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 2,458,0632,458,063 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, EP3OO 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. 尽管使用全基因组关联研究已经确定了 90 多种帕金森病的独立风险变异，但大多数研究一次只在一个人群中进行。在这里，我们对帕金森病进行了大规模的多血统荟萃分析，有 49,049 例、18,785 例代理病例和 2,458,0632,458,063 对照，包括欧洲、东亚、拉丁美洲和非洲血统的个体。在一项荟萃分析中，我们确定了 78 个独立的全基因组重要基因座，包括 12 个潜在的新基因座 （MTF2、PIK3CA、ADD1、SYBU、IRS2、USP8、PIGL、FASN、MYLK2、USP25、EP3OO 和 PPP6R2）和 6 个已知 PD 基因座的 6 个推定因果变异。通过将我们的结果与公开可用的 eQTL 数据相结合，我们在这些新基因座中鉴定了 25 个推定的风险基因，这些基因的表达与 PD 风险相关。这项工作为未来旨在鉴定非欧洲人群 PD 基因座的努力奠定了基础。
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 帕金森病 （PD） 是一种神经退行性疾病，病理学定义为大脑中的路易体包涵体和中脑中多巴胺能神经元的死亡。确定遗传风险因素对于减轻 PD 的全球负担至关重要，PD 是增长最快的与年龄相关的神经退行性疾病之一。一个大
PD genome-wide association study (GWAS) meta-analysis uncovered 90 independent genetic risk variants in individuals of European ancestry ^(1){ }^{1}. Similarly, large-scale PD GWAS meta-analyses of East Asian ^(2){ }^{2} and a single GWAS of Latin American ^(3){ }^{3} individuals have each identified two risk loci that were not previously identified in Europeans. For PD, there PD 全基因组关联研究 （GWAS） 荟萃分析发现了欧洲血统个体中的 90 个独立的遗传风险变异 ^(1){ }^{1} 。同样，东亚 ^(2){ }^{2} 的大规模 PD GWAS 荟萃分析和拉丁美洲 ^(3){ }^{3} 个体的单一 GWAS 分别确定了两个以前在欧洲人中未发现的风险位点。对于 PD，有
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){ }^{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. 现在正在大规模努力对代表性不足的人群的基因组数据进行测序和分析，目标是识别新的相关位点、精细映射已知位点并解决当前精准医学工作 ^(4,5){ }^{4,5} 中存在的不平等。在这里，我们通过包括来自四个祖先群体的个体对 PD GWAS 进行了大规模多血统荟萃分析 （MAMA）：欧洲、东亚、拉丁美洲和非洲。这项工作可以作为未来遗传分析的指南，以增加祖先的代表性。
Meta-analyses identify 66 known and 12 novel loci Meta 分析确定了 66 个已知位点和 12 个新位点
In addition to results from previously described European ^(1){ }^{1}, East Asian ^(2){ }^{2} and Latin American ^(3){ }^{3} studies, we also usedFinnGen 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 regression ^(6){ }^{6} 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){ }^{7}. The random-effects model had greater power to detect homogenous allelic effects ^(7){ }^{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. 除了先前描述的欧洲 ^(1){ }^{1} 、东亚 ^(2){ }^{2} 和拉丁美洲 ^(3){ }^{3} 研究的结果外，我们还使用了 FinnGen 和 23andMe， Inc 的东亚、拉丁美洲和非洲队列的其他数据集（表 1、图 1 和补充表 1）。我们总共纳入了 49,049 例 PD 病例、18,618 例代理病例（PD 的一级亲属）和 2,458,063 例神经系统健康的对照。来自血统 ^(6){ }^{6} 内连锁不平衡 （LD） 评分回归的遗传协方差截距接近零或接近 95% 置信区间，这意味着队列之间没有样本重叠（补充表 1）。在将数据协调并映射到基因组构建 hg19 后，使用随机效应模型和多种族遗传关联的 meta 回归 （MR-MEGA） ^(7){ }^{7} 进行 MAMAs。随机效应模型在检测同质等位基因效应 ^(7){ }^{7} 方面具有更大的能力。MR-MEGA 在其 meta 回归分析中使用遗传变异轴作为协变量，并且在检测不同队列中的异质效应方面具有更大的能力。MR-MEGA 还使用不同队列的等位基因频率产生的遗传变异轴将祖先异质性（由于祖先水平遗传变异导致的效果估计差异）与残余异质性区分开来。
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 5xx10^(-9)5 \times 10^{-9}, a more stringent threshold chosen to account for the larger number of haplotypes resulting from the ancestrally diverse datasets ^(8){ }^{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)\left(P_{\text {ANC-HET }}<0.05\right) but no significant residual heterogeneity (P_("RES-HET ") > 0.148)\left(P_{\text {RES-HET }}>0.148\right), 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 xxP_{\text {ANC-HET }}=5.3 \times10^(-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 ")=0.035P_{\text {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){ }^{9}. Lastly, the USP25 locus had the most significant ancestral heterogeneity (lead SNP rs1736020, P_("ANC-Het ")=4.74 xx10^(-5)P_{\text {ANC-Het }}=4.74 \times 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. 结合随机效应模型和 MR-MEGA 的结果，我们发现了 12 个新的帕金森病风险位点和 66 个来自单祖先 GWAS 的已知风险位点（表 2、图 2 和补充表 2-5）的命中，它们满足 Bonferroni 校正的 alpha 5xx10^(-9)5 \times 10^{-9} ，这是一个更严格的阈值，用于解释由祖先多样化的数据集 ^(8){ }^{8} 产生的大量单倍型.在确定的 78 个风险位点中，69 个在随机效应模型中显著，而 3 个仅在 MR-MEGA 中显著。通过随机效应方法发现的 8 个新基因座在 4 个不同的祖先中显示出同质效应。通过随机效应方法鉴定的另一个新基因座 （FASN） 在所有可用人群中都显示出同质效应，但请注意，该变体在两个东亚数据集中都未能通过质量控制。其他 3 个位点仅在 MR-MEGA 中鉴定，显示出祖先异质效应。所有三个基因座（IRS2、MYLK2 和 USP25）都显示出显著的祖先异质性 (P_("ANC-HET ") < 0.05)\left(P_{\text {ANC-HET }}<0.05\right) 的证据，但没有显著的残余异质性 (P_("RES-HET ") > 0.148)\left(P_{\text {RES-HET }}>0.148\right) ，支持信号是由于种群结构差异而不是其他混杂因素的观点（图 3）。对于 IRS2 基因座 （先导 SNP rs1078514， ），与荟萃分析效应估计相比， P_("ANC-HET ")=5.3 xxP_{\text {ANC-HET }}=5.3 \times10^(-3)10^{-3} 芬兰队列具有相反的效果方向（补充图 4）。同样，MYLK2 位点的非洲效应估计与荟萃分析效应估计 （lead SNP rs6060983， P_("ANC-HET ")=0.035P_{\text {ANC-HET }}=0.035 ） 差异最大，表明种群之间的效应不同。 虽然这是一个新的单性状 GWAS 基因座，但其先导 SNP 之前在精神分裂症和 PD 之间的多性状条件/连接性错误发现率 （FDR） 研究中被发现为潜在的多效性基因座 ^(9){ }^{9} 。最后，USP25 位点具有最显著的祖先异质性 （先导 SNP rs1736020）， P_("ANC-Het ")=4.74 xx10^(-5)P_{\text {ANC-Het }}=4.74 \times 10^{-5} 其影响特定于欧洲和非洲队列，尽管方向不同。当查看最接近每个新先导 SNP 的蛋白质编码基因及其功能丧失不耐受 （pLI） 评分的概率时，我们发现 12 个基因中有 7 个 pLI 评分为 0.99 或 1。在具有 （MYLK2） 和无 （SYBU 、 PIGL 和 PPP6R2） 显著祖先异质性的基因座中均发现低 pLI 评分的基因。
Study Ancestral population Cases/proxy/controls
Nalls et al. ^(1) European (EUR) 37,688//18,618//1,411,006
Foo et al. ^(2) East 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| Study | Ancestral population | Cases/proxy/controls |
| :--- | :--- | :--- |
| Nalls et al. ${ }^{1}$ | European (EUR) | $37,688 / 18,618 / 1,411,006$ |
| Foo et al. ${ }^{2}$ | East 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 | | $49,049 / 18,618 / 2,458,063$ |
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\mathrm{PP}=0.972 ) despite being significant in the European study ^(1){ }^{1} but not in the East Asian study ^(2){ }^{2}. We also observed that the novel lead variants for MTF2 (rs35940311), PIK3CA (rs11918587), EP3OO (rs4820434) and PPP6R2(rs60708277) had higher PP estimates for being shared causal variants across both populations (PP_("shared ")=0.757,0.214,0.769,0.946)\left(\mathrm{PP}_{\text {shared }}=0.757,0.214,0.769,0.946\right) than for being causal variants in a single population (PP_(EUR) < 0.080:}\left(\mathrm{PP}_{\mathrm{EUR}}<0.080\right., PP_(EAS) < 0.001\mathrm{PP}_{\mathrm{EAS}}<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. PESCA v0.3 （参考文献 10） 用于主要的欧洲和东亚荟萃分析，并探讨了主要分析中确定的所有基因座（补充表 6）。PESCA 使用祖先匹配的 LD 估计值来推断因果变异是种群特异性的还是在两个种群之间共享的。被确定为在人群之间共享的变异可能更可能是因果关系。此外，我们预计 MAMA 鉴定的基因座中的共同因果变异的后验概率 （PP） 更高，即使它们之前未在单血统研究中鉴定过。RIMS1 基因座中的先导 SNP （rs12528068） 具有较高的 PP，因为它是一个共同的因果变异 （ PP=0.972\mathrm{PP}=0.972 ），尽管在欧洲研究中 ^(1){ }^{1} 很重要，但在东亚研究中 ^(2){ }^{2} 不显著。我们还观察到 MTF2 （rs35940311）、PIK3CA （rs11918587）、EP3OO （rs4820434） 和 PPP6R2 （rs60708277） 的新先导变异在两个群体中共享因果变异的 PP 估计值 (PP_("shared ")=0.757,0.214,0.769,0.946)\left(\mathrm{PP}_{\text {shared }}=0.757,0.214,0.769,0.946\right) 高于在单个群体 (PP_(EUR) < 0.080:}\left(\mathrm{PP}_{\mathrm{EUR}}<0.080\right. 中的因果变异 ， PP_(EAS) < 0.001\mathrm{PP}_{\mathrm{EAS}}<0.001 ）。然而，重要的是要注意，欧洲和东亚数据之间的样本量差异影响了我们在这些位点中的任何一个上检测人群特异性因果变异的能力。
We found 17 suggestive loci that failed to meet our stringent significance threshold but had P < 5xx10^(-8)P<5 \times 10^{-8} in a fixed-effects meta-analysis and P < 1xx10^(-6)P<1 \times 10^{-6} in the random-effects meta-analysis (Supplementary Table 4). Fourteen of these regions were novel loci. Two loci nearJAK1 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%0.02 \%, 0.23 \%; AFR: 1.64%,8.84%1.64 \%, 8.84 \%;AMR: 0.41%0.41 \%, 1.91%). If confirmed, these loci would confer a strong effect on PD risk (beta: -1.3,-0.54-1.3,-0.54 ). These loci merit further studies in the African and Latin American populations. 我们发现了 17 个提示性基因座，这些基因座未能达到我们严格的显著性阈值，但在固定效应 meta 分析和随机效应 meta 分析 P < 1xx10^(-6)P<1 \times 10^{-6} 中具有 P < 5xx10^(-8)P<5 \times 10^{-8} （补充表 4）。其中 14 个区域是新位点。JAK1 和 HS1BP3 附近的两个位点仅在 23andMe 拉丁美洲和非洲队列中发现。这些基因座的领先 SNP（rs578139575 和 rs73919910）是非编码的，在欧洲人群中非常罕见，但在非洲人和拉丁美洲人中更常见（gnomAD v3.1.2 次要等位基因频率，以欧元为单位： 0.02%,0.23%0.02 \%, 0.23 \% ;AFR： 1.64%,8.84%1.64 \%, 8.84 \% ;AMR： 0.41%0.41 \% ，1.91%）。如果得到证实，这些基因座将对 PD 风险产生强烈影响 （beta： -1.3,-0.54-1.3,-0.54 ）。这些基因座值得在非洲和拉丁美洲人群中进一步研究。
Fine-mapping identifies six credible sets with single variants 精细映射识别具有单个变体的 6 个可信集
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%95 \% credible set. Of these, MR-MEGA nominated a single putative causal variant with > 95%>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){ }^{11}. The putative variants HIP1RH I P 1 R have strong evidence for regulome binding (RegulomeDB rank <= 2\leq 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). 还使用 MR-MEGA 进行了精细映射，MR-MEGA 使用祖先异质性来提高精细映射分辨率。我们确定了 23 个基因座， 95%95 \% 这些基因座在可信集中的变异少于 5 个。其中，MR-MEGA 提名了一个 > 95%>95 \% PP 在 6 个位点的推定因果变异：TMEM163、TMEM175、SNCA、CAMK2D、HIP1R 和 LSM7（表 3 和补充表 7 和 8）。我们的结果肯定了之前的结果，表明 TMEM175 p.M393T 编码变体是可能的因果变体 ^(11){ }^{11} 。推定的变体 HIP1RH I P 1 R 具有调节组结合的有力证据（RegulomeDB 等级 <= 2\leq 2 ）。特别是 HIP1R 变体 rs10847864 位于转录起始位点，该位点活跃于黑质组织（染色质状态窗口：chr12：123326200.123327200）和脊髓和大脑中的星形胶质细胞（染色质状态窗口：chr12：123326400.123326600）。在包含单个变异的可信集之外，我们在两个基因中鉴定了错义变异： FCGR2A （p.H167R， PP = 0.145） 和 SLC18B1 （p.S30P， PP = 0.780）。
Study participants 研究参与者
Goal: Collate the largest and most diverse set of participants in Parkinson’s disease genomics 目标：整理帕金森病基因组学中规模最大、最多样化的参与者
Multiancestry genome-wide meta-analysis 多血统全基因组荟萃分析
Goal: Identify common SNPs that are associated with Parkinson’s disease risk that are applicable across different ancestries 目标：确定与帕金森病风险相关的常见 SNP，这些 SNP 适用于不同的祖先
Downstream analyses 下游分析
Goal: Interpret the meta-analysis results and identify potential targets and biological mechanisms 目标：解释荟萃分析结果并确定潜在靶点和生物学机制
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 图 1 |MAMA 研究设计。上图：荟萃分析中使用的四个祖先组。中图：MAMA 和使用的两种方法。随机效应（上图）更适合于不同祖先之间效应方向同质的风险变异，而 MR-MEGA（下图）可以识别由于祖先引入的群体分层而具有异质效应的风险变异
differences. The densely dashed lines indicate Bonferroni adjusted suggestive threshold of two-sided P < 1xx10^(-6)P<1 \times 10^{-6}, and the loosely dashed lines indicate Bonferroni adjusted significant threshold of two-sided P < 5xx10^(-9)P<5 \times 10^{-9}. Bottom panel: downstream analyses and their examples. Created with Biorender.com. 差异。密集的虚线表示 Bonferroni 调整的双侧 P < 1xx10^(-6)P<1 \times 10^{-6} 暗示阈值 ，松散的虚线表示 Bonferroni 调整的双侧 P < 5xx10^(-9)P<5 \times 10^{-9} 显著阈值。下图：下游分析及其示例。使用 Biorender.com 创建。
Gene set analysis finds enrichment in brain tissues 基因集分析发现脑组织中富集
We used the Functional Mapping and Annotation (FUMA) software ^(12,13){ }^{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){ }^{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 我们使用功能映射和注释 （FUMA） 软件 ^(12,13){ }^{12,13} 对随机效应结果进行功能注释。我们生成了一个定制的 1000 基因组参考面板，它反映了我们数据集的祖先比例，并针对基因本体、组织水平和单细胞表达数据运行了基因组注释 （MAGMA） ^(14){ }^{14} 的多标记分析。我们在 MSigDB v7.0（参考文献 15）中测试了 16,992 个基因本体集，并使用条件分析来丢弃冗余术语或识别必须一起解释的基因集。我们发现 40 个基因集通过条件分析显著富集
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){ }^{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 ")=0.018P_{\text {FDR }}=0.018 ) and ‘cellular component: vacuolar membrane’ ( P_(FDR)=0.047P_{\mathrm{FDR}}=0.047 ). In addition, ontology terms in immune system pathways (microglial cell proliferation, macrophage proliferation, natural killer T cell differentiation: P_(FDR) < 0.04P_{\mathrm{FDR}}<0.04 ), mitochondria (response to mitochondrial depolarization: P_("FDR ")=0.028P_{\text {FDR }}=0.028 ), vesicles (vesicle uncoating, 确定 13 个与至少一个其他基因集共享信号的基因集（补充表 9）。这比 Nalls 及其同事 ^(1){ }^{1} 进行的欧洲荟萃分析中的前 10 个基因集大幅增加。在 Nalls 等人的荟萃分析中，只有两个基因本体论术语在多次测试校正后的多血统结果中也具有显着意义：“精选基因集：Ikeda MIR30 Targets Up”（ P_("FDR ")=0.018P_{\text {FDR }}=0.018 ） 和“细胞成分：液泡膜”（ P_(FDR)=0.047P_{\mathrm{FDR}}=0.047 ）。此外，免疫系统途径中的本体术语（小胶质细胞增殖、巨噬细胞增殖、自然杀伤 T 细胞分化： P_(FDR) < 0.04P_{\mathrm{FDR}}<0.04 ）、线粒体（对线粒体去极化的反应： P_("FDR ")=0.028P_{\text {FDR }}=0.028 ）、囊泡（囊泡脱壳、
^(1){ }^{1} Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. ^(2){ }^{2} Preventive Neurology Unit, Centre for Prevention Diagnosis and Detection, Wolfson Institute of Population Health, Queen Mary University of London, London, UK. ^(3){ }^{3} Data Tecnica International, Washington, DC, USA. ^(4){ }^{4} Center 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. ^(5){ }^{5} Neurogenetics Research Center, Instituto Nacional de Ciencias Neurológicas, Lima, Peru. ^(6){ }^{6} Institute for Genome Sciences, University of Maryland, Baltimore, MD, USA. ^(7){ }^{7} Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Singapore. ^(8){ }^{8} Genome Institute of Singapore, Agency for Science, Technology and Research, A*STAR, Singapore, Singapore. ^(9)23{ }^{9} 23 andMe, Inc., Sunnyvale, CA, USA. ^(10){ }^{10} Pharmaceutical Sciences and Pharmacogenomics, UCSF, San Francisco, CA, USA. "Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA. ^(12){ }^{12} Memory and Aging Center, UCSF, San Francisco, CA, USA. ^(13){ }^{13} Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK. ^(14){ }^{14} UCL Movement Disorders Centre, University College London, London, UK. ^(15){ }^{15} Department of Neurology, National Neuroscience Institute, Duke NUS Medical School, Singapore, Singapore. ^(16){ }^{16} Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA. ^(167){ }^{167} These authors contributed equally: Jonggeol Jeffrey Kim, Dan Vitale, Diego Veliz-Otani, Michelle Mulan Lian. ^(168){ }^{168} These 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\boxtimes e-mail: kimjoj@nih.gov; cornelis.blauwendraat@nih.gov; mike@datatecnica.com; jianee.foo@ntu.edu.sg; matai@ccf.org ^(1){ }^{1} 美国国立卫生研究院国家老龄化研究所神经遗传学实验室，美国马里兰州贝塞斯达。 ^(2){ }^{2} 英国伦敦玛丽皇后大学沃尔夫森人口健康研究所预防诊断和检测中心预防神经病学科。 ^(3){ }^{3} Data Tecnica International，美国华盛顿特区。 ^(4){ }^{4} 美国国立卫生研究院国家老龄化研究所和国家神经疾病和中风研究所阿尔茨海默氏症和相关痴呆症中心 （CARD），美国马里兰州贝塞斯达。 ^(5){ }^{5} 神经遗传学研究中心，秘鲁利马国家神经科学研究所。 ^(6){ }^{6} 马里兰大学基因组科学研究所，美国马里兰州巴尔的摩。 ^(7){ }^{7} 新加坡南洋理工大学李光前医学院，新加坡，新加坡。 ^(8){ }^{8} 新加坡基因组研究所，科学、技术和研究局，A*STAR，新加坡，新加坡。 ^(9)23{ }^{9} 23 以及美国加利福尼亚州桑尼维尔的 Me， Inc.。 ^(10){ }^{10} 加州大学旧金山分校药物科学和药物基因组学。“美国加利福尼亚州旧金山加州大学旧金山分校神经病学系和威尔神经科学研究所。 ^(12){ }^{12} 美国加利福尼亚州旧金山分校 UCSF 记忆与衰老中心。 ^(13){ }^{13} 英国伦敦大学学院皇后广场神经病学研究所临床和运动神经科学系。 ^(14){ }^{14} 伦敦大学学院运动障碍中心，伦敦大学学院，英国伦敦。 ^(15){ }^{15} 杜克新加坡国立大学医学院国家神经科学研究所神经病学系，新加坡，新加坡。 ^(16){ }^{16} 基因组医学，克利夫兰诊所基金会勒纳研究所，美国俄亥俄州克利夫兰。 ^(167){ }^{167} 这些作者的贡献相同：Jonggeol Jeffrey Kim、Dan Vitale、Diego Veliz-Otani、Michelle Mulan Lian。 ^(168){ }^{168} 这些作者共同监督了这项工作：Cornelis Blauwendraat、Mike A. Nalls、Jia Nee Foo、Ignacio Mata。*作者及其单位列表显示在论文末尾。 ⊠e\boxtimes e -邮件：kimjoj@nih.gov;cornelis.blauwendraat@nih.gov;mike@datatecnica.com;jianee.foo@ntu.edu.sg;matai@ccf.org
