences in effect sizes and allele frequencies between the different populations and thus should be studied as loci with potentially ancestrally divergent risk. 18 of the previous 92 known loci from single-ancestry GWASs did not overlap with any genome-wide significant loci in the multi-ancestry results at the significance threshold of 5 × 10−9 (Supplementary Table 13). However, our results do not necessarily invalidate these previous results. First, several of the cohorts have small sample sizes, which may increase the influence of sampling variation. Another reason may be due to the stringent genome-wide significance threshold of 5 × 10 −9 . Although this is a large PD GWAS meta-analysis, the more stringent significance threshold further raises the sample size needed to achieve equivalent statistical power. Of the 17 European loci identified, 3 were significant at the 5 × 10−8 threshold, and all 17 loci were at least nominally significant with the MR-MEGA method (P MR-MEGA < 5 × 10 −6 ). Lastly, variants may be more specific to the population in which they were first identified. 5 of the 17 variants had nominal ancestral heterogeneity (P ANC-HET < 0.05). It is worth noting that there are large differences in statistical power across ancestries. Additional population-specific loci will likely reach significance when larger sample sizes are available for non-European datasets. Our fine-mapping isolated several putative causal variants in previously discovered loci. TMEM175-rs34311866 has been previously identified as functionally relevant to PD risk37, which is consistent with our fine-mapping results. Fine-mapped variants in TMEM163, HIP1R and CAMK3D were also found to be parts of active or strong transcription sites in substantia nigra tissues. Among the fine-mapped variants were two missense variants in FCGR2A and SLC18B1, albeit with a lower PP than the 7 singular putative variants that we highlighted in Table 3. FCGR2A is present in multiple immune-related ontology gene sets, further highlighting the potential role of the immune system in PD pathology. However, the function of SLC18B1 is still unknown. Although the fine-mapping results provided by MR-MEGA are sufficient a b 0 1 2 MTF2 PIK3CA IRS2 MYLK2 USP25 MTF2 PIK3CA ADD1 SYBU USP8 PIGL FASN EP300 PPP6R2 3 54 6 7 98 Chromosome 1110 1312 15 16 17 18 19 20 21 2214 1 2 3 54 6 7 98 Chromosome 1110 1312 15 16 17 18 19 20 21 2214 5 10 15 20 25 –log10(P) 30 35 40 75 0 5 10 15 20 25 –log10(P) 30 35 40 123 90 Fig. 2 | Manhattan plots of the meta-analysis results across 2,525,730 participants. a, Random-effects model test. b, MR-MEGA meta-regression test (chi-squared test with df = 4). The x axis shows chromosome and base pair positions of each variant tested in the meta-analyses. The y axis shows the two-sided P value with no multiple-test correction in the −log10 scale. Orange horizontal dashed line indicates the Bonferroni-adjusted significant threshold of P < 5 × 10−9. Gray horizontal dashed line indicates the truncation line, where all − log10 P values greater than 40 were truncated to 40 for visual clarity. Novel loci are highlighted in red and annotated with the nearest protein coding gene. Nature Genetics | Volume 56 | January 2024 | 27–36 32 Article https://doi.org/10.1038/s41588-023-01584-8 to identify putative causal variants for loci driven by one independent signal, multiple variants in a locus can contribute to complex traits. The additive and epistatic effects of multiple causal variants in a locus can be difficult to interpret when the effects associated with each independent signal are small. The gene ontology analysis found multiple pathways that may be relevant to PD pathology (Supplementary Table 9), including those related to mitochondria (response to mitochondrial depolarization) vesicles (vesicle uncoating, phagolysosome assembly, regulation of autophagosome maturation) tau protein (tau protein kinase activity) USP25 (rs1736020-A)* FAM47E (rs6854006-T) ASH1L (rs12734374-A) BRIP1 (rs61169879-T) TMEM163 (rs57891859-A) STK39(rs12987123-A) IRS2 (rs1078514-T)* CNTN1 (rs73273590-A) ASXL3 (rs8091977-T) MYLK2 (rs6060983-T)* IGSF9B (rs3802920-T) SV2C (rs246815-C) ITGA2B (rs850731-T) DLG2 (rs6592142-T) ELOVL7 (rs1867598-A) HIP1R (rs10847864-T) SIPA1L2 (rs1326293-T) SETD1A (rs1870293-T) ITGA8 (rs13664-T) IP6K2 (rs12497850-T) PDLIM2 (rs11783129-A) VAMP4 (rs10903-A) MIPOL1 (rs1956438-A) RIMS1 (rs12528068-T) ALL EUR EUR2 EAS EAS2 AMR AMR2 AFR 0 20 40 60 80 100 Heterogeneity (I2, %) I2 Ancestry Residual –0.6514 0 0.4003 Beta Heterogeneity in top variant per locus (I2 > 30% only) MTF2 (rs11164870-C) PIK3CA (rs7636454-A) ADD1 (rs16843452-T) SYBU (rs6469271-T) IRS2 (rs1078514-T) USP8 (rs28648524-A) PIGL (rs11650438-A) FASN (rs4485435-C) MYLK2 (rs6060983-T) USP25 (rs1736020-A) EP300 (rs73174657-A) PPP6R2 (rs10775809-A) ALL EUR EUR2 EAS EAS2 AMR AMR2 AFR 0 20 40 60 80 100 Heterogeneity (I2, %) I2 Ancestry Residual –0.2846 0 0.4003 Beta Heterogeneity in top variants in novel loci a b Fig. 3 | Heterogeneity upset plots. a, Top variants per novel loci. b, Top variants per MR-MEGA identified locus with moderate to high heterogeneity (I2 > 30). The top bar plot illustrates heterogeneity with dark blue indicating ancestry heterogeneity proportion and light blue indicating other residual heterogeneity proportion. The bottom plot shows the subcohort level beta values with blue indicating positive and red indicating negative effect directions. Three variants with greater than 30% I2 total heterogeneity were only identified in the MR-MEGA meta-analysis method, whereas little to no heterogeneity is observed in loci identified in random effect. Nature Genetics | Volume 56 | January 2024 | 27–36 33 Article https://doi.org/10.1038/s41588-023-01584-8 and immune cells (microglial cell/macrophage proliferation, and natural killer T cell differentiation)36. Neither mitochondrial nor immune cell pathways were significant in the previous European-only meta-analysis. Novel signals from the multi-ancestry approach may have given enough power to highlight these ontology terms. Out of 10 ontology terms that were significant in the previous European-only meta-analysis1, 4 terms were not tested due to version differences in MSigDB and only 2 of the remaining terms were significant. However, the other 4 terms were still nominally significant at P < 0.05. This may be due to genome-wide signals that were less significant due to their heterogeneity across the different populations. Although this is a large multi-ancestry PD meta-analysis GWAS, the European population is still overrepresented. Around 80% of full PD cases are of European descent. Individuals of African descent were particularly underrepresented at just 0.5% of the effective PD cases. The discoveries in our study warrant future efforts to expand studies in more diverse populations. The Global Parkinson’s Genetics Program (GP2) is partnering with institutions that care for underrepresented populations to generate data for these underserved communities all over the world5, and we will continue the ongoing analysis as more participants are genotyped. Just as the first PD GWASs failed to identify significant signals38,39, we are confident that future diverse ancestry GWAS will produce impactful association results as sample sizes increase. Further efforts in multi-ancestry and non-European GWAS will identify loci that are more relevant to the global population and will continue to facilitate finemapping efforts to identify the genetic variants that drive these associations. 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M. et al. High-resolution whole-genome association study of Parkinson disease. Am. J. Hum. Genet. 77, 685–693 (2005). Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. 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Fon32, Oury Monchi33, Ted Fon34, Benjamin Pizarro Galleguillos35, Marcelo Miranda36, Maria Leonor Bustamante37, Patricio Olguin35, Pedro Chana38, Beisha Tang39, Huifang Shang40, Jifeng Guo41, Piu Chan42, Wei Luo43, Gonzalo Arboleda44, Jorge Orozc45, Marlene Jimenez del Rio46, Alvaro Hernandez47, Mohamed Salama48, Walaa A. Kamel49, Yared Z. Zewde50, Alexis Brice51, Jean-Christophe Corvol52, Ana Westenberger53, Anastasia Illarionova54, Brit Mollenhauer55, Christine Klein53, Eva-Juliane Vollstedt53, Franziska Hopfner56, Günter Höglinger56, Harutyun Madoev53, Joanne Trinh53, Johanna Junker53, Katja Lohmann53, Lara M. Lange53,57, Manu Sharma58, Sergiu Groppa59, Thomas Gasser58, Zih-Hua Fang60, Albert Akpalu61, Georgia Xiromerisiou62, Georgios Hadjigorgiou62, Ioannis Dagklis63, Ioannis Tarnanas64, Leonidas Stefanis65, Maria Stamelou66, Efthymios Dadiotis62, Alex Medina67, Germaine Hiu-Fai Chan68, Nancy Ip69, Nelson Yuk-Fai Cheung68, Phillip Chan69, Xiaopu Zhou69, Asha Kishore70, K. P. Divya71, Pramod Pal72, Prashanth Lingappa Kukkle73, Roopa Rajan74, Rupam Borgohain75, Mehri Salari76, Andrea Quattrone77, Nature Genetics | Volume 56 | January 2024 | 27–36 35 Article https://doi.org/10.1038/s41588-023-01584-8 Enza Maria Valente78, Lucilla Parnetti79, Micol Avenali78, Tommaso Schirinzi80, Manabu Funayama81, Nobutaka Hattori82, Tomotaka Shiraishi83, Altynay Karimova84, Gulnaz Kaishibayeva84, Cholpon Shambetova85, Rejko Krüger86, Ai Huey Tan87, Azlina Ahmad-Annuar87, Mohamed Ibrahim Norlinah88, Nor Azian Abdul Murad89, Shahrul Azmin90, Shen-Yang Lim87, Wael Mohamed91, Yi Wen Tay87, Daniel Martinez-Ramirez92, Mayela Rodriguez-Violante93, Paula Reyes-Pérez94, Bayasgalan Tserensodnom95, Rajeev Ojha96, Tim J. Anderson97, Toni L. Pitcher97, Arinola Sanyaolu98, Njideka Okubadejo98, Oluwadamilola Ojo99, Jan O. Aasly100, Lasse Pihlstrøm101, Manuela Tan101, Shoaib Ur-Rehman102, Diego Veliz-Otani5,6, Mario Cornejo-Olivas103, Maria Leila Doquenia104, Raymond Rosales104, Angel Vinuela105, Elena Iakovenko106, Bashayer Al Mubarak107, Muhammad Umair108, Eng-King Tan15, Michelle Mulan Lian7,8,167, Jia Nee Foo7,8,168, Ferzana Amod109, Jonathan Carr110, Soraya Bardien110, Beomseok Jeon111, Yun Joong Kim112, Esther Cubo113, Ignacio Alvarez114, Janet Hoenicka115, Katrin Beyer116, Maria Teresa Periñan117, Pau Pastor118, Sarah El-Sadig119, Kajsa Brolin120, Christiane Zweier121, Gerd Tinkhauser122, Paul Krack121, Chin-Hsien Lin123, Hsiu-Chuan Wu124, Pin-Jui Kung125, Ruey-Meei Wu123, Yihru Wu124, Rim Amouri126, Samia Ben Sassi127, A. Nazl Başak128, Gencer Genc129, Özgür Öztop Çakmak128, Sibel Ertan128, Alastair J. Noyce2, Alejandro Martínez-Carrasco13, Anette Schrag13, Anthony Schapira13, Camille Carroll130, Claire Bale131, Donald Grosset132, Eleanor J. Stafford13, Henry Houlden13, Huw R. Morris13,14, John Hardy13, Kin Ying Mok13, Mie Rizig13, Nicholas Wood13, Nigel Williams133, Olaitan Okunoye13, Patrick Alfryn Lewis134, Rauan Kaiyrzhanov13, Rimona Weil13, Seth Love135, Simon Stott136, Simona Jasaityte13, Sumit Dey2, Vida Obese13, Alberto Espay137, Alyssa O’Grady138, Andrew B. Singleton1,4, Andrew K. Sobering139, Bernadette Siddiqi138, Bradford Casey138, Brian Fiske138, Cabell Jonas140, Carlos Cruchaga141, Caroline B. Pantazis4, Charisse Comart138, Claire Wegel142, Cornelis Blauwendraat1,4,168, Dan Vitale1,3,4,167, Deborah Hall143, Dena Hernandez1, Ejaz Shiamim144, Ekemini Riley145, Faraz Faghri3,4, Geidy E. Serrano146, Hampton Leonard1,3,4, Hirotaka Iwaki1,3,4, Honglei Chen147, Ignacio F. Mata16, Ignacio Juan Keller Sarmiento148, Jared Williamson144, Jonggeol Jeffrey Kim1,2,167, Joseph Jankovic149, Joshua Shulman149,150, Justin C. Solle138, Kaileigh Murphy138, Karen Nuytemans151, Karl Kieburtz152, Katerina Markopoulou153, Kenneth Marek154, Kristin S. Levine3,4, Lana M. Chahine155, Laura Ibanez156, Laurel Screven4, Lauren Ruffrage157, Lisa Shulman158, Luca Marsili137, Maggie Kuhl138, Marissa Dean157, Mary B. Makarious1,13,14, Mathew Koretsky1,4, Megan J. Puckelwartz148, Miguel Inca-Martinez16, Mike A. Nalls1,3,4,168, Naomi Louie138, Niccolò Emanuele Mencacci148, Roger Albin159, Roy Alcalay160, Ruth Walker161, Sara Bandres-Ciga1,4, Sohini Chowdhury138, Sonya Dumanis162, Steven Lubbe148, Tao Xie163, Tatiana Foroud164, Thomas Beach146, Todd Sherer138, Yeajin Song3,4, Duan Nguyen165, Toan Nguyen165 & Masharip Atadzhanov166 17Sanatorio de la Trinidad MitreINEBA, Buenos Aires, Argentina. 18Hospital JM Ramos Mejia, Buenos Aires, Argentina. 19Somnus Neurology Clinic, Yerevan, Armenia. 20Neuroscience Research Australia, Sydney, New South Wales, Australia. 21ANZAC Research Institute, Concord, New South Wales, Australia. 22Garvan Institute of Medical Research and Concord Repatriation General Hospital, Darlinghurst, New South Wales, Australia. 23Concord Hospital, Concord, New South Wales, Australia. 24QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia. 25Murdoch University, Perth, Western Australia, Australia. 26Medical University Vienna Austria, Vienna, Austria. 27Universidade Federal do Rio Grande do Sul / Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil. 28Federal University of Health Sciences of Porto Alegre, Porto Alegre, Brazil. 29Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil. 30University of São Paulo, São Paulo, Brazil. 31Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. 32Montreal Neurological Institute, Montreal, Quebec, Canada. 33Institut universitaire de gériatrie de Montréal, Montreal, Quebec, Canada. 34McGill University, Montreal, Quebec, Canada. 35Universidad de Chile, Santiago, Chile. 36Fundación Diagnosis, Santiago, Chile. 37Faculty of Medicine Universidad de Chile, Santiago, Chile. 38CETRAM, Santiago, Chile. 39Central South University, Changsha, China. 40West China Hospital Sichuan University, Chengdu, China. 41Xiangya Hospital, Changsha, China. 42Capital Medical University, Beijing, China. 43Zhejiang University, Hangzhou, China. 44Universidad Nacional de Colombia, Bogotá, Colombia. 45Fundación Valle del Lili, Santiago De Cali, Colombia. 46University of Antioquia, Medellin, Colombia. 47University of Costa Rica, San Jose, Costa Rica. 48The American University in Cairo, Cairo, Egypt. 49Beni-Suef University, Beni Suef, Egypt. 50Addis Ababa University, Addis Ababa, Ethiopia. 51Paris Brain Institute, Paris, France. 52Sorbonne Université, Paris, France. 53University of Lübeck, Lübeck, Germany. 54Deutsches Zentrum für Neurodegenerative Erkrankungen, Göttingen, Germany. 55University Medical Center Göttingen, Göttingen, Germany. 56Department of Neurology, University Hospital, LMU Munich, Munich, Germany. 57University Medical Center Schleswig-Holstein, Lübeck, Germany. 58University of Tubingen, Tübingen, Germany. 59University of Mainz, Mainz, Germany. 60The German Center for Neurodegenerative Diseases, Göttingen, Germany. 61University of Ghana Medical School, Accra, Ghana. 62University of Thessaly, Volos, Greece. 63Aristotle University of Thessaloniki, Thessaloniki, Greece. 64Ionian University, Corfu, Greece. 65Biomedical research Foundation of the Academy of Athens, Athens, Greece. 66Diagnostic and Therapeutic Centre HYGEIA Hospital, Marousi, Greece. 67Hospital San Felipe, Tegucigalpa, Honduras. 68Queen Elizabeth Hospital, Kowloon, Hong Kong. 69The Hong Kong University of Science and Technology, Kowloon, Hong Kong. 70Aster Medcity, Kochi, India. 71Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, India. 72National Institute of Mental Health & Neurosciences, Bengaluru, India. 73Manipal Hospital, Delhi, India. 74All India Institute of Medical Sciences, Delhi, India. 75Nizam’s Institute of Medical Sciences, Hyderabad, India. 76Shahid Beheshti University of Medical Science, Tehran, Iran. 77Magna Græcia University of Catanzaro, Catanzaro, Italy. 78University of Pavia, Pavia, Italy. 79University of Perugia, Perugia, Italy. 80University of Rome Tor Vergata, Rome, Italy. 81Juntendo University, Tokyo, Japan. 82Faculty of Medicine, Juntendo University, Tokyo, Japan. 83Jikei University School of Medicine, Tokyo, Japan. 84Institute of Neurology and Neurorehabilitation, Almaty, Kazakhstan. 85Kyrgyz State Medical Academy, Bishkek, Kyrgyzstan. 86University of Luxembourg, Luxembourg, Luxembourg. 87University of Malaya, Kuala Lumpur, Malaysia. 88Universiti Kebangsaan Malaysia, Selangor, Malaysia. 89UKM Medical Molecular Biology Institute, Kuala Lumpur, Malaysia. 90Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur, Malaysia. 91International Islamic University, Kuala Lumpur, Malaysia. 92Tecnologico de Monterrey, Monterrey, Mexico. 93Instituto Nacional de Neurologia y Neurocirugia, Mexico City, Mexico. 94Universidad Nacional Autónoma de México, Mexico City, Mexico. 95Mongolian National University of Medical Sciences, Ulaanbaatar, Mongolia. 96Tribhuvan University, Kirtipur, Nepal. 97University of Otago, Dunedin, New Zealand. 98University of Lagos, Lagos, Nigeria. 99College of Medicine of the University of Lagos, Lagos, Nigeria. 100Norwegian University of Science and Technology, Trondheim, Norway. 101Oslo University Hospital, Oslo, Norway. 102University of Science and Technology Bannu, Bannu, Pakistan. 103Universidad Cientifica del Sur, Lima, Peru. 104Metropolitan Medical Center, Manila, Philippines. 105University of Puerto Rico, San Juan, Puerto Rico. 106Research Center of Neurology, Moscow, Russia. 107King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia. 108King Abdullah International Medical Research Center, Jeddah, Saudi Arabia. 109University of KwaZulu-Natal, Durban, South Africa. 110Stellenbosch University, Stellenbosch, South Africa. 111Seoul National University Hospital, Seoul, South Korea. 112Yongin Severance Hospital, Seoul, South Korea. 113Hospital Universitario Burgos, Burgos, Spain. 114University Hospital Mutua Terrassa, Barcelona, Spain.