Genome-wide cross-trait analysis of vascular dementia and Alzheimer’s disease highlights novel loci and lung-brain axis

Genome-wide cross-trait analysis of vascular dementia and Alzheimer’s disease highlights novel loci and lung-brain axis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genome-wide cross-trait analysis of vascular dementia and Alzheimer’s disease highlights novel loci and lung-brain axis Guiyou Liu, Shan Gao, Shiyang Wu, Fengzhen Liu, Ping Zhu, Yijie He, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8988189/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Until now, most genetic risk for vascular dementia (VD) remains unknown. Here, we firstly performed the largest cross-ancestry genome-wide association study meta-analysis comprising 5,886 VD and 1,027,883 controls of European, East Asian, South Asian, African, and Admixed American ancestry. We identified 37 genome-wide significant loci including CLU and APOE tagged by common variants and 35 loci tagged by rare variants, and demonstrated enrichment of VD heritability in lung and genetic association between VD and lung function traits. We further conducted a cross-trait of VD and Alzheimer’s disease, and identified 13 genome-wide significant loci including CR1 , BIN1 , GRM7 , HLA-DRA , TREM2 , CLU , ECHDC3 , AGBL2 , MS4A4E , PICALM , SLC24A4 , ABCA7 , and APOE. A multi-omics integrative analysis identified 619 genes. 241 genes were significantly differentially expressed in VD cells and 21 exhibited strong evidence of interaction with FDA-approved drugs. Collectively, our findings provide valuable insights into the potential underlying mechanisms of VD. Biological sciences/Genetics Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Vascular dementia (VD), caused by reduced blood flow to the brain, is the second most common form of dementia after Alzheimer’s disease (AD) and accounts for at least 20% of dementia cases 1 , 2 . Genetic factors play important roles in the etiology of both AD and VD 1,2 . Until now, large-scale genome-wide association study (GWAS) datasets have identified the common AD genetic variants and risk loci especially APOE 3 . Unlike AD, there are only few VD GWAS and limited sample sizes including 67 cases and 5,700 controls 4 , 84 cases and 200 controls 5 , 373 cases and 3,289 controls 6 , 89 cases and 3,016 controls 6 . The Mega Vascular Cognitive Impairment and Dementia (MEGAVCID) Consortium performed a large-scale GWAS including 3,892 VD and 466,606 controls of European descent, and only identified one genetic variant rs429358 near the APOE reaching the genome-wide significance with P = 2.90E-196 7 . Until now, the majority of genetic risk of VD remains unknown. It is known that GWAS was designed to broadly capture the common genetic variants with a minor allele frequency (MAF) greater than 1% 8,9 . Rare variants were not tagged by common genetic variants from genotyping arrays and imputation 8 , 9 . It was largely unknown about the contribution of rare variants (MAF < 1%) to human traits and diseases 8 , 9 . Until recently, publicly available biobanks using whole-genome sequence offered an unprecedented opportunity to assess the effects of both common and rare genetic variants on human traits and diseases, and highlighted large effects and significant contribution of rare variants 10 – 14 . Here, we hypothesized that VD GWAS using rare variants may contribute to (1) increase the number of novel genetic variants and susceptibility loci, (2) identify the rare variants of large effects, and (3) increase the proportion of heritability. We collected four publicly available biobanks, and conducted the largest VD cross-ancestry GWAS meta-analysis to date in 5,886 patients diagnosed with VD and 1,027,883 control individuals from five ancestral populations: European, East Asian, South Asian, African, and Admixed American using genetic variants with MAF > 0.01%. We further systematically characterized the genetic architecture of VD using multiple multi-omics integration approaches including gene mapping, gene-based association test, polygenic priority score (PoPS) analysis, gene set enrichment analysis, tissue enrichment analysis, transcriptome-wide association study (TWAS), colocalization analysis, summary-data-based Mendelian randomization (SMR), multi-trait analysis of GWAS (MTAG), case-control gene expression analysis, drug-gene interaction analysis, and genetic correlation analysis. An overview of the workflow is provided in Fig. 1 . Figure 1 Result European-specific GWAS meta-analysis (stage 1) We selected two independent VD GWAS datasets including 5,698 patients diagnosed with VD and 931,850 control individuals of European descent from UKBB (2,074 VD and 456,366 controls) 15 and FinnGen R12 (3,624 VD and 475,484 controls) 16 (Methods, Supplementary Table 1). We conducted a large-scale GWAS meta-analysis of both datasets using a fixed-effects inverse-variance weighted (IVW) method implemented in METAL 17 . Using linkage disequilibrium (LD) score regression (LDSC) 18 , we estimated the single nucleotide polymorphism (SNP) based heritability on liability scale to be ℎ 2 =6.57% and s.e.=0.0132, assuming a VD population prevalence of 1.16% 19 . The genomic inflation factor (λ GC ) was 1.0754 and the LDSC intercept was 1.0241 (s.e.=0.0078), which indicated little evidence of genetic inflation (Supplementary Fig. 1A). Utilizing Functional Mapping and Annotation (FUMA) 20 , we identified three independent genome-wide significant loci including HTR4 , CLU , and APOE , which are tagged by rs564080066, rs7982 and rs429358, respectively (Fig. 2 A, Table 1 , Supplementary Fig. 2). A sensitivity GWAS meta-analysis using GWAMA (Genome-Wide Association Meta-Analysis) further confirmed all three loci ( P < 5.00E-08) 21 (Fig. 2 A). rs564080066 is novel and rare variant with an effect allele frequency of 0.0023 in European population (Table 1 ). rs7982 is a proxy of rs11136000 ( r ²=0.98 and D’=0.99), which was a known genome-wide significant variant associated with AD 22 . rs429358 is a well-known genome-wide significant variant associated with multiple dementias including VD 7 , AD 22 , frontotemporal dementia (FTD) 23 , and Lewy body dementia (LBD) 24 . Using FUMA 20 , we identified 48 independent genetic variants ( r 2 < 0.6, Supplementary Table 2) and 21 independent lead variants ( r 2 < 0.1, Supplementary Table 3) around these three loci. A stepwise conditional analysis using GCTA conditional and joint (COJO) analysis and linkage disequilibrium (LD) information from UKBB individuals 25 further confirmed these three loci (Supplementary Table 4). Using positional mapping, eQTLs mapping, and chromatin interaction mapping (Supplementary Table 5, Supplementary Data 1), we identified 120 VD risk genes (Fig. 2 C, Supplementary Table 6). Table 1 Genome-wide signifiant loci from vascular dementia GWAS meta-analysis in stage 1 and stage 2 Locus SNP Known/Novel Chr Pos Ensembl ID Nearest gene A1 A2 Freq Beta SE P METAL P GWAMA Genome-wide-significant loci identified in Stage1 (EUR) 1 rs564080066 Novel 5 148,037,600 ENSG00000164270 HTR4 A G 0.9977 -1.0203 0.1862 4.29E-08 4.39E-08 2 rs7982 Novel 8 27,462,481 ENSG00000120885 CLU A G 0.4112 -0.1134 0.0196 7.62E-09 7.82E-09 3 rs429358 Known 19 45,411,941 ENSG00000130203 APOE T C 0.8331 -0.6945 0.0231 3.32E-199 3.93E-199 Genome-wide-significant loci identified in Stage2 (Cross) 1 rs150423973 Novel 1 116,871,703 ENSG00000163399 ATP1A1 T C 0.0007 3.5278 0.6405 3.63E-08 3.72E-08 2 rs55977072 Novel 1 120,072,559 ENSG00000203857 HSD3B1 A T 0.9998 -3.039 0.5555 4.49E-08 4.60E-08 3 rs555863053 Novel 1 167,628,193 ENSG00000198771 RCSD1 T C 0.0001 3.8996 0.7018 2.75E-08 2.81E-08 4 rs543737714 Novel 2 7,559,484 ENSG00000134321 RSAD2 A C 0.0006 3.3646 0.5672 3.00E-09 3.09E-09 5 rs571573246 Novel 2 129,694,243 ENSG00000136720 HS6ST1 A G 0.0005 3.3224 0.5601 3.00E-09 3.08E-09 6 rs138654898 Novel 2 170,746,289 ENSG00000144357 UBR3 A G 0.0002 3.1549 0.5596 1.72E-08 1.77E-08 7 rs536931879 Novel 3 9,967,952 ENSG00000163703 CRELD1 A G 0.0005 3.178 0.5826 4.90E-08 5.01E-08 8 rs79120584 Novel 4 89,592,499 ENSG00000138641 HERC3 T G 0.0003 3.0396 0.5096 2.45E-09 2.52E-09 9 rs534723021 Novel 5 20,583,716 ENSG00000145526 CDH18 A C 0.0002 4.0688 0.7008 6.39E-09 6.56E-09 10 rs200249535 Novel 5 79,616,698 ENSG00000164299 SPZ1 A G 0.0002 4.6334 0.7118 7.55E-11 7.81E-11 11 rs563370505 Novel 5 91,599,460 ENSG00000164199 ADGRV1 C G 0.9996 -3.1345 0.5539 1.53E-08 1.57E-08 12 rs564080066 Novel 5 148,037,600 ENSG00000164270 HTR4 A G 0.9977 -1.0359 0.1854 2.32E-08 2.38E-08 13 rs202007547 Novel 6 75,160,301 ENSG00000111799 COL12A1 T G 0.9996 -4.0564 0.743 4.77E-08 4.88E-08 14 rs542806512 Novel 6 86,810,914 ENSG00000135318 NT5E A G 0.9987 -3.166 0.5729 3.27E-08 3.35E-08 15 rs576730428 Novel 6 158,645,336 ENSG00000272047 GTF2H5 A G 0.0001 5.255 0.8526 7.11E-10 7.33E-10 16 rs561189374 Novel 7 149,911,042 ENSG00000106526 ACTR3C T G 0.0010 3.7286 0.6591 1.54E-08 1.58E-08 17 rs138507927 Novel 7 154,108,389 ENSG00000130226 DPP6 A T 0.9995 -2.7941 0.5004 2.36E-08 2.42E-08 18 rs11136000 Novel 8 27,464,519 ENSG00000120885 CLU T C 0.4110 -0.1074 0.0193 2.77E-08 2.83E-08 19 rs192554851 Novel 9 18,356,094 ENSG00000178031 ADAMTSL1 A G 0.0006 2.4517 0.4133 2.99E-09 3.07E-09 20 rs570028361 Novel 9 81,376,912 ENSG00000135069 PSAT1 C G 0.9948 -2.0568 0.3705 2.82E-08 2.89E-08 21 rs546018638 Novel 10 100,741,266 ENSG00000172987 HPSE2 A G 0.9999 -4.8774 0.832 4.56E-09 4.69E-09 22 rs535202922 Novel 11 118,302,718 ENSG00000167283 ATP5L T C 0.0001 5.7135 0.785 3.38E-13 3.53E-13 23 rs184836761 Novel 12 131,052,809 ENSG00000125207 PIWIL1 A G 0.0002 4.5144 0.7511 1.85E-09 1.90E-09 24 rs533330090 Novel 13 75,417,919 ENSG00000136111 TBC1D4 T C 0.0007 3.6903 0.6315 5.12E-09 5.26E-09 25 rs576381927 Novel 13 113,427,550 ENSG00000068650 ATP11A A G 0.0006 2.5067 0.4542 3.41E-08 3.49E-08 26 rs527795127 Novel 14 51,819,391 ENSG00000139921 TMX1 A G 0.0005 2.7189 0.4752 1.06E-08 1.08E-08 27 rs547455871 Novel 15 26,349,988 ENSG00000206190 ATP10A T G 0.9998 -5.0513 0.6487 6.89E-15 7.21E-15 28 rs183472759 Novel 15 42,506,323 ENSG00000103978 TMEM87A A G 0.0003 3.951 0.6778 5.57E-09 5.72E-09 29 rs146972069 Novel 15 100,714,857 ENSG00000140470 ADAMTS17 T C 0.0008 2.5234 0.4512 2.23E-08 2.29E-08 30 rs564332482 Novel 16 1,181,498 ENSG00000196557 CACNA1H T G 0.0005 2.6517 0.4703 1.72E-08 1.76E-08 31 rs529505148 Novel 16 47,270,214 ENSG00000129636 ITFG1 A G 0.0006 3.6966 0.6446 9.74E-09 1.00E-08 32 rs429358 Known 19 45,411,941 ENSG00000130203 APOE T C 0.8341 -0.6919 0.0228 2.35E-202 2.78E-202 33 rs78062743 Novel 20 31,826,339 ENSG00000131059 BPIFA3 T C 0.0012 3.3257 0.5876 1.51E-08 1.55E-08 34 rs573127339 Novel 20 35,794,556 ENSG00000101353 MROH8 A T 0.0003 3.8213 0.6256 1.01E-09 1.04E-09 35 rs569752694 Novel 20 40,364,147 ENSG00000124177 CHD6 T C 0.0001 5.6128 0.8552 5.28E-11 5.46E-11 36 rs575233877 Novel 20 40,716,015 ENSG00000196090 PTPRT T C 0.0002 4.8725 0.8291 4.18E-09 4.29E-09 37 rs529401448 Novel 22 47,747,652 ENSG00000054611 TBC1D22A A G 0.0001 5.7615 0.8953 1.23E-10 1.27E-10 SNP, single-nucleotide polymorphism; A1: effect allele; A2: non-effect allele; Chr : chromosome; Pos: position on hg19; Freq: the frequency of A1; Beta; effect size; SE; standard error. A total of 29 lines of evidence were used for gene prioritization, and each column represents a type of supportive evidence. The left table displays the gene symbol and the total score across all evidence categories. The right heatmap groups and colors evidence categories according to their respective domains. Only genes with a priority score ≥ 10 are shown in this figure, and the full results can be found in Supplementary Table 36. pLI, probability of being loss-of-function intolerant; CADD, combined annotation-dependent depletion; RDB, RegulomeDB; PoPS, polygenic priority score; TWAS, transcriptome-wide association study; COLOC, colocalization;,SMR, summary-data-based Mendelian randomization. Table 1 . Figure 2 . Cross-ancestry GWAS meta-analysis (stage 2) We conducted a cross-ancestry VD GWAS meta-analysis using the fixed-effects IVW meta-analysis method including 5,886 patients diagnosed with VD and 1,027,883 control individuals using four independent GWAS datasets from UKBB (European, 2,074 VD and 456,366 controls) 15 , FinnGen R12 (European, 3,624 VD and 475,484 controls) 16 , Genes & Health (GH, South Asian, 119 VD and 43,659 controls) 26 , and Mass General Brigham Biobank (MGBB, European, South Asian, African, and Admixed American, 69 VD and 52,374 controls) (Methods, Supplementary Table 1) 27 . The genomic inflation factor λ GC = 1.0741 and LDSC intercept of 1.0204 (s.e.= 0.0074) showed little evidence of genetic inflation (Supplementary Fig. 1B). The SNP-based heritability on liability scale was h 2 = 6.47% (s.e.=0.0112) assuming the VD population prevalence of 1.16% 19 . We revealed 37 independent genome-wide significant loci by confirming HTR4 (rs564080066), CLU (rs11136000), and APOE (rs429358) from European-specific GWAS meta-analysis, and highlighting 34 novel loci all tagged by rare variants (Fig. 2 B, Table 1 , Supplementary Fig. 3). These 37 GWAS loci explained 53% of VD variance including 13.25% from loci tagged by common variants and 39.19% from loci tagged by rare variants. A sensitivity GWAS meta-analysis using GWAMA shown in Fig. 2 B further confirmed 36 loci (excluding CRELD1 ) ( P < 5.00E-08) 21 . Using FUMA, we identified 111 independent genetic variants ( r 2 < 0.6, Supplementary Table 7) and 66 independent lead variants ( r 2 < 0.1, Supplementary Table 8) around these three loci ( HTR4 , CLU , APOE ). GCTA COJO analysis further confirmed these loci (Supplementary Table 9, Supplementary Data 2). Using positional mapping, eQTLs mapping, and chromatin interaction mapping, we identified 340 VD risk genes (Fig. 2 D, Supplementary Table 10). Functional annotation In stage 1, we annotated 388 SNPs in LD with the 48 independent significant lead variants using ANNOVAR 28 , and found predominant enrichment in the intronic, upstream, downstream, exonic, UTR3 and UTR5 (Supplementary Tables 11–12 and Fig. 2 E). Among these 388 SNPs, we identified 286 SNPs that were in LD ( r 2 > 0.6) with 48 independent significant lead variants, extracted from the 1000 genomes European reference panel (Supplementary Table 13) 29 . rs564080066 is an intronic variant with a Combined Annotation Dependent Depletion (CADD) score of 3.382, indicating a moderate potential of regulatory impact (Supplementary Table 13) 30 . rs7982 is an exonic variant with CADD score of 1.763, indicating a moderate potential of regulatory impact (Supplementary Table 13). rs429358 is an exonic variant with a high CADD score of 12.64, suggesting a potential of deleterious effects (Supplementary Table 13). In stage 2, we annotated 381 SNPs in LD with the 89 independent lead variants using ANNOVAR 28 , and revealed predominant enrichment in intronic, downstream, exonic, upstream, ncRNA_exonic and UTR3 genomic categories (Fig. 2 E, Supplementary Tables 14–15). Among these 381 SNPs, 278 SNPs were in LD ( r 2 > 0.6) with 89 independent significant lead variants, extracted from the 1000 genomes ALL reference panel (Supplementary Table 16) 29 . Figure 3 Gene-based association test, gene set and tissue enrichment analyses It is noted all subsequent analyses were performed using the stage 1 GWAS summary statistics. We conducted a gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of stage 1 VD GWAS meta-analysis summary data using MAGMA 31 . Gene-based association test identified 18 statistically significant genes with the Benjamini-Hochberg FDR-corrected P < 0.05 (Fig. 3 A, Supplementary Table 17). 15 genes are located within the APOE region including APOC1 , APOE , APOC4 , NECTIN2 , TOMM40 , RELB , CEACAM16 , PVR , CLPTM1 , IGSF23 , CEACAM19 , EXOC3L2 , KLC3 , ZNF226 , ZNF227 . 3 genes are located outside the APOE region including CLU , CALCRL , and WNK1 (Supplementary Table 17). In brief, gene-based association test not only verified two GWAS loci APOE and CLU , but also highlighted two additional novel genes CALCRL and WNK1 (Supplementary Table 17). Gene set enrichment analysis identified two statistically significant gene ontology (GO) cellular components with the Benjamini-Hochberg FDR-corrected P < 0.05 including neurofibrillary tangle (GO:0097418, P = 1.24E-05, and FDR = 1.29E-02), and late endosome (GO:0005770, P = 4.42E-05, and FDR = 2.30E-02) (Fig. 3 B, Supplementary Table 18). Meanwhile, 5 GO biological processes including positive regulation of amyloid fibril formation (GO:1905908, P = 5.63E-05, and FDR = 1.96E-01), NMDA glutamate receptor clustering (GO:0097114, P = 2.04E-04, and FDR = 3.19E-01), regulation of amyloid fibril formation (GO:1905906, P = 3.62E-0504, and FDR = 3.88E-01), amyloid-beta clearance by cellular catabolic process (GO:0150094, P = 4.78E-04, and FDR = 4.00E-01), negative regulation of amyloid fibril formation (GO:1905907, P = 5.35E-04, and FDR = 4.00E-01) showed suggestive association with VD (Supplementary Table 18). Tissue enrichment analysis showed evidence of enrichment in GTEx v8 lung ( P = 4.60E-03) and spleen ( P = 1.46E-02), but not in brain tissues or blood ( P = 7.66E-02) (Fig. 3 D, Supplementary Table 19). Genetic association between VD and lung function traits Tissue enrichment analysis showed that the VD heritability was mainly enriched in GTEx v8 lung ( P = 4.60E-03) (Supplementary Table 20). Here, we further investigated the genetic correlation between VD (stage 1) and 6 lung function traits including forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), FEV1/FVC, peak expiratory flow (PEF), asthma and COPD using LDSC (Supplementary Table 20) 18 . We identified that VD showed statistically significant negative genetic correlation with FEV1 ( rg =-0.1695, P = 5.20E-03) and FVC ( rg =-0.1678, P = 3.10E-03) using a Bonferroni-corrected statistical significance threshold of 0.05/6. VD was suggestively associated with PEF ( rg =-0.1528, P = 1.26E-02), and asthma ( rg = 0.1965, P = 4.81E-02) (Supplementary Table 21). These findings showed that impaired lung function was associated with a higher risk of VD. Polygenic Priority Score Polygenic Priority Score (PoPS) is a new method that learns trait-relevant gene features to pinpoint the most likely causal genes at GWAS loci 32 . We computed the polygenic priority score by combining the stage 1 VD GWAS meta-analysis summary statistics with biological pathways, gene expression and protein–protein interaction data 32 . Among 18 statistically significant genes from gene-based association test, APOE , APOC1 , CLU , RELB , WNK1 , PVR , IGSF23 , CALCRL , EXOC3L2 , TOMM40 , CLPTM1 , and KLC3 were ranked the top 10% of the polygenic priority scores (range: 0.37–4.22), underscoring their likely functional relevance and potential roles in the pathogenesis of VD. APOE , CLU , WNK1 , and CALCRL were ranked with the highest, 4th, 42nd and 125th polygenic priority scores, respectively (Fig. 3 A, Supplementary Table 22). Transcriptome-wide association study and colocalization analysis We performed a TWAS to identify the genes whose expression levels are implicated in the pathogenesis of VD 33 . Here, we integrated the stage 1 VD GWAS meta-analysis dataset with the gene expression data from Genotype-Tissue Expression (GTEx) 34 , Young Finns Study (YFS) 35 , and brain eQTLs datasets from CommonMind Consortium (CMC) 36 , respectively. We identified 25 transcriptome-wide significant VD genes with the Benjamini-Hochberg FDR-corrected P < 0.05 including 17 genes around the APOE region and 8 genes outside the APOE region including CLU (novel GWAS locus and novel gene from gene-based association test), WNK1 (novel gene from gene-based association test), SLC17A4 , PARD3 , AFF1 , FBXW8 , WDR27 , and WWOX (Fig. 4 A-B, Supplementary Table 23). These genes exhibited multiple lines of evidence with VD including CLU (2 tissues), WNK1 (2 tissues), SLC17A4 (4 tissues), PARD3 (aorta artery), AFF1 (6 tissues), FBXW8 (24 tissues), WDR27 (41 tissues), and WWOX (24 tissues) (Fig. 4 A-B, Supplementary Table 23). CALCRL , the novel gene from gene-based association test, also showed evidence of association with VD in 9 tissues including aorta artery, coronary artery, tibial artery, cerebellum, tibial nerve, and whole blood. Bayesian colocalization analysis highlighted that CLU , WWOX , and NKPD1 (within the APOE region) showed evidence of colocalization with PPH 4 >0.70 (Fig. 4 A-B, Supplementary Table 24). Figure 4 Summary-data-based Mendelian randomization We conducted a SMR 37 to identify genes putatively causally associated with VD by integrating the stage 1 VD GWAS meta-analysis summary data with multiple eQTLs datasets from GTEx (v8 54 tissues) 34 , eQTLGen (whole blood) 38 , BrainMeta v2 (brain) 39 , and brain single-nucleus eQTLs datasets 40 . Using bulk tissue eQTLs datasets, we revealed 3 statistically significant genes including APOC4 , APOC1 , and CLU with the Benjamini-Hochberg FDR-corrected P 0.01 (Fig. 4 C-D, Supplementary Table 25). Collectively, both SMR and TWAS provide consistent findings about the involvement of APOE and CLU in VD. Using single-nucleus eQTLs datasets, SMR highlighted PICALM as the only statistically significant gene with Benjamini-Hochberg FDR-corrected P < 0.05 in microglia. In brief, genetically increased PICALM expression in microglia was associated with significantly decreased risk of VD with beta=-0.18, P SMR =4.52E-05, FDR = 0.03, and P HEIDI =0.87 (Fig. 4 E, Supplementary Table 26). Meanwhile, PICALM was suggestively associated with the risk of VD in oligodendrocyte (beta = 0.34, P SMR =8.25E-04, and P HEIDI =0.14) and excitatory neuron (beta=-0.63, P SMR =5.74E-03, and P HEIDI =0.52) (Fig. 4 E, Supplementary Table 26). Figure 5 Cross-trait meta-analysis of VD and AD (stage 3) We first estimated the genetic correlation between VD (stage 1) and AD (European) 22 using LDSC 18 , and found a statistically significant positive genetic correlation with r g = 0.4469 and P = 3.75E-14, which suggested that it was appropriate and reliable to combine both GWAS datasets. We further conducted a cross-trait meta-analysis of VD (stage 1) and AD GWAS datasets using the fixed-effects IVW method implemented in METAL 17 , and identified 13 independent genome-wide significant loci including CR1 , BIN1 , GRM7 , HLA-DRA , TREM2 , CLU , ECHDC3 , AGBL2 , MS4A4E , PICALM , SLC24A4 , ABCA7 , and APOE (Fig. 5 A-C, Supplementary Table 27). Subsequently, we performed a sensitivity cross-trait meta-analysis of AD and VD GWAS datasets using multi-trait analysis of GWAS (MTAG) 41 . MTAG identified 10 genome-wide significant loci for AD and 11 loci for VD, of which 10 loci were shared between the two traits. Interestingly, all these 10 shared loci were known loci identified using METAL, further confirming the robustness and pleiotropic nature of these genetic signals (Fig. 5 D, Supplementary Table 28). Using FUMA 20 , we further delineated 213 independent genetic variants ( r 2 < 0.6; Supplementary Table 29) and 72 independent lead variants ( r 2 < 0.1; Supplementary Table 30), and annotated 2,213 SNPs in LD with the 213 independent significant lead variants across these loci. Through positional mapping, eQTL mapping, and chromatin‑interaction mapping, we ultimately identified 353 candidate risk genes (Supplementary Tables 31–33). We conducted the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of the cross-trait GWAS meta-analysis summary data using MAGMA 31 . Gene-based association test identified 127 statistically significant genes with the Benjamini-Hochberg FDR-corrected P < 0.05 (Fig. 3 A, Supplementary Table 34). 44 of these 127 genes were ranked within the top 10% of polygenic priority scores (range: 0.37–4.22), indicating their high posterior probability of being functionally relevant in VD (Fig. 3 A, Supplementary Table 35) 32 . APOE and CLU were ranked with the highest and 4th polygenic priority scores, respectively (Supplementary Table 35). Gene set enrichment analysis identified 49 statistically significant GO pathways with the Benjamini-Hochberg FDR-corrected P < 0.05 including neurofibrillary tangle (GO:0097418, P = 5.99E-11, and FDR = 1.02E-06), negative regulation of endopeptidase activity (GO:0010951), autophagic cell death (GO:0048102), positive regulation of complement activation (GO:0045917), regulation of antigen processing and presentation of peptide or polysaccharide antigen via MHC class II (GO:0002580), negative regulation of amyloid precursor protein catabolic process (GO:1902992), amyloid-beta clearance by cellular catabolic process (GO:0150094, P = 1.31E-06, and FDR = 2.69E-03), positive regulation of amyloid-beta clearance (GO:1900223), positive regulation of high-density lipoprotein particle clearance (GO:0010983), and regulation of amyloid fibril formation (GO:1905906, P = 2.18E-06, and FDR = 3.24E-03), as the top 10 significant signals (Fig. 3 C, Supplementary Table 36). Tissue enrichment analysis identified evidence of enrichment of VD heritability in GTEx v8 liver ( P = 1.08E-04), blood ( P = 7.56E-03), lung ( P = 2.68E-02), spleen ( P = 3.23E-02), and small intestine ( P = 4.54E-02), but not in human brain tissues (Fig. 3 D, Supplementary Table 37). Gene prioritization To identify the potential causal genes, we integrated evidence from 29 complementary approaches: GWAS significance ( P < 5.00E-08), gene mapping (position mapping, chromatin interaction mapping, or eQTLs mapping), variant annotation (exonic SNPs, CADD/RDB/pLI), gene-based association test ( P FDR <0.05), PoPS (top 10%), TWAS ( P FDR 0.70), SMR (eQTLs/sc-QTLs, P FDR 0.5 and P FDR <0.05). We calculated the priority scores ranging from 1 to 29 (Supplementary Table 38). Finally, 14 of the 619 protein-coding genes were supported by at least 15 lines of evidence, including CLU and other 13 genes around the APOE region (Fig. 6 ). Figure 6 Differential gene expression analysis in human brain cells We identified 619 protein-coding genes using VD GWAS (stage 1 and stage 2), gene mapping (stage 1 and stage 2), gene-based association test (stage 1), TWAS (stage 1), SMR (stage 1), cross-trait GWAS meta-analysis, gene mapping (stage 3), and gene-based association test (stage 3) (Supplementary Table 38). To investigate the differential expression of VD genes in human brain cells, we analyzed the single-nucleus RNA-sequencing (snRNA-seq) data in human periventricular white matter from 5 VD patients with lesion, 5 VD patients adjacent to the lesion, and 5 normal control subjects (GEO accession: GSE213897, Methods) 42 , 4 VD patients and 4 age and sex-matched healthy controls (GEO accession: GSE282111, Methods) 43 . All single cells in GSE282111 were classified into 30 clusters and 6 primary cell types astrocytes, endothelial cells, microglia, neurons, oligodendrocytes and oligodendrocyte precursor cells (OPCs) (Fig. 7 A-B). 89 and 201 genes including 49 shared genes exhibited significantly differential expression in VD patients compared to normal controls in at least one cell type with |Log 2 fold change (FC)|>0.5 and P FDR <0.05 in GSE213897 and GSE282111, respectively (Fig. 7 C-D, Supplementary Tables 39–40). Briefly, we found significantly differential expression of 24 VD GWAS loci ( DPP6 , ITFG1 , RCSD1 , CHD6 , PTPRT , CLU , HPSE2, ADAMTS17 , ATP11A , TBC1D22A , TBC1D4 , ADAMTSL1 , ADGRV1 , APOE , CDH18 , PSAT1 , HERC3 , ATP1A1 , CRELD1 , COL12A1 , HS6ST1 , MROH8 , TMEM87A , ATP10A ) and 9 cross-trait GWAS loci ( SLC24A4 , CD2AP , PICALM , CLU , HLA-DRA , APOE , BIN1 , ABCA7 , TREM2 , MS4A4E ) (Fig. 7 E-Q, Supplementary Tables 39–40, Supplementary Fig. 4). Meanwhile, CALCRL from gene-based association test, WWOX , PARD3 , AFF1 and FBXW8 from TWAS also showed significantly differential expression. Figure 7 Drug-gene interaction analysis To investigate whether these significantly differentially expressed genes are potential therapeutic targets, we examined the interactions between these genes and known drugs using the Drug-Gene Interaction Database 5.0 (DGIdb, https://dgidb.org ) 44 . Among 241 significantly differentially expressed genes, 21 showed strong evidence of interaction with U.S. Food and Drug Administration (FDA) approved drugs with interaction score > 1, which highlighted the potential clinical utility of these genes (Supplementary Table 41). CDH18 , the VD GWAS significant locus, indicated the strongest interaction (interaction score = 26.25) with Thiamine (vitamin B1). ATP1A1 , the VD GWAS significant locus, indicated the strong interaction with Almitrine (interaction score = 10.50). HSD3B1 , the VD GWAS significant locus, indicated the strong interaction with Trilostane (interaction score = 8.75). NT5E , the VD GWAS significant locus, indicated the strong interaction with Tinidazole (interaction score = 5.53), which is used to treat infections caused by protozoa. ATP10A , the VD GWAS significant locus, indicated the strong interaction with Duloxetine (interaction score = 20.2), which is used to treat depression and anxiety. SLC24A4 , the cross-trait GWAS significant locus, indicated the strong interaction with salbutamol (interaction score = 8.08), which was approved to treat asthma and chronic obstructive pulmonary disease (COPD). Discussion Until now, APOE is the only genome-wide significant locus identified by recent VD GWAS from MEGAVCID consortium 7 . Here, we performed the largest VD GWAS meta-analysis to date in 1,033,769 individuals including 5,886 VD patients and 1,027,883 controls from five ancestral populations: European, East Asian, South Asian, African, and Admixed American. We identified 37 independent genome-wide significant loci including APOE and CLU tagged by common variants and 35 loci tagged by rare variants, which explained 53% of VD variance. Gene-based association test, TWAS, SMR and gene set enrichment analysis identified statistically significant 18, 25, 4 genes and 2 pathways, respectively. Cross-trait meta-analysis of VD and AD identified 13 independent genome-wide significant loci, 127 statistically significant genes, 49 statistically significant pathways. These findings provide novel insights into the genetic basis of VD and new leads for the molecular mechanisms underlying VD. Until now, the exact roles of APOE and CLU in VD remain unclear, however their roles have been investigated in AD. APOE is a well-known genome-wide significant locus associated with multiple dementias including AD 22 , FTD 23 , and LBD 24 , 45 , 46 . CLU , which codes clusterin protein, was a known genome-wide significant locus for AD 22 . It is known that VD is caused by reduced blood flow to the brain, which damages and eventually kills brain cells 2 . Loss of clusterin shifts amyloid deposition to the cerebrovasculature, and promotes cerebrovascular cerebral amyloid angiopathy (CAA), which is a neurological condition where amyloid-beta protein deposits in the walls of cerebral blood vessels 47 , 48 . CLU ameliorates diabetic atherosclerosis by inhibiting the release of inflammatory factors and macrophage pyroptosis 49 . In addition to APOE and CLU , we identified CALCRL and WNK1 as two additional novel VD genes using gene-based association test. CALCRL (calcitonin receptor like receptor) is a major G-protein-coupled neuropeptide receptor for both adrenomedullin and calcitonin gene-related peptide (CGRP), which contribute to widen blood vessels, allowing more blood to flow through 50 . TWAS revealed that genetically decreased arterial expression of CALCRL and higher abundance in blood were associated with increased white matter hyperintensities (WMH), a MRI marker of cerebral small vessel disease (CSVD) that is the most common pathology underlying VD 51,52 . CALCRL was a known genome-wide significant locus for ischemic stroke 53 . Mendelian randomization showed that higher expression of CALCRL in the brain tissues was linked to larger WMH burden and AD risk 54 . WNK1 plays an important role in regulating blood pressure and vasoconstriction 55 , 56 . Inactivation of mouse Wnk1 in mature neurons leads to axon degeneration in the adult brain, and WNK1 may have neuroprotective role in kinds of neurodegenerative diseases 57 . PARD3 , TWAS significant gene in aorta artery, regulates the trafficking and processing of amyloid precursor protein 58 . Loss of Par3 promotes dendritic spine neoteny and enhances learning and memory 59 . Hippocampal atrophy is a recognized biological marker of AD 60 . FBXW8 , TWAS significant gene in 24 tissues, is a known genome-wide significant locus for hippocampal volume 60 . WWOX , TWAS significant gene in 24 tissues, is a known genome-wide significant locus for AD 61 . WDR27 , TWAS significant gene in 41 tissues, is associated with a higher genetic risk for AD and related dementia 62 . The combination of WDR27 variants UNC93A variants can impair the function of the neurovascular unit (which includes brain blood vessels) and contribute to the development of dementia 62 . ATP1A1 , a novel VD GWAS significant locus tagged by rare variant, is associated with thrombosis and hypertension. ATP1A1 haplodeficiency or inhibition significantly inhibited thrombosis and sensitized clopidogrel’s anti-thrombotic effect 63 . ATP1A1 somatic mutations can lead to aldosterone-producing adenomas, causing excess aldosterone and high blood pressure 64 . ATP1A1 genetic variant was associated with essential hypertension 65 . An ATP1A1 -related long non-coding RNA, ATP1A1-AS1 , is implicated in the development of intracranial aneurysms by promoting smooth muscle cells phenotype switching and apoptosis 66 . CDH18 , the VD GWAS significant locus tagged by rare variant, regulates differentiation towards vascular smooth muscle cells 67 , and shows the most significantly upregulated protein expression in the good prognosis group of moyamoya disease, a rare cerebrovascular disorder 68 . ATP10A , the novel VD GWAS significant locus tagged by rare variant, showed a positive association with TDP-43 protein level, the accumulation of which in the central nervous system is a hallmark of frontotemporal lobar degeneration and amyotrophic lateral sclerosis 69 . ATP10A indicated lower expression in AD endothelial cells 70 . Gene-set enrichment analysis of VD GWAS highlighted the involvement of neurofibrillary tangle (GO:0097418), late endosome (GO:0005770), positive regulation of amyloid fibril formation (GO:1905908), NMDA glutamate receptor clustering (GO:0097114), regulation of amyloid fibril formation (GO:1905906), amyloid-beta clearance by cellular catabolic process (GO:0150094), negative regulation of amyloid fibril formation (GO:1905907). It is known that AD is characterised by both amyloid-β plaques and neurofibrillary tangles, which suggests that VD may coexist with AD. Interestingly, our genetic correlation analysis supported the statistically significant positive genetic correlation between VD and AD with rg = 0.4469 and P = 3.75E-14. In fact, population study demonstrated that amyloid-β plaques and neurofibrillary tangles have the strongest association with dementia including AD, VD, or both 71 , and midlife vascular risk factors was significantly associated with later-life elevated amyloid-β plaques 72 . Meanwhile, endosome dysfunction was involved in AD and other neurodegenerative diseases, and targeting endosome may be a strategy for treatment 73 . We further conducted a cross-trait GWAs meta-analysis of VD and AD, and identified 13 loci, 127 genes, and 53 pathways. It is noted that GRM7 is the only novel cross-trait GWAS locus that was not previously reported to be associated with VD or AD 22 . The subcellular-resolution spatial transcriptome atlas of the human prefrontal cortex revealed the increase of GRM7 expression in the severe AD group compared to the moderate AD group 74 . Biallelic GRM7 variants cause epilepsy, microcephaly, and cerebral atrophy 75 . GRM7 prevents glutamate release from pre-synaptic vesicles 76 . GRM7 variants predict the risk of schizophrenia and antipsychotic effect of seven common drugs 77 . Using scRNA-seq data, we demonstrated significantly differential expression of 241 VD genes including 24 VD GWAS loci and 9 cross-trait GWAS loci especially CLU , CDH18 , ATP1A1 , and SLC24A4 . Interestingly, evidence supported the dysregulation of these genes in other neurological diseases. CLU was significantly downregulated in dementia with Lewy bodies (DLB) microglia (Log 2 FC=-3.76 and P FDR =2.87E-08), and OPC (Log 2 FC=-1.42 and P FDR =3.08E-02), downregulated in Parkinson’s disease with dementia (PDD) excitatory neuron (Log 2 FC=-1.34 and P FDR =1.30E-27), microglia (Log 2 FC=-1.36 and P FDR =4.12E-02), and OPC (Log 2 FC=-1.16 and P FDR =7.60E-04), upregulated in Parkinson’s disease (PD) excitatory neuron (Log 2 FC = 0.91 and P FDR =5.85E-116), inhibitory neuron (Log 2 FC = 0.75 and P FDR =8.03E-24), oligodendrocyte (Log 2 FC = 1.15 and P FDR =3.86E-29) 78 , dopaminergic neuron (Log 2 FC=-1.58 and P FDR =1.12E-03), OPC (Log 2 FC=-1.12 and P FDR =3.40.E-02), and pericyte (Log 2 FC=-4.57 and P FDR =1.08.E-10) 79 . CDH18 was significantly upregulated in DLB excitatory neuron (Log 2 FC = 1.37 and P FDR =3.26E-08), and PDD excitatory neuron (Log 2 FC = 0.94 and P FDR =3.00E-09), but downregulated in PD excitatory neuron (Log 2 FC=-0.96 and P FDR =8.64E-15) 78 . ATP1A1 was significantly downregulated in DLB (astrocyte, excitatory neuron, inhibitory neuron, microglia, oligodendrocyte, OPC, vascular), PDD (astrocyte, excitatory neuron) 78 , and dopaminergic neuron (Log 2 FC=-2.6 and P FDR =1.49.E-02) 79 . SLC24A4 was significantly downregulated in DLB excitatory neuron (Log 2 FC=-0.66 and P FDR =4.20E-04) 78 . Meanwhile APOE was significantly upregulated in dementia with DLB astrocyte (Log 2 FC = 1.12 and P FDR =2.42E-38) 78 , and PD microglia (Log 2 FC = 1.748 and P FDR =2.76.E-02) 79 . Drug-gene interaction analysis highlights APOE , CDH18 , ATP1A1 , HSD3B1 , NT5E , ATP10A , and SLC24A4 , as the potential therapeutic targets for VD (Supplementary Table 13). APOE is the target of 37 drugs especially FDA approved drugs for the treatment of AD including lecanemab 80 , donepezil 81 , rivastigmine 82 , and galantamine 83 . Importantly, randomized controlled trials (RCTs) demonstrated that donepezil and galantamine effectively improved cognition in VD patients with good safety and tolerability 84 – 86 . CDH18 indicated the strongest interaction (interaction score = 26.25) with thiamine (vitamin B1). Two cross-sectional observational studies using data from the National Health and Nutrition Examination Survey (NHANES) showed that the increase in dietary intake of vitamin B1 contribute to better cognitive function in individuals aged over 60, and a decreased risk of stroke in older individuals 87 , 88 . In cognitively healthy and older Chinese individuals, there is a J-shaped association between dietary vitamin B1 intake and cognitive decline 89 . These findings highlight the potential importance of adequate dietary vitamin B1 intake to prevent cognitive decline and stroke in the aging population. ATP1A1 shows the strong interaction with almitrine (interaction score = 10.50), which was approved to treat chronic obstructive lung disease. A meta-analysis of three RCTs showed that Duxil (a combination of almitrine and raubasine) significantly improved the cognitive function in VD patients measured by MMSE 90 . ATP10A showed a strong interaction with Duloxetine (interaction score = 20.2), a type of antidepressant medicine to treat depression and anxiety 91 . Recent study identified duloxetine as a highly potent selective competitive inhibitor of butyrylcholinesterase, which was involved in the regulation of the nervous system that affects memory and cognition, and may have positive effects on memory and cognitive functions in the elderly 91 . A RCT demonstrated that duloxetine was effective to treat patients with moderate to severe central post-stroke pain 92 . SLC24A4 indicated the strong interaction with salbutamol (interaction score = 8.08), which was approved to treat asthma and COPD. Interestingly, salbutamol is effective at reducing the accumulation and rate of the tau protein formation 93 . Our current findings broaden the potential therapeutic scope of the available drugs, and may offer potential as a new treatment for VD. Tissue enrichment analysis showed evidence of enrichment of VD heritability in lung and spleen, and VD + AD heritability in liver, blood, lung, spleen, and small intestine. In fact, large-scale GWAS showed that AD heritability was enriched in whole blood, spleen and lung 94 . Population-based cohort studies from the Atherosclerosis Risk in Communities (ARIC) study 95 – 97 , the Rotterdam study 98 , the Rush Memory and Aging Project (MAP) 99 , Swedish National Study on Aging and Care in Kungsholmen (SNAC-K, 2001–2004 to 2016–2019) 100 , and Health and Retirement study 101 collectively demonstrated that poor lung function increased the risk of both mild cognitive impairment (MCI) and dementia, accelerated progression from MCI to dementia, associated with AD pathology and cerebral vascular disease pathology, brain microvascular damage and global brain atrophy. Impaired lung function, defined as peak expiratory flow < 80% predicted, was associated with a higher risk of dementia (HR = 1.74, 95% CI:1.34–2.25) 101 . Compared to those without impaired lung function, individuals with impaired lung function had 0.10 SD higher NfL and 0.09 SD higher p-Tau 181, which mediated 7.3% and 5% of the total effect of impaired lung function on dementia 101 . A prospective cohort study of 431,834 non-demented individuals from the UK Biobank indicated that lung function decrease was associated with increased risk for all-cause dementia, AD and VD 102 . Together, our large-scale GWAS meta-analysis and integrative analysis uncovered novel VD loci, genes and pathways. Our current findings demonstrated that the use of both common and rare genetic variants in large-scale VD GWAS could (1) enhance the ability to identify new loci, (2) identify rare variants of large effects, and (3) increase the proportion of VD heritability. These genetic findings provide valuable insights into the potential underlying mechanisms of VD and inform some potential clinically actionable drugs for the treatment of VD, which deserve further investigation. Materials and Methods VD GWAS datasets We selected four independent GWAS datasets from UKBB (European, 2,074 VD and 456,366 controls) 15 , FinnGen R12 (European, 3,624 VD and 475,484 controls) 16 , GH (South Asian, 119 VD and 43,659 controls) 26 , and MGBB (European, South Asian, African, and Admixed American, 69 VD and 52,374 controls) 27 . UKBB cohort represents a population-based, longitudinal study of over 500,000 volunteers aged 40–70 years, recruited from England, Scotland, and Wales between 2006 and 2010 103 . Extensive participant data were gathered through surveys, interviews, physiological assessments, and genetic profiling 103 . VD GWAS included 2,074 VD and 456,366 controls of non-Finnish European ancestry about 16,477,695 variants with position information from the Genome Reference Consortium Human Build 37 (GRCh37) 15 . VD were diagnosed using the available medical records, which were ascertained from Hospital Episode Statistics and recorded as International Classification of Disease version 10 (ICD-10) codes 15 . FinnGen study encompasses six regional and three nationwide Finnish biobanks, with participants’ health outcomes meticulously tracked through linkages to the comprehensive national health registries, spanning from birth to death 16 . Using ICD-9 and ICD-10 codes, 3,624 individuals were identified as VD cases, and 475,484 individuals were identified as controls in FinnGen R12 16 . FinnGen VD GWAS included 21,306,039 genetic variants with position information from GRCh38 16 . Here, we converted the position information from GRCh38 to GRCh37. After a rigorous quality control process to eliminate mismatched variants between GRCh38 and GRCh37, we obtained 13,092,808 genetic variants for meta-analysis. GH is a community-based population genomics and health study comprising about 50,000 British individuals of South Asian ancestry (British Bangladeshi and British Pakistani) recruited in the United Kingdom including East London and Bradford 26 . GH identified 119 VD cases and 43,659 controls using ICD-10, and included 37,686,810 genetic variants with position information from GRCh38 26 . MGBB was established based on Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. MGBB currently included 142,238 participants from European, South Asian, African, and Admixed American ancestries with 69 VD and 52,305 controls 27 . GWAS meta-analysis GWAS meta-analysis was performed using the fixed-effects IVW implemented by METAL, weighted by effect size and standard error (SE), with genomic control correction 17 . In Stage 1, a GWAS meta-analysis was performed in participants of European ancestry from UKBB and FinnGen R12. In stage 2, a GWAS meta-analysis was performed in participants of European, South Asian, African, and Admixed American ancestries from UKBB, FinnGen R12, GH and MGBB. In addition to METAL, a sensitivity GWAS meta-analysis using GWAMA (v2.2.2) was performed 21 . To assess the genomic inflation, we calculated the genomic inflation factor (λ GC ) using LDSC (v1.0.1) 18 . We defined the statistically significant SNPs using the genome-wide significant threshold ( P < 5.00E-08). Identification of genetic risk loci To identify genetic risk loci from the GWAS meta‑analysis and perform functional annotation, we used FUMA v1.5.2 with ancestry-specific reference panels: the 1000 Genomes Phase 3 European panel for stage 1 European-specific GWAS meta-analysis, and the 1000 Genomes Phase 3 ALL panel for stage 2 cross-ancestry GWAS meta-analysis 20 . Initially, FUMA identified SNPs with a significance level of P < 5.00E-08 and LD threshold r 2 < 0.6 as the independent significant SNPs using LD-based clumping 20 . The independent genetic risk loci were characterized by considering all SNPs in LD ( r 2 ≥ 0.6) with one of the independent significant SNPs within a region of 250 kilobase (kb) 20 . Within each genetic risk locus, FUMA further distinguished the lead SNPs, which are a subset of the independent significant SNPs in LD with each other ( r 2 < 0.1) 20 . Each locus was represented by the lead SNP with the most significant P value. Genetic loci were classified as novel if they were beyond a 1,000 kb from previously recognized loci 20 . The nearest gene corresponding to the lead SNPs in each locus was annotated using the get_nearest_gene () function from the gwasRtools package ( https://github.com/lcpilling/gwasRtools ). Conditional analysis To identify independent genetic signals at each genomic locus, we performed a step-wise conditional analysis of the VD GWAS meta-analysis summary statistics using GCTA (v 1.94.1) COJO analysis with the --cojo-slct function 25 . The parameters set for this function included a significance threshold of P = 5.00E-08, a distance of 10,000 kb, and a co-linearity threshold of 0.9. LD information was from the 1000 Genomes European reference panel 25 . Heritability analysis We used LDSC (v1.0.1) to calculate the SNP-based heritability of VD GWAS meta-analysis summary statistics 18 . LDSC is a computationally efficient tool that leverages GWAS summary statistics to estimate the SNP-based heritability and genetic correlation among multiple genetic traits, while accounting for potential sample overlap 18 . The SNP-based heritability measures the proportion of phenotypic variance explained by the additive effects of all common SNPs (i.e, the proportion of variance in disease liability due to genetic factors) 18 . LD scores are from the 1000 genomes phase 3 European reference panel ( https://data.broadinstitute.org/alkesgroup/ ) using a population prevalence estimate 1.16% 19 . Functional annotation FUMA offers a comprehensive annotation framework by integrating diverse external data sources including ANNOVAR 28 , CADD 30 , RegulomeDB 104 and 15-core chromatin state 105 , 106 . Here, we conducted a functional annotation of all genome-wide significant SNPs (or independent significant SNPs) and their tagged SNPs with LD r 2 ≥ 0.6 using FUMA v1.5.2 and 1000 Genomes European reference panel 20 . Gene mapping We assigned the genome-wide significant loci to specific genes using positional mapping, eQTLs mapping, and chromatin interaction mapping implemented in FUMA v1.5.2 20 . Positional mapping pinpointed protein-coding genes located within a 10 kb range of significant SNPs (either genome-wide significant or independently significant) 20 . eQTLs mapping assigned the significant SNPs to their corresponding protein-coding genes using significant eQTLs ( P FDR <0.05) 20 . eQTLs datasets are from the eQTLGen 38 , Blood eQTL 107 , BIOS QTL 108 , GTEx v8 109 , PsychENCODE 110 , xQTLServer 36 , CMC 111 , eQTL catalogue 112 and BRAINEAC 113 . Gene-based association test, gene set and tissue enrichment analyses We performed the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of stage 1 VD GWAS meta-analysis summary data using MAGMA v1.08 31 . MAGMA aggregates SNP-level association statistics into gene scores by mapping all SNPs from VD GWAS meta-analysis to 17,903 protein-coding genes using the SNP-wise mean model, genomic location and boundary information from human reference genome build 37, and the ancestry-matched LD information from the 1000 Genomes Project phase 3 reference panel 31 . Finally, a gene-based association score was calculated by the aggregate of all SNPs inside each gene 31 . MAGMA performed a gene set enrichment analysis through competitive analysis to identify the genes in a gene set that are more strongly associated with the phenotype of interest than other genes 31 . Here, we focused on 16,228 GO terms including biological processes, cellular components and molecular functions from the Molecular Signatures Database (MSigDB) (v7.0, version 2025.1.Hs) 114 . MAGMA determined whether VD heritability is enriched in specific tissues by integrating the stage 1 VD GWAS meta-analysis summary data with gene expression data from 30 GTEx v8 tissues 115 . BH-adjusted P < 0.05 was considered statistically significant for gene-based association test, gene set enrichment analysis and tissue enrichment analysis. Genetic association between VD and lung function traits Using LDSC default parameters and the precomputed LD scores from the 1000 Genomes European reference panel 18 , we performed a genetic association analysis to investigate the genetic correlation between VD (stage 1) and 6 lung function traits including FEV1, FVC, FEV1/FVC, PEF, asthma and COPD using large scale GWAS summary datasets from UKBB 15 , as provided in Supplementary Table 18. We defined the statistically significant genetic association using the Bonferroni-corrected threshold P < 8.33E-03 (0.05/6). Polygenic Priority Score To pinpoint the most likely causal genes at VD GWAS loci, we calculated the polygenic priority score using PoPS (v.0.2) and the gene-level results from MAGMA analysis of stage 1 VD GWAS meta-analysis summary statistics 32 . PoPS computes gene-level z-scores from GWAS summary statistics with an LD reference panel using MAGMA, and learns trait-relevant gene features from cell-type specific gene expression, biological pathways, and protein-protein interactions 32 . In each genome-wide significant locus, genes within ± 1 Mb of the lead variant were assigned a polygenic priority score. Within each region, genes ranking in the top 10% of the polygenic priority scores indicate a higher probability of being causal for VD. Transcriptome-wide association study We performed a TWAS to identify the genes whose expression levels are significantly associated with VD using FUSION v3 33 . TWAS leverages the correlation between genotype and expression to identify eQTLs that modulate gene expression and are associated with the phenotype of interest 33 . Using FUSION v3, we integrated the VD GWAS meta-analysis dataset with the gene expression data from GTEx v8 in human tissues and cell types, a whole blood eQTLs dataset from YFS ( N = 1,264) 35 and a brain dorsolateral prefrontal cortex eQTLs dataset from CMC ( N = 452) 111 . A Benjamini & Hochberg FDR-corrected threshold of 0.05 was considered statistically significant for each dataset ( P FDR <0.05). Colocalization analysis Using COLOC implemented in FUSION v3, we performed a Bayesian colocalization analysis to identify a subset of TWAS significant genes that had the same single variant associated with both VD and gene expression with a high posterior probability (PP) 116 . Basically, COLOC calculated five kinds of PPs 116 . The PP for the null hypothesis (PP.H 0 ): neither trait has a genetic association in the region, PP for the first alternative hypothesis (PP.H 1 ): only trait 1 has a genetic association in the region, PP for the second alternative hypothesis (PP.H 2 ): only trait 2 has a genetic association in the region, PP for the third alternative hypothesis (PP.H 3 ): both traits are associated, but with different causal variants, PP for the fourth alternative hypothesis (PP.H 4 ): both traits are associated and share a single causal variant 116 . A PP.H 4 > 0.70 indicated evidence of colocalization 117 . Summary-data-based Mendelian randomization SMR is a complementary method of TWAS to verify the causal role of TWAS genes 37 . Here, we used SMR v1.3.1 to integrate the VD GWAS meta-analysis dataset with multiple large-scale eQTLs datasets from GTEx tissues 34 , a large blood eQTLs dataset from the eQTLGen consortium including 31,684 human blood samples 38 , and a brain eQTLs dataset including 2,865 human brain cortex samples 39 . We defined the statistically significant SMR genes using P FDR 0.01 118 . Cross-trait meta-analysis of VD and AD We selected the largest clinically diagnosed AD GWAS in individuals of European ancestry from the International Genomics of Alzheimer’s Project (IGAP) stage 1 ( n = 63,926) including a total of 9,456,058 common variants and 2,024,574 rare variants 22 . Using LDSC default parameters and the precomputed LD scores from the 1000 Genomes European reference panel 18 , we first estimated the genetic correlation between VD (stage 1) and AD 22 . We further conducted a cross-trait meta-analysis of VD and AD GWAS datasets using a fixed-effects IVW method implemented by METAL 17 . Meanwhile, we performed a sensitivity cross-trait meta-analysis of AD and VD GWAS datasets using MTAG (1.0.8) 41 . MTAG extends the standard single-trait GWAS by jointly analyzing multiple genetically related traits, thereby increasing the statistical power to detect pleiotropic loci while accounting for sample overlap 41 . To identify robust cross-trait associations, our analysis focused on SNPs exhibiting: (i) concordant effect directions (identical risk alleles) across both diseases; (ii) a meta-analysis P -value < 5.00E-08, reflecting association with the cross-trait phenotype; and (iii) a single-trait P -value < 0.05 for each trait independently. Independent genomic loci were then defined using FUMA. We also conducted the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of the cross-trait GWAS meta-analysis summary data using MAGMA 31 . Gene prioritization To prioritize the most probable causative genes for VD, we integrated 29 lines of evidence including GWAS significance ( P < 5.00E-08), gene mapping (position mapping, eQTLs mapping, and chromatin interaction mapping), variant annotation (exonic SNPs, CADD/RDB/pLI), gene level association test (MAGMA, P FDR <0.05), PoPS (top 10%), TWAS ( P FDR 0.70), SMR (eQTLs/sc-eQTLs, P FDR <0.05). For each gene, the prioritization score is the sum of multiple lines of evidence by counting “0” if the gene was ‘not prioritized’ and as “1” if the gene was ‘prioritized’. This approach ensured that genes with stronger cumulative evidence were more likely to be causally associated with VD, and reflected a higher degree of confidence in the VD etiology. Differential gene expression analysis in human brain cells We performed a differential gene expression analysis of VD risk genes using snRNA-seq data in human periventricular white matter from 5 VD patients with lesion, 5 VD patients adjacent to the lesion, and 5 normal control subjects (GEO accession: GSE213897) 42 , 4 VD patients and 4 age and sex-matched healthy controls (GEO accession: GSE282111) 43 . Using “Seurat” package, we preprocessed and transformed the raw scRNA-seq data, excluding cells with fewer than three genes and fewer than 50 unique features counted per cell. Subsequently, we used the NormalizeData and ScaleData functions to normalize and scale the RNA transcripts per million (TPM). Using “SingleR” package, we annotated the cell types, which were classified into 6 primary cell types including astrocytes, endothelial cells, microglia, neurons, oligodendrocytes and oligodendrocyte precursor cells (OPCs), as well as 30 clusters 42 . Finally, we conducted the differential expression analysis using Wilcoxon rank sum test. In a specific cell type, we defined the significantly differential expression in VD patients compared to normal controls with |Log 2 FC|>0.5 and P FDR <0.05. Drug-gene interaction analysis To assess whether VD risk genes could serve as potential therapeutic targets, we performed a drug-gene interaction analysis using DGIdb v5.0 44 . DGIdb v5.0 is an online database that integrates information from drug–gene interaction databases (accessed December 2024) 44 . DGIdb contains over 10,000 genes and 20,000 drugs involved in nearly 70,000 drug-gene interactions or belonging to one of 43 potentially druggable gene categories 44 . A interaction score is used to rank results in an interaction search result set 44 . Declarations Data availability All relevant data underlying the findings are fully available without restriction. GWAS summary statistics of UK Biobank is available at https://www.ebi.ac.uk/gwas (GCST90473240); GWAS summary statistics of FinnGen is available at https://r12.finngen.fi/; The Mass General Brigham Biobank (MGBB) GWAS summary statistics is available at https://api.kpndataregistry.org/api/d/BPEif3; Genes & Health (GH) GWAS summary statistics is available at https://www.genesandhealth.org/research/gwas-data-downloads; The 1000 Genomes Phase 3 ancestry-specific LD reference are obtained from the FUMA website at https://fuma.ctglab.nl; TWAS weights are available at http://gusevlab.org/projects/fusion/; eQTL summary datasets used for SMR are available at https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata; Single cell gene expression datasets are available at GSE213897 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213897) and GSE282111 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282111). AD GWAS summary statistics is available at https://www.niagads.org/igap-rv-summary-stats-kunkle-p-value-data, GWAS summary statistics of forced expiratory volume in 1-second (FEV1) is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/continuous-20150-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of forced vital capacity (FVC) is available at https://pan-ukb-us-east-1.s3.amazonaws.com/ sumstats_flat_files_tabix/continuous-20151-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of FEV1/FVC is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/continuous-FEV1FVC-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of peak expiratory flow (PEF) is available at https://pan-ukb-us-east-1.s3.amazonaws.com/ sumstats_flat_files_tabix/continuous-3064-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of asthma is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/phecode-495-both_sexes.tsv.bgz.tbi, GWAS summary statistics of COPD is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/categorical-22130-both_sexes-22130.tsv.bgz.tbi. Code availability No custom code was used in this study. As detailed in the Methods section, we used previously released software to generate the analysis and cite them throughout the manuscript references. METAL (March 25, 2011 release) is available at https://csg.sph.umich.edu/abecasis/Metal/; GWAMA (v2.2.2) is available at https://genomics.ut.ee/en/tools; FUMA (1.5.2) is available at https://fuma.ctglab.nl/; LocusZoom is available at http://locuszoom.sph.umich.edu/; MTAG (v1.0.8) is available at https://github-wiki-see.page/m/JonJala/mtag/; GCTA-COJO (v1.94.1) is available at https://yanglab.westlake.edu.cn/software/gcta/; MAGMA (v1.08) is available at https://cncr.nl/research/magma/; LDSC is available at https:// github.com/bulik/ldsc; PoPS (v0.2) is available at https://github. com/FinucaneLab/pops; FUSION (v3) is available at http://gusevlab.org/projects/fusion/; SMR (v1.3.1) is available at https://yanglab.westlake.edu.cn/software/smr/; DGIdb (v5.0): https://beta.dgidb.org/. Ethics approval and consent to participate This article involves human individuals from prior investigations. All individuals provided informed consent in all of the corresponding original investigations, as reported in the Materials and methods. Our analysis is based on publicly available, large-scale datasets rather than individual-level data. Thus, ethical approval was not sought. Consent for publication Not applicable. Availability of data and materials All relevant data are within the paper. The authors confirm that all data underlying the findings are either fully available without restriction through consortia websites, or may be made available from consortia upon request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by funding from the National Key Research and Development Program of China (Grant No. 2023YFC3605200), Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0505300, 2023ZD0505302), National Natural Science Foundation of China (Grant No. 82471449). Authors’ contributions The authors declare no competing interests. G.Y.L. conceived and initiated the project. G.Y.L. and G.S analyzed the data and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. References Roman, G.C., Erkinjuntti, T., Wallin, A., Pantoni, L. & Chui, H.C. Subcortical ischaemic vascular dementia. Lancet Neurol 1, 426–36 (2002). Venkat, P., Chopp, M. & Chen, J. Models and mechanisms of vascular dementia. Exp Neurol 272, 97–108 (2015). Gao, S. et al. 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Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Yang","suffix":""},{"id":599433194,"identity":"993a331e-27d5-4771-8c74-eab29ff5517b","order_by":9,"name":"Lu Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Zhao","suffix":""},{"id":599433195,"identity":"05dfc782-20ef-4677-a0ce-59f7f41adb8d","order_by":10,"name":"Xuman Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xuman","middleName":"","lastName":"Liu","suffix":""},{"id":599433196,"identity":"a7ee5402-50d9-4e7f-ae23-0dcaab2d6813","order_by":11,"name":"Zhifa Han","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhifa","middleName":"","lastName":"Han","suffix":""},{"id":599433197,"identity":"bd4d55a1-1686-4930-b62c-073d6d20f343","order_by":12,"name":"Tao Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Wang","suffix":""},{"id":599433198,"identity":"49bfa38d-a4a0-4472-8262-966848af2a8a","order_by":13,"name":"Yan Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":599433199,"identity":"7dc38745-b321-48d5-9bce-f403abf77598","order_by":14,"name":"Kun Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":599433200,"identity":"6ad3c7d6-45a9-4c8c-b5b2-ff12f80d8a59","order_by":15,"name":"Yan Chen","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Chen","suffix":""},{"id":599433201,"identity":"27dc0a90-5ef4-4094-8b6d-316718ef726e","order_by":16,"name":"Keshen Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Keshen","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-02-27 12:52:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8988189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8988189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781691,"identity":"7caf4b6d-2d1a-4bfe-b49a-a44f4be80260","added_by":"auto","created_at":"2026-03-17 07:56:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":847486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy overview.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePart 1\u003c/strong\u003e. Data sources and study population. We analyzed genome-wide association study (GWAS) summary statistics from four publicly available biobanks/cohorts comprising five ancestral populations: European (EUR), East Asian (EAS), South Asian (SAS), African (AFR), and Admixed American ancestry (AMR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePart 2\u003c/strong\u003e. Two-stage genetic discovery. Stage 1, European meta-analysis of UKBB and FinnGen (5,698 cases and 931,850 controls) identified 3 genome-wide significant loci (2 novel). Stage 2, Cross-ancestry meta-analysis of all four cohorts (5,889 cases and 1,027,883 controls) identified 37 significant loci. Analyses were performed with METAL and GWAMA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePart 3\u003c/strong\u003e. Characterization of genetic architecture. We performed functional annotation of risk variants and prioritized candidate genes through gene mapping, gene-based association testing, polygenic priority score (PoPS) analysis, transcriptome-wide association study (TWAS) with colocalization (COLOC), and summary-data-based Mendelian randomization (SMR). Pathway, tissue enrichment, and genetic correlation with lung function traits were also assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePart 4\u003c/strong\u003e. Cross-trait genetic analysis. A meta-analysis of VD and Alzheimer’s disease (AD) GWAS data using METAL and MTAG revealed shared genetic risk loci and shared risk genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePart 5\u003c/strong\u003e. Functional validation and drug target discovery. Genes were prioritized by integrating 29 lines of evidence. Their differential expression was validated in single-cell RNA sequencing data from VD patients, leading to the identification of potential druggable targets.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/a302371d1a94c4326713e6d7.jpg"},{"id":104539987,"identity":"f5164c5a-8d55-418e-b24c-424a89404eef","added_by":"auto","created_at":"2026-03-13 05:33:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":726375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional annotation of vascular dementia risk loci through GWAS meta-analyses.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Manhattan plots from the Stage 1 (European-specific) GWAS meta-analysis performed with METAL (upwards) and GWAMA (downwards).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Manhattan plots from the Stage 2 (cross-ancestry) GWAS meta-analysis performed with METAL (upwards) and GWAMA (downwards). In A and B, the horizontal axis shows the chromosomal position (chromosomes 1-22) and the vertical axis shows the significance (-log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003eP\u003c/em\u003e value) of tested markers. \u003cem\u003eP\u003c/em\u003e values are two-sided and based on an inverse variance weighted (IVW) fixed effects meta-analysis. Each dot represents a genetic variant. The threshold for genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;5.00E-08) is indicated by a grey dotted line, and genome-wide significance loci are shown in red. \u003cem\u003eP\u003c/em\u003e values are two-sided and derived from an inverse-variance-weighted fixed-effects meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e An UpSet plot and a Venn diagram illustrating the overlap of candidate risk genes identified through three independent mapping strategies (positional mapping, expression quantitative trait locus mapping, and chromatin interaction mapping) within the genome-wide significant loci from Stage 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e An UpSet plot and a Venn diagram showing the overlap of candidate risk genes identified by the same three mapping strategies within the genome-wide significant loci from Stage 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e Scatter plot summarizing the results of functional enrichment analyses for variants within the genome-wide significant loci from Stage 1 (upwards) and Stage 2 (downwards). Asterisks denote statistical significance: * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05/11.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/dcaabeb6f5322edfa1fd6064.jpg"},{"id":104782167,"identity":"cf371234-75a3-4e35-b310-15df553ac140","added_by":"auto","created_at":"2026-03-17 07:56:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":953962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene-based association test, polygenic priority score analysis, gene-set enrichment analysis, and tissue enrichment analysis.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Multi-track circular plot synthesizes the result of gene-based association and polygenic priority score (PoPS) analyses from Stage 1 and Stage 3. The concentric tracks, from outermost to innermost, sequentially display: the union of genes mapped to significant loci (FDR\u0026lt;0.05) from either stage; genes identified by gene-based association analysis in Stage 1 (the y-axis represents -log\u003csub\u003e10\u003c/sub\u003e adjusted \u003cem\u003eP\u003c/em\u003e-value), genes identified by gene-based association analysis in Stage 3 (the y-axis represents -log\u003csub\u003e10\u003c/sub\u003e adjusted \u003cem\u003eP\u003c/em\u003e-value), the top 10% of PoPS in Stage 1 (genes within this top 10% PoPS rank are filled in red), and top 10% of PoPS in Stage 3 (genes within this top 10% PoPS rank are filled in prink).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. \u003c/strong\u003eDual-axis bar chart presents the significant results (FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) of gene set enrichment analysis performed on Stage 1 using MAGMA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. \u003c/strong\u003eDual-axis bar chart presents the significant results (FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) of gene set enrichment analysis performed on Stage 3 using MAGMA. In B and C,the x-axis lists the names of the significant Gene Ontology (GO) terms. The left y-axis indicates the number of enriched genes (NGENES) within each term, represented by the bars. The right y-axis indicates the statistical significance level (-log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003eP\u003c/em\u003e-value) of the enrichment for each term, represented by the line plot with points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e Butterfly bar chart presents the results of tissue-specific enrichment analysis for Stage 1 and Stage 3 VD risk loci, performed using MAGMA based on the GTEx dataset. The y-axis lists the names of 30 tissues from GTEx. The horizontal position (x-axis) of each bar represents the enrichment beta value. Bars extending to the left depict enrichment results for Stage 1, while bars extending to the right depict results for Stage 3. For each stage, tissues with statistically significant enrichment (FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) are filled in red, and non-significant tissues are filled in blue.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/709e2b26d342a8fbb36e0c39.jpg"},{"id":104539990,"identity":"3f4a0f21-3cf1-49ce-a725-8ead2f00bfa1","added_by":"auto","created_at":"2026-03-13 05:33:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":539862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrative analysis of GWAS and multi-omics xQTLs in Stage 1.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Integrative analysis of VD Stage 1 and eQTLs datasets in relevant tissues using TWAS. The size of each dot represents the absolute z-score for each gene, and the dots are colored according to the direction of the effect. White dots indicate the associations with PPH4\u0026gt;0.70 in coloc analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Upset plot of genes identified via TWAS using expression weights data from 51 eQTLs datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e Integrative analysis of VD Stage 1 and eQTLs datasets in relevant tissues using SMR. The size of each dot represents the beta for each gene, and the dots are colored according to the direction of the effect. White dots indicate the associations with \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e\u0026gt;0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e Upset plot of genes identified via SMR using expression weights data from eQTLs datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e Grouped volcano plot validates genes identified via SMR using expression weights data from sc-qtl dataset. The seven panels correspond to seven major cell types. In each panel, the x-axis represents effect (b_SMR) and the y-axis represents the significance (-log\u003csub\u003e10 \u003c/sub\u003e\u003cem\u003ep\u003c/em\u003e_SMR). Each point represents a single gene within the specified cell type, visually confirming its dysregulation in the VD.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/87d44b498ba6175b5a056985.jpg"},{"id":104539995,"identity":"d415a0e7-0c7d-4cdf-8534-5c05e7b87f65","added_by":"auto","created_at":"2026-03-13 05:33:37","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":566584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of shared risk loci through multi-trait analysis.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eSchematic diagram of cross-trait meta-analysis for identifying shared genetic loci in AD and VD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. \u003c/strong\u003eManhattan plots from the VD (upwards) and AD GWAS (downwards) meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. \u003c/strong\u003eManhattan plots from the VD-AD (cross-trait) GWAS meta-analysis performed with METAL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. \u003c/strong\u003eManhattan plots from the VD (upwards) and AD GWAS (downwards) meta-analysis performed with MTAG. In B, C,and D, the horizontal axis shows the chromosomal position (chromosomes 1-22) and the vertical axis shows the significance (-log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003eP\u003c/em\u003e value) of tested markers. \u003cem\u003eP\u003c/em\u003e values are two-sided and based on an inverse variance weighted (IVW) fixed effects meta-analysis. Each dot represents a genetic variant. The threshold for genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;5.00E-08) is indicated by a grey dotted line, and genome-wide significance loci are shown in red. \u003cem\u003eP \u003c/em\u003evalues are two-sided and derived from an inverse-variance-weighted fixed-effects meta-analysis.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/82fd142e75bb2a9ca6c7a3d4.jpg"},{"id":104539991,"identity":"da707d7b-1c0c-411d-98c0-a1bef3a29037","added_by":"auto","created_at":"2026-03-13 05:33:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1020305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of evidence for vascular dementia priority genes (priority score≥10).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 29 lines of evidence were used for gene prioritization, and each column represents a type of supportive evidence. The left table displays the gene symbol and the total score across all evidence categories. The right heatmap groups and colors evidence categories according to their respective domains. Only genes with a priority score≥10 are shown in this figure, and the full results can be found in Supplementary Table 36. pLI, probability of being loss-of-function intolerant; CADD, combined annotation-dependent depletion; RDB, RegulomeDB; PoPS, polygenic priority score; TWAS, transcriptome-wide association study; COLOC, colocalization;,SMR, summary-data-based Mendelian randomization.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/befff0d55f25a0089c79e84f.jpg"},{"id":104539996,"identity":"0ee31a3a-7ff1-46ba-9618-cdd6b3034244","added_by":"auto","created_at":"2026-03-13 05:33:37","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":761499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell validation of differentially expressed genes across vascular dementia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-B. \u003c/strong\u003eThe UMAP plot illustrates the comprehensive annotation of VD and control samples into 6 distinct cell types in GSE282111, with each color representing a specific cell type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. \u003c/strong\u003eGrouped scatter plot validates the differential expression of 49 genes identified as shared between the GSE213897 and GSE282111 datasets, across distinct cell types within the GSE213897 single-cell RNA sequencing data\u003cstrong\u003e. \u003c/strong\u003ePanels correspond to four independent disease-control comparisons: DAPI VaD vs. NC, DAPI VaDadj vs. NC, DAPI+ERG VaD vs. NC, and DAPI+ERG VaDadj vs. NC. In each panel, the y-axis denotes individual cell types, while the x-axis represents the gene symbol. Each point signifies the expression change of a single gene within a specific cell type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. \u003c/strong\u003eGrouped volcano plot validates the differential expression of the 49 genes shared between the GSE213897 and GSE282111 datasets, across various cell types in the GSE282111 single-cell RNA sequencing data. The six panels correspond to six major cell types. In each panel, the x-axis represents the statistical significance of expression change (-log10 adjusted \u003cem\u003eP-\u003c/em\u003evalue), and the y-axis represents the magnitude and direction of change (log\u003csub\u003e2 \u003c/sub\u003efold change). Each point represents a single gene within the specified cell type, visually confirming its dysregulation in the VD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE. \u003c/strong\u003eBox plot of \u003cem\u003eADAMTS17 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF. \u003c/strong\u003eBox plot of \u003cem\u003eATP11A \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG. \u003c/strong\u003eBox plot of \u003cem\u003ePSAT1 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH. \u003c/strong\u003eBox plot of \u003cem\u003eBIN1 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI. \u003c/strong\u003eBox plot of \u003cem\u003eHLA-DRA\u003c/em\u003e from\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ. \u003c/strong\u003eBox plot of \u003cem\u003eADGRV1 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK. \u003c/strong\u003eBox plot of \u003cem\u003eAPOE \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL. \u003c/strong\u003eBox plot of \u003cem\u003eCDH18 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM. \u003c/strong\u003eBox plot of \u003cem\u003eADAMTSL1 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eN. \u003c/strong\u003eBox plot of \u003cem\u003eHPSE2 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eO. \u003c/strong\u003eBox plot of \u003cem\u003eTBC1D22A \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP. \u003c/strong\u003eBox plot of \u003cem\u003eCLU \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ. \u003c/strong\u003eBox plot of \u003cem\u003eTBC1D4 \u003c/em\u003efrom\u003cem\u003e \u003c/em\u003edifferential expression analysis using GSE282111;\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/e96780af087875c6f919887f.jpg"},{"id":104835171,"identity":"dc19392d-15ad-477b-9424-9905634a7fbc","added_by":"auto","created_at":"2026-03-17 17:41:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7719465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/12e40e08-5e50-4324-aebd-2e01b666581f.pdf"},{"id":104780797,"identity":"9c091c03-cb6e-4fc3-beb5-5b7a1f76c8dc","added_by":"auto","created_at":"2026-03-17 07:54:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1800862,"visible":true,"origin":"","legend":"Supplementary data 1","description":"","filename":"Supplementarydata1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/1633298cb47b649afd0775df.pdf"},{"id":104539992,"identity":"e2a4c7d1-2671-451b-8380-2aeb025821e8","added_by":"auto","created_at":"2026-03-13 05:33:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5523279,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/10c7073b3d9b846f594c868b.xlsx"},{"id":104539997,"identity":"9e17028e-232f-4722-ad1b-ebef30afe9b5","added_by":"auto","created_at":"2026-03-13 05:33:37","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13938591,"visible":true,"origin":"","legend":"Supplementary data 2","description":"","filename":"Supplementarydata2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/72bcb304af248cee71091453.pdf"},{"id":104539993,"identity":"4b1ed350-42d8-40de-9a26-5d867cb772f8","added_by":"auto","created_at":"2026-03-13 05:33:37","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":14316124,"visible":true,"origin":"","legend":"Supplementary figures","description":"","filename":"Supplementaryfigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8988189/v1/ea3d55dbfec0eda0f7f038a0.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Genome-wide cross-trait analysis of vascular dementia and Alzheimer’s disease highlights novel loci and lung-brain axis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVascular dementia (VD), caused by reduced blood flow to the brain, is the second most common form of dementia after Alzheimer\u0026rsquo;s disease (AD) and accounts for at least 20% of dementia cases \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Genetic factors play important roles in the etiology of both AD and VD \u003csup\u003e1,2\u003c/sup\u003e. Until now, large-scale genome-wide association study (GWAS) datasets have identified the common AD genetic variants and risk loci especially \u003cem\u003eAPOE\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Unlike AD, there are only few VD GWAS and limited sample sizes including 67 cases and 5,700 controls \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, 84 cases and 200 controls \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, 373 cases and 3,289 controls \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, 89 cases and 3,016 controls \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The Mega Vascular Cognitive Impairment and Dementia (MEGAVCID) Consortium performed a large-scale GWAS including 3,892 VD and 466,606 controls of European descent, and only identified one genetic variant rs429358 near the \u003cem\u003eAPOE\u003c/em\u003e reaching the genome-wide significance with \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;2.90E-196 \u003csup\u003e7\u003c/sup\u003e. Until now, the majority of genetic risk of VD remains unknown.\u003c/p\u003e \u003cp\u003eIt is known that GWAS was designed to broadly capture the common genetic variants with a minor allele frequency (MAF) greater than 1% \u003csup\u003e8,9\u003c/sup\u003e. Rare variants were not tagged by common genetic variants from genotyping arrays and imputation \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. It was largely unknown about the contribution of rare variants (MAF\u0026thinsp;\u0026lt;\u0026thinsp;1%) to human traits and diseases \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Until recently, publicly available biobanks using whole-genome sequence offered an unprecedented opportunity to assess the effects of both common and rare genetic variants on human traits and diseases, and highlighted large effects and significant contribution of rare variants \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we hypothesized that VD GWAS using rare variants may contribute to (1) increase the number of novel genetic variants and susceptibility loci, (2) identify the rare variants of large effects, and (3) increase the proportion of heritability. We collected four publicly available biobanks, and conducted the largest VD cross-ancestry GWAS meta-analysis to date in 5,886 patients diagnosed with VD and 1,027,883 control individuals from five ancestral populations: European, East Asian, South Asian, African, and Admixed American using genetic variants with MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01%. We further systematically characterized the genetic architecture of VD using multiple multi-omics integration approaches including gene mapping, gene-based association test, polygenic priority score (PoPS) analysis, gene set enrichment analysis, tissue enrichment analysis, transcriptome-wide association study (TWAS), colocalization analysis, summary-data-based Mendelian randomization (SMR), multi-trait analysis of GWAS (MTAG), case-control gene expression analysis, drug-gene interaction analysis, and genetic correlation analysis. An overview of the workflow is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEuropean-specific GWAS meta-analysis (stage 1)\u003c/h2\u003e \u003cp\u003eWe selected two independent VD GWAS datasets including 5,698 patients diagnosed with VD and 931,850 control individuals of European descent from UKBB (2,074 VD and 456,366 controls) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and FinnGen R12 (3,624 VD and 475,484 controls) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e (Methods, Supplementary Table\u0026nbsp;1). We conducted a large-scale GWAS meta-analysis of both datasets using a fixed-effects inverse-variance weighted (IVW) method implemented in METAL \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Using linkage disequilibrium (LD) score regression (LDSC) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, we estimated the single nucleotide polymorphism (SNP) based heritability on liability scale to be ℎ\u003csup\u003e2\u003c/sup\u003e=6.57% and s.e.=0.0132, assuming a VD population prevalence of 1.16% \u003csup\u003e19\u003c/sup\u003e. The genomic inflation factor (λ\u003csub\u003eGC\u003c/sub\u003e) was 1.0754 and the LDSC intercept was 1.0241 (s.e.=0.0078), which indicated little evidence of genetic inflation (Supplementary Fig.\u0026nbsp;1A).\u003c/p\u003e \u003cp\u003eUtilizing Functional Mapping and Annotation (FUMA) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we identified three independent genome-wide significant loci including \u003cem\u003eHTR4\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, and \u003cem\u003eAPOE\u003c/em\u003e, which are tagged by rs564080066, rs7982 and rs429358, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Fig.\u0026nbsp;2). A sensitivity GWAS meta-analysis using GWAMA (Genome-Wide Association Meta-Analysis) further confirmed all three loci (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). rs564080066 is novel and rare variant with an effect allele frequency of 0.0023 in European population (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). rs7982 is a proxy of rs11136000 (\u003cem\u003er\u003c/em\u003e\u0026sup2;=0.98 and D\u0026rsquo;=0.99), which was a known genome-wide significant variant associated with AD \u003csup\u003e22\u003c/sup\u003e. rs429358 is a well-known genome-wide significant variant associated with multiple dementias including VD \u003csup\u003e7\u003c/sup\u003e, AD \u003csup\u003e22\u003c/sup\u003e, frontotemporal dementia (FTD) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and Lewy body dementia (LBD) \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Using FUMA \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we identified 48 independent genetic variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6, Supplementary Table\u0026nbsp;2) and 21 independent lead variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, Supplementary Table\u0026nbsp;3) around these three loci. A stepwise conditional analysis using GCTA conditional and joint (COJO) analysis and linkage disequilibrium (LD) information from UKBB individuals \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e further confirmed these three loci (Supplementary Table\u0026nbsp;4). Using positional mapping, eQTLs mapping, and chromatin interaction mapping (Supplementary Table\u0026nbsp;5, Supplementary Data 1), we identified 120 VD risk genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Supplementary Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \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\u003eGenome-wide signifiant loci from vascular dementia GWAS meta-analysis in stage 1 and stage 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocus\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\u003eKnown/Novel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePos\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnsembl ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNearest gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eA2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFreq\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eMETAL\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eGWAMA\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"14\" nameend=\"c14\" namest=\"c1\"\u003e \u003cp\u003eGenome-wide-significant loci identified in Stage1 (EUR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers564080066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148,037,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000164270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHTR4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.0203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.29E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.39E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers7982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,462,481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000120885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCLU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.1134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.62E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7.82E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers429358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45,411,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000130203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.6945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.32E-199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.93E-199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenome-wide-significant loci identified in Stage2 (Cross)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers150423973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116,871,703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000163399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eATP1A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.5278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.6405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.63E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.72E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers55977072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120,072,559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000203857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHSD3B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-3.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.49E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.60E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers555863053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167,628,193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000198771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRCSD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.8996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.7018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.75E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.81E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers543737714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,559,484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000134321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.3646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.00E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.09E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers571573246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e129,694,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000136720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eHS6ST1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.3224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.00E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.08E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers138654898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170,746,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000144357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eUBR3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.1549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.72E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.77E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers536931879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,967,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000163703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCRELD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.90E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.01E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers79120584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers534723021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20,583,716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000145526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCDH18\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.0688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.7008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6.39E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6.56E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers200249535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79,616,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000164299\u003c/p\u003e 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colname=\"c12\"\u003e \u003cp\u003e0.5539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.53E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.57E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers564080066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148,037,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000164270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75,160,301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000111799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCOL12A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-4.0564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd 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colname=\"c14\"\u003e \u003cp\u003e7.33E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers561189374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149,911,042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000106526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eACTR3C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e 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colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers11136000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,464,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000120885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCLU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.1074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.0193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.77E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.83E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers192554851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18,356,094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000178031\u003c/p\u003e \u003c/td\u003e 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colname=\"c13\"\u003e \u003cp\u003e3.38E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3.53E-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers184836761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131,052,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000125207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePIWIL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75,417,919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000136111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTBC1D4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.6903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.6315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.12E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.26E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers576381927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113,427,550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000068650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eATP11A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e 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colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers547455871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26,349,988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000206190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eATP10A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd 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colname=\"c12\"\u003e \u003cp\u003e0.4512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.23E-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.29E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers564332482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,181,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000196557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47,270,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000129636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eITFG1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.6966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.6446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9.74E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.00E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers429358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45,411,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000130203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e 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\u003cp\u003e1.55E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers573127339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35,794,556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000101353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eMROH8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.8213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.6256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.01E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.04E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers569752694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40,364,147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000124177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eCHD6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.6128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.8552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5.28E-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.46E-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers575233877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40,716,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000196090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePTPRT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.8725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.8291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.18E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.29E-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers529401448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNovel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47,747,652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eENSG00000054611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTBC1D22A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.7615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.8953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.23E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.27E-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eSNP, single-nucleotide polymorphism; A1: effect allele; A2: non-effect allele; Chr : chromosome; Pos: position on hg19; Freq: the frequency of A1; Beta; effect size; SE; standard error.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eA total of 29 lines of evidence were used for gene prioritization, and each column represents a type of supportive evidence. The left table displays the gene symbol and the total score across all evidence categories. The right heatmap groups and colors evidence categories according to their respective domains. Only genes with a priority score\u0026thinsp;\u0026ge;\u0026thinsp;10 are shown in this figure, and the full results can be found in Supplementary Table\u0026nbsp;36. pLI, probability of being loss-of-function intolerant; CADD, combined annotation-dependent depletion; RDB, RegulomeDB; PoPS, polygenic priority score; TWAS, transcriptome-wide association study; COLOC, colocalization;,SMR, summary-data-based Mendelian randomization.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCross-ancestry GWAS meta-analysis (stage 2)\u003c/h3\u003e\n\u003cp\u003eWe conducted a cross-ancestry VD GWAS meta-analysis using the fixed-effects IVW meta-analysis method including 5,886 patients diagnosed with VD and 1,027,883 control individuals using four independent GWAS datasets from UKBB (European, 2,074 VD and 456,366 controls) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, FinnGen R12 (European, 3,624 VD and 475,484 controls) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, Genes \u0026amp; Health (GH, South Asian, 119 VD and 43,659 controls) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and Mass General Brigham Biobank (MGBB, European, South Asian, African, and Admixed American, 69 VD and 52,374 controls) (Methods, Supplementary Table\u0026nbsp;1) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The genomic inflation factor \u003cem\u003eλ\u003c/em\u003e\u003csub\u003eGC\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.0741 and LDSC intercept of 1.0204 (s.e.= 0.0074) showed little evidence of genetic inflation (Supplementary Fig.\u0026nbsp;1B). The SNP-based heritability on liability scale was \u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.47% (s.e.=0.0112) assuming the VD population prevalence of 1.16% \u003csup\u003e19\u003c/sup\u003e. We revealed 37 independent genome-wide significant loci by confirming \u003cem\u003eHTR4\u003c/em\u003e (rs564080066), \u003cem\u003eCLU\u003c/em\u003e (rs11136000), and \u003cem\u003eAPOE\u003c/em\u003e (rs429358) from European-specific GWAS meta-analysis, and highlighting 34 novel loci all tagged by rare variants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Fig.\u0026nbsp;3). These 37 GWAS loci explained 53% of VD variance including 13.25% from loci tagged by common variants and 39.19% from loci tagged by rare variants. A sensitivity GWAS meta-analysis using GWAMA shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB further confirmed 36 loci (excluding \u003cem\u003eCRELD1\u003c/em\u003e) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Using FUMA, we identified 111 independent genetic variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6, Supplementary Table\u0026nbsp;7) and 66 independent lead variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, Supplementary Table\u0026nbsp;8) around these three loci (\u003cem\u003eHTR4\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eAPOE\u003c/em\u003e). GCTA COJO analysis further confirmed these loci (Supplementary Table\u0026nbsp;9, Supplementary Data 2). Using positional mapping, eQTLs mapping, and chromatin interaction mapping, we identified 340 VD risk genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Supplementary Table\u0026nbsp;10).\u003c/p\u003e\n\u003ch3\u003eFunctional annotation\u003c/h3\u003e\n\u003cp\u003eIn stage 1, we annotated 388 SNPs in LD with the 48 independent significant lead variants using ANNOVAR \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and found predominant enrichment in the intronic, upstream, downstream, exonic, UTR3 and UTR5 (Supplementary Tables\u0026nbsp;11\u0026ndash;12 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Among these 388 SNPs, we identified 286 SNPs that were in LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6) with 48 independent significant lead variants, extracted from the 1000 genomes European reference panel (Supplementary Table\u0026nbsp;13) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. rs564080066 is an intronic variant with a Combined Annotation Dependent Depletion (CADD) score of 3.382, indicating a moderate potential of regulatory impact (Supplementary Table\u0026nbsp;13) \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. rs7982 is an exonic variant with CADD score of 1.763, indicating a moderate potential of regulatory impact (Supplementary Table\u0026nbsp;13). rs429358 is an exonic variant with a high CADD score of 12.64, suggesting a potential of deleterious effects (Supplementary Table\u0026nbsp;13). In stage 2, we annotated 381 SNPs in LD with the 89 independent lead variants using ANNOVAR \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and revealed predominant enrichment in intronic, downstream, exonic, upstream, ncRNA_exonic and UTR3 genomic categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, Supplementary Tables\u0026nbsp;14\u0026ndash;15). Among these 381 SNPs, 278 SNPs were in LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.6) with 89 independent significant lead variants, extracted from the 1000 genomes ALL reference panel (Supplementary Table\u0026nbsp;16) \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eGene-based association test, gene set and tissue enrichment analyses\u003c/h3\u003e\n\u003cp\u003eIt is noted all subsequent analyses were performed using the stage 1 GWAS summary statistics. We conducted a gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of stage 1 VD GWAS meta-analysis summary data using MAGMA \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Gene-based association test identified 18 statistically significant genes with the Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;17). 15 genes are located within the \u003cem\u003eAPOE\u003c/em\u003e region including \u003cem\u003eAPOC1\u003c/em\u003e, \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eAPOC4\u003c/em\u003e, \u003cem\u003eNECTIN2\u003c/em\u003e, \u003cem\u003eTOMM40\u003c/em\u003e, \u003cem\u003eRELB\u003c/em\u003e, \u003cem\u003eCEACAM16\u003c/em\u003e, \u003cem\u003ePVR\u003c/em\u003e, \u003cem\u003eCLPTM1\u003c/em\u003e, \u003cem\u003eIGSF23\u003c/em\u003e, \u003cem\u003eCEACAM19\u003c/em\u003e, \u003cem\u003eEXOC3L2\u003c/em\u003e, \u003cem\u003eKLC3\u003c/em\u003e, \u003cem\u003eZNF226\u003c/em\u003e, \u003cem\u003eZNF227\u003c/em\u003e. 3 genes are located outside the \u003cem\u003eAPOE\u003c/em\u003e region including \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eCALCRL\u003c/em\u003e, and \u003cem\u003eWNK1\u003c/em\u003e (Supplementary Table\u0026nbsp;17). In brief, gene-based association test not only verified two GWAS loci \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eCLU\u003c/em\u003e, but also highlighted two additional novel genes \u003cem\u003eCALCRL\u003c/em\u003e and \u003cem\u003eWNK1\u003c/em\u003e (Supplementary Table\u0026nbsp;17).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene set enrichment analysis identified two statistically significant gene ontology (GO) cellular components with the Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 including neurofibrillary tangle (GO:0097418, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.24E-05, and FDR\u0026thinsp;=\u0026thinsp;1.29E-02), and late endosome (GO:0005770, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.42E-05, and FDR\u0026thinsp;=\u0026thinsp;2.30E-02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Supplementary Table\u0026nbsp;18). Meanwhile, 5 GO biological processes including positive regulation of amyloid fibril formation (GO:1905908, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.63E-05, and FDR\u0026thinsp;=\u0026thinsp;1.96E-01), NMDA glutamate receptor clustering (GO:0097114, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.04E-04, and FDR\u0026thinsp;=\u0026thinsp;3.19E-01), regulation of amyloid fibril formation (GO:1905906, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.62E-0504, and FDR\u0026thinsp;=\u0026thinsp;3.88E-01), amyloid-beta clearance by cellular catabolic process (GO:0150094, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.78E-04, and FDR\u0026thinsp;=\u0026thinsp;4.00E-01), negative regulation of amyloid fibril formation (GO:1905907, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.35E-04, and FDR\u0026thinsp;=\u0026thinsp;4.00E-01) showed suggestive association with VD (Supplementary Table\u0026nbsp;18). Tissue enrichment analysis showed evidence of enrichment in GTEx v8 lung (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.60E-03) and spleen (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.46E-02), but not in brain tissues or blood (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.66E-02) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Supplementary Table\u0026nbsp;19).\u003c/p\u003e\n\u003ch3\u003eGenetic association between VD and lung function traits\u003c/h3\u003e\n\u003cp\u003eTissue enrichment analysis showed that the VD heritability was mainly enriched in GTEx v8 lung (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.60E-03) (Supplementary Table\u0026nbsp;20). Here, we further investigated the genetic correlation between VD (stage 1) and 6 lung function traits including forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), FEV1/FVC, peak expiratory flow (PEF), asthma and COPD using LDSC (Supplementary Table\u0026nbsp;20) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. We identified that VD showed statistically significant negative genetic correlation with FEV1 (\u003cem\u003erg\u003c/em\u003e=-0.1695, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.20E-03) and FVC (\u003cem\u003erg\u003c/em\u003e=-0.1678, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.10E-03) using a Bonferroni-corrected statistical significance threshold of 0.05/6. VD was suggestively associated with PEF (\u003cem\u003erg\u003c/em\u003e=-0.1528, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.26E-02), and asthma (\u003cem\u003erg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1965, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.81E-02) (Supplementary Table\u0026nbsp;21). These findings showed that impaired lung function was associated with a higher risk of VD.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePolygenic Priority Score\u003c/h2\u003e \u003cp\u003ePolygenic Priority Score (PoPS) is a new method that learns trait-relevant gene features to pinpoint the most likely causal genes at GWAS loci \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We computed the polygenic priority score by combining the stage 1 VD GWAS meta-analysis summary statistics with biological pathways, gene expression and protein\u0026ndash;protein interaction data \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Among 18 statistically significant genes from gene-based association test, \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eAPOC1\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eRELB\u003c/em\u003e, \u003cem\u003eWNK1\u003c/em\u003e, \u003cem\u003ePVR\u003c/em\u003e, \u003cem\u003eIGSF23\u003c/em\u003e, \u003cem\u003eCALCRL\u003c/em\u003e, \u003cem\u003eEXOC3L2\u003c/em\u003e, \u003cem\u003eTOMM40\u003c/em\u003e, \u003cem\u003eCLPTM1\u003c/em\u003e, and \u003cem\u003eKLC3\u003c/em\u003e were ranked the top 10% of the polygenic priority scores (range: 0.37\u0026ndash;4.22), underscoring their likely functional relevance and potential roles in the pathogenesis of VD. \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eWNK1\u003c/em\u003e, and \u003cem\u003eCALCRL\u003c/em\u003e were ranked with the highest, 4th, 42nd and 125th polygenic priority scores, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;22).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTranscriptome-wide association study and colocalization analysis\u003c/h3\u003e\n\u003cp\u003eWe performed a TWAS to identify the genes whose expression levels are implicated in the pathogenesis of VD \u003csup\u003e33\u003c/sup\u003e. Here, we integrated the stage 1 VD GWAS meta-analysis dataset with the gene expression data from Genotype-Tissue Expression (GTEx) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, Young Finns Study (YFS) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, and brain eQTLs datasets from CommonMind Consortium (CMC) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, respectively. We identified 25 transcriptome-wide significant VD genes with the Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 including 17 genes around the \u003cem\u003eAPOE\u003c/em\u003e region and 8 genes outside the \u003cem\u003eAPOE\u003c/em\u003e region including \u003cem\u003eCLU\u003c/em\u003e (novel GWAS locus and novel gene from gene-based association test), \u003cem\u003eWNK1\u003c/em\u003e (novel gene from gene-based association test), \u003cem\u003eSLC17A4\u003c/em\u003e, \u003cem\u003ePARD3\u003c/em\u003e, \u003cem\u003eAFF1\u003c/em\u003e, \u003cem\u003eFBXW8\u003c/em\u003e, \u003cem\u003eWDR27\u003c/em\u003e, and \u003cem\u003eWWOX\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B, Supplementary Table\u0026nbsp;23). These genes exhibited multiple lines of evidence with VD including \u003cem\u003eCLU\u003c/em\u003e (2 tissues), \u003cem\u003eWNK1\u003c/em\u003e (2 tissues), \u003cem\u003eSLC17A4\u003c/em\u003e (4 tissues), \u003cem\u003ePARD3\u003c/em\u003e (aorta artery), \u003cem\u003eAFF1\u003c/em\u003e (6 tissues), \u003cem\u003eFBXW8\u003c/em\u003e (24 tissues), \u003cem\u003eWDR27\u003c/em\u003e (41 tissues), and \u003cem\u003eWWOX\u003c/em\u003e (24 tissues) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B, Supplementary Table\u0026nbsp;23). \u003cem\u003eCALCRL\u003c/em\u003e, the novel gene from gene-based association test, also showed evidence of association with VD in 9 tissues including aorta artery, coronary artery, tibial artery, cerebellum, tibial nerve, and whole blood. Bayesian colocalization analysis highlighted that \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eWWOX\u003c/em\u003e, and \u003cem\u003eNKPD1\u003c/em\u003e (within the \u003cem\u003eAPOE\u003c/em\u003e region) showed evidence of colocalization with PPH\u003csub\u003e4\u003c/sub\u003e\u0026gt;0.70 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B, Supplementary Table\u0026nbsp;24).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eSummary-data-based Mendelian randomization\u003c/h3\u003e\n\u003cp\u003eWe conducted a SMR \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e to identify genes putatively causally associated with VD by integrating the stage 1 VD GWAS meta-analysis summary data with multiple eQTLs datasets from GTEx (v8 54 tissues) \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, eQTLGen (whole blood) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, BrainMeta v2 (brain) \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and brain single-nucleus eQTLs datasets \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Using bulk tissue eQTLs datasets, we revealed 3 statistically significant genes including \u003cem\u003eAPOC4\u003c/em\u003e, \u003cem\u003eAPOC1\u003c/em\u003e, and \u003cem\u003eCLU\u003c/em\u003e with the Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and HEIDI (heterogeneity in dependent instruments) test \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D, Supplementary Table\u0026nbsp;25). Collectively, both SMR and TWAS provide consistent findings about the involvement of \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eCLU\u003c/em\u003e in VD. Using single-nucleus eQTLs datasets, SMR highlighted \u003cem\u003ePICALM\u003c/em\u003e as the only statistically significant gene with Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in microglia. In brief, genetically increased \u003cem\u003ePICALM\u003c/em\u003e expression in microglia was associated with significantly decreased risk of VD with beta=-0.18, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e=4.52E-05, FDR\u0026thinsp;=\u0026thinsp;0.03, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e=0.87 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, Supplementary Table\u0026nbsp;26). Meanwhile, \u003cem\u003ePICALM\u003c/em\u003e was suggestively associated with the risk of VD in oligodendrocyte (beta\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e=8.25E-04, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e=0.14) and excitatory neuron (beta=-0.63, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e=5.74E-03, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e=0.52) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, Supplementary Table\u0026nbsp;26).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCross-trait meta-analysis of VD and AD (stage 3)\u003c/h2\u003e \u003cp\u003eWe first estimated the genetic correlation between VD (stage 1) and AD (European) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e using LDSC \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and found a statistically significant positive genetic correlation with \u003cem\u003er\u003c/em\u003eg\u0026thinsp;=\u0026thinsp;0.4469 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.75E-14, which suggested that it was appropriate and reliable to combine both GWAS datasets. We further conducted a cross-trait meta-analysis of VD (stage 1) and AD GWAS datasets using the fixed-effects IVW method implemented in METAL \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and identified 13 independent genome-wide significant loci including \u003cem\u003eCR1\u003c/em\u003e, \u003cem\u003eBIN1\u003c/em\u003e, \u003cem\u003eGRM7\u003c/em\u003e, \u003cem\u003eHLA-DRA\u003c/em\u003e, \u003cem\u003eTREM2\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eECHDC3\u003c/em\u003e, \u003cem\u003eAGBL2\u003c/em\u003e, \u003cem\u003eMS4A4E\u003c/em\u003e, \u003cem\u003ePICALM\u003c/em\u003e, \u003cem\u003eSLC24A4\u003c/em\u003e, \u003cem\u003eABCA7\u003c/em\u003e, and \u003cem\u003eAPOE\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-C, Supplementary Table\u0026nbsp;27). Subsequently, we performed a sensitivity cross-trait meta-analysis of AD and VD GWAS datasets using multi-trait analysis of GWAS (MTAG) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. MTAG identified 10 genome-wide significant loci for AD and 11 loci for VD, of which 10 loci were shared between the two traits. Interestingly, all these 10 shared loci were known loci identified using METAL, further confirming the robustness and pleiotropic nature of these genetic signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, Supplementary Table\u0026nbsp;28). Using FUMA \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, we further delineated 213 independent genetic variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026lt; 0.6; Supplementary Table\u0026nbsp;29) and 72 independent lead variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026lt; 0.1; Supplementary Table\u0026nbsp;30), and annotated 2,213 SNPs in LD with the 213 independent significant lead variants across these loci. Through positional mapping, eQTL mapping, and chromatin‑interaction mapping, we ultimately identified 353 candidate risk genes (Supplementary Tables\u0026nbsp;31\u0026ndash;33).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe conducted the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of the cross-trait GWAS meta-analysis summary data using MAGMA \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Gene-based association test identified 127 statistically significant genes with the Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;34). 44 of these 127 genes were ranked within the top 10% of polygenic priority scores (range: 0.37\u0026ndash;4.22), indicating their high posterior probability of being functionally relevant in VD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Supplementary Table\u0026nbsp;35) \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eCLU\u003c/em\u003e were ranked with the highest and 4th polygenic priority scores, respectively (Supplementary Table\u0026nbsp;35).\u003c/p\u003e \u003cp\u003eGene set enrichment analysis identified 49 statistically significant GO pathways with the Benjamini-Hochberg FDR-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 including neurofibrillary tangle (GO:0097418, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.99E-11, and FDR\u0026thinsp;=\u0026thinsp;1.02E-06), negative regulation of endopeptidase activity (GO:0010951), autophagic cell death (GO:0048102), positive regulation of complement activation (GO:0045917), regulation of antigen processing and presentation of peptide or polysaccharide antigen via MHC class II (GO:0002580), negative regulation of amyloid precursor protein catabolic process (GO:1902992), amyloid-beta clearance by cellular catabolic process (GO:0150094, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.31E-06, and FDR\u0026thinsp;=\u0026thinsp;2.69E-03), positive regulation of amyloid-beta clearance (GO:1900223), positive regulation of high-density lipoprotein particle clearance (GO:0010983), and regulation of amyloid fibril formation (GO:1905906, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.18E-06, and FDR\u0026thinsp;=\u0026thinsp;3.24E-03), as the top 10 significant signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Supplementary Table\u0026nbsp;36). Tissue enrichment analysis identified evidence of enrichment of VD heritability in GTEx v8 liver (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.08E-04), blood (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.56E-03), lung (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.68E-02), spleen (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.23E-02), and small intestine (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.54E-02), but not in human brain tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Supplementary Table\u0026nbsp;37).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGene prioritization\u003c/h2\u003e \u003cp\u003eTo identify the potential causal genes, we integrated evidence from 29 complementary approaches: GWAS significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08), gene mapping (position mapping, chromatin interaction mapping, or eQTLs mapping), variant annotation (exonic SNPs, CADD/RDB/pLI), gene-based association test (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05), PoPS (top 10%), TWAS (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05), colocalization (\u003cem\u003ePPH\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026gt;0.70), SMR (eQTLs/sc-QTLs, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05), drug-gene interaction, gene expression in brain cells (|Log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05). We calculated the priority scores ranging from 1 to 29 (Supplementary Table\u0026nbsp;38). Finally, 14 of the 619 protein-coding genes were supported by at least 15 lines of evidence, including \u003cem\u003eCLU\u003c/em\u003e and other 13 genes around the \u003cem\u003eAPOE\u003c/em\u003e region (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis in human brain cells\u003c/h2\u003e \u003cp\u003eWe identified 619 protein-coding genes using VD GWAS (stage 1 and stage 2), gene mapping (stage 1 and stage 2), gene-based association test (stage 1), TWAS (stage 1), SMR (stage 1), cross-trait GWAS meta-analysis, gene mapping (stage 3), and gene-based association test (stage 3) (Supplementary Table\u0026nbsp;38). To investigate the differential expression of VD genes in human brain cells, we analyzed the single-nucleus RNA-sequencing (snRNA-seq) data in human periventricular white matter from 5 VD patients with lesion, 5 VD patients adjacent to the lesion, and 5 normal control subjects (GEO accession: GSE213897, Methods) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, 4 VD patients and 4 age and sex-matched healthy controls (GEO accession: GSE282111, Methods) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. All single cells in GSE282111 were classified into 30 clusters and 6 primary cell types astrocytes, endothelial cells, microglia, neurons, oligodendrocytes and oligodendrocyte precursor cells (OPCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). 89 and 201 genes including 49 shared genes exhibited significantly differential expression in VD patients compared to normal controls in at least one cell type with |Log\u003csub\u003e2\u003c/sub\u003efold change (FC)|\u0026gt;0.5 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05 in GSE213897 and GSE282111, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D, Supplementary Tables\u0026nbsp;39\u0026ndash;40). Briefly, we found significantly differential expression of 24 VD GWAS loci (\u003cem\u003eDPP6\u003c/em\u003e, \u003cem\u003eITFG1\u003c/em\u003e, \u003cem\u003eRCSD1\u003c/em\u003e, \u003cem\u003eCHD6\u003c/em\u003e, \u003cem\u003ePTPRT\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eHPSE2, ADAMTS17\u003c/em\u003e, \u003cem\u003eATP11A\u003c/em\u003e, \u003cem\u003eTBC1D22A\u003c/em\u003e, \u003cem\u003eTBC1D4\u003c/em\u003e, \u003cem\u003eADAMTSL1\u003c/em\u003e, \u003cem\u003eADGRV1\u003c/em\u003e, \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eCDH18\u003c/em\u003e, \u003cem\u003ePSAT1\u003c/em\u003e, \u003cem\u003eHERC3\u003c/em\u003e, \u003cem\u003eATP1A1\u003c/em\u003e, \u003cem\u003eCRELD1\u003c/em\u003e, \u003cem\u003eCOL12A1\u003c/em\u003e, \u003cem\u003eHS6ST1\u003c/em\u003e, \u003cem\u003eMROH8\u003c/em\u003e, \u003cem\u003eTMEM87A\u003c/em\u003e, \u003cem\u003eATP10A\u003c/em\u003e) and 9 cross-trait GWAS loci (\u003cem\u003eSLC24A4\u003c/em\u003e, \u003cem\u003eCD2AP\u003c/em\u003e, \u003cem\u003ePICALM\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eHLA-DRA\u003c/em\u003e, \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eBIN1\u003c/em\u003e, \u003cem\u003eABCA7\u003c/em\u003e, \u003cem\u003eTREM2\u003c/em\u003e, \u003cem\u003eMS4A4E\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-Q, Supplementary Tables\u0026nbsp;39\u0026ndash;40, Supplementary Fig.\u0026nbsp;4). Meanwhile, \u003cem\u003eCALCRL\u003c/em\u003e from gene-based association test, \u003cem\u003eWWOX\u003c/em\u003e, \u003cem\u003ePARD3\u003c/em\u003e, \u003cem\u003eAFF1\u003c/em\u003e and \u003cem\u003eFBXW8\u003c/em\u003e from TWAS also showed significantly differential expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDrug-gene interaction analysis\u003c/h2\u003e \u003cp\u003eTo investigate whether these significantly differentially expressed genes are potential therapeutic targets, we examined the interactions between these genes and known drugs using the Drug-Gene Interaction Database 5.0 (DGIdb, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org\u003c/span\u003e\u003cspan address=\"https://dgidb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Among 241 significantly differentially expressed genes, 21 showed strong evidence of interaction with U.S. Food and Drug Administration (FDA) approved drugs with interaction score\u0026thinsp;\u0026gt;\u0026thinsp;1, which highlighted the potential clinical utility of these genes (Supplementary Table\u0026nbsp;41). \u003cem\u003eCDH18\u003c/em\u003e, the VD GWAS significant locus, indicated the strongest interaction (interaction score\u0026thinsp;=\u0026thinsp;26.25) with Thiamine (vitamin B1). \u003cem\u003eATP1A1\u003c/em\u003e, the VD GWAS significant locus, indicated the strong interaction with Almitrine (interaction score\u0026thinsp;=\u0026thinsp;10.50). \u003cem\u003eHSD3B1\u003c/em\u003e, the VD GWAS significant locus, indicated the strong interaction with Trilostane (interaction score\u0026thinsp;=\u0026thinsp;8.75). \u003cem\u003eNT5E\u003c/em\u003e, the VD GWAS significant locus, indicated the strong interaction with Tinidazole (interaction score\u0026thinsp;=\u0026thinsp;5.53), which is used to treat infections caused by protozoa. \u003cem\u003eATP10A\u003c/em\u003e, the VD GWAS significant locus, indicated the strong interaction with Duloxetine (interaction score\u0026thinsp;=\u0026thinsp;20.2), which is used to treat depression and anxiety. \u003cem\u003eSLC24A4\u003c/em\u003e, the cross-trait GWAS significant locus, indicated the strong interaction with salbutamol (interaction score\u0026thinsp;=\u0026thinsp;8.08), which was approved to treat asthma and chronic obstructive pulmonary disease (COPD).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUntil now, \u003cem\u003eAPOE\u003c/em\u003e is the only genome-wide significant locus identified by recent VD GWAS from MEGAVCID consortium \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Here, we performed the largest VD GWAS meta-analysis to date in 1,033,769 individuals including 5,886 VD patients and 1,027,883 controls from five ancestral populations: European, East Asian, South Asian, African, and Admixed American. We identified 37 independent genome-wide significant loci including \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eCLU\u003c/em\u003e tagged by common variants and 35 loci tagged by rare variants, which explained 53% of VD variance. Gene-based association test, TWAS, SMR and gene set enrichment analysis identified statistically significant 18, 25, 4 genes and 2 pathways, respectively. Cross-trait meta-analysis of VD and AD identified 13 independent genome-wide significant loci, 127 statistically significant genes, 49 statistically significant pathways. These findings provide novel insights into the genetic basis of VD and new leads for the molecular mechanisms underlying VD.\u003c/p\u003e \u003cp\u003eUntil now, the exact roles of \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eCLU\u003c/em\u003e in VD remain unclear, however their roles have been investigated in AD. \u003cem\u003eAPOE\u003c/em\u003e is a well-known genome-wide significant locus associated with multiple dementias including AD \u003csup\u003e22\u003c/sup\u003e, FTD \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and LBD \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCLU\u003c/em\u003e, which codes clusterin protein, was a known genome-wide significant locus for AD \u003csup\u003e22\u003c/sup\u003e. It is known that VD is caused by reduced blood flow to the brain, which damages and eventually kills brain cells \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Loss of clusterin shifts amyloid deposition to the cerebrovasculature, and promotes cerebrovascular cerebral amyloid angiopathy (CAA), which is a neurological condition where amyloid-beta protein deposits in the walls of cerebral blood vessels \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCLU\u003c/em\u003e ameliorates diabetic atherosclerosis by inhibiting the release of inflammatory factors and macrophage pyroptosis \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to \u003cem\u003eAPOE\u003c/em\u003e and \u003cem\u003eCLU\u003c/em\u003e, we identified \u003cem\u003eCALCRL\u003c/em\u003e and \u003cem\u003eWNK1\u003c/em\u003e as two additional novel VD genes using gene-based association test. \u003cem\u003eCALCRL\u003c/em\u003e (calcitonin receptor like receptor) is a major G-protein-coupled neuropeptide receptor for both adrenomedullin and calcitonin gene-related peptide (CGRP), which contribute to widen blood vessels, allowing more blood to flow through \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. TWAS revealed that genetically decreased arterial expression of \u003cem\u003eCALCRL\u003c/em\u003e and higher abundance in blood were associated with increased white matter hyperintensities (WMH), a MRI marker of cerebral small vessel disease (CSVD) that is the most common pathology underlying VD \u003csup\u003e51,52\u003c/sup\u003e. \u003cem\u003eCALCRL\u003c/em\u003e was a known genome-wide significant locus for ischemic stroke \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Mendelian randomization showed that higher expression of CALCRL in the brain tissues was linked to larger WMH burden and AD risk \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eWNK1\u003c/em\u003e plays an important role in regulating blood pressure and vasoconstriction \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Inactivation of mouse \u003cem\u003eWnk1\u003c/em\u003e in mature neurons leads to axon degeneration in the adult brain, and \u003cem\u003eWNK1\u003c/em\u003e may have neuroprotective role in kinds of neurodegenerative diseases \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePARD3\u003c/em\u003e, TWAS significant gene in aorta artery, regulates the trafficking and processing of amyloid precursor protein \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Loss of Par3 promotes dendritic spine neoteny and enhances learning and memory \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Hippocampal atrophy is a recognized biological marker of AD \u003csup\u003e60\u003c/sup\u003e. \u003cem\u003eFBXW8\u003c/em\u003e, TWAS significant gene in 24 tissues, is a known genome-wide significant locus for hippocampal volume \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eWWOX\u003c/em\u003e, TWAS significant gene in 24 tissues, is a known genome-wide significant locus for AD \u003csup\u003e61\u003c/sup\u003e. \u003cem\u003eWDR27\u003c/em\u003e, TWAS significant gene in 41 tissues, is associated with a higher genetic risk for AD and related dementia \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The combination of \u003cem\u003eWDR27\u003c/em\u003e variants \u003cem\u003eUNC93A\u003c/em\u003e variants can impair the function of the neurovascular unit (which includes brain blood vessels) and contribute to the development of dementia \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eATP1A1\u003c/em\u003e, a novel VD GWAS significant locus tagged by rare variant, is associated with thrombosis and hypertension. \u003cem\u003eATP1A1\u003c/em\u003e haplodeficiency or inhibition significantly inhibited thrombosis and sensitized clopidogrel\u0026rsquo;s anti-thrombotic effect \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eATP1A1\u003c/em\u003e somatic mutations can lead to aldosterone-producing adenomas, causing excess aldosterone and high blood pressure \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eATP1A1\u003c/em\u003e genetic variant was associated with essential hypertension \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. An \u003cem\u003eATP1A1\u003c/em\u003e-related long non-coding RNA, \u003cem\u003eATP1A1-AS1\u003c/em\u003e, is implicated in the development of intracranial aneurysms by promoting smooth muscle cells phenotype switching and apoptosis \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCDH18\u003c/em\u003e, the VD GWAS significant locus tagged by rare variant, regulates differentiation towards vascular smooth muscle cells \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, and shows the most significantly upregulated protein expression in the good prognosis group of moyamoya disease, a rare cerebrovascular disorder \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eATP10A\u003c/em\u003e, the novel VD GWAS significant locus tagged by rare variant, showed a positive association with TDP-43 protein level, the accumulation of which in the central nervous system is a hallmark of frontotemporal lobar degeneration and amyotrophic lateral sclerosis \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eATP10A\u003c/em\u003e indicated lower expression in AD endothelial cells \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Gene-set enrichment analysis of VD GWAS highlighted the involvement of neurofibrillary tangle (GO:0097418), late endosome (GO:0005770), positive regulation of amyloid fibril formation (GO:1905908), NMDA glutamate receptor clustering (GO:0097114), regulation of amyloid fibril formation (GO:1905906), amyloid-beta clearance by cellular catabolic process (GO:0150094), negative regulation of amyloid fibril formation (GO:1905907). It is known that AD is characterised by both amyloid-β plaques and neurofibrillary tangles, which suggests that VD may coexist with AD. Interestingly, our genetic correlation analysis supported the statistically significant positive genetic correlation between VD and AD with \u003cem\u003erg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4469 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.75E-14. In fact, population study demonstrated that amyloid-β plaques and neurofibrillary tangles have the strongest association with dementia including AD, VD, or both \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, and midlife vascular risk factors was significantly associated with later-life elevated amyloid-β plaques \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Meanwhile, endosome dysfunction was involved in AD and other neurodegenerative diseases, and targeting endosome may be a strategy for treatment \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe further conducted a cross-trait GWAs meta-analysis of VD and AD, and identified 13 loci, 127 genes, and 53 pathways. It is noted that \u003cem\u003eGRM7\u003c/em\u003e is the only novel cross-trait GWAS locus that was not previously reported to be associated with VD or AD \u003csup\u003e22\u003c/sup\u003e. The subcellular-resolution spatial transcriptome atlas of the human prefrontal cortex revealed the increase of \u003cem\u003eGRM7\u003c/em\u003e expression in the severe AD group compared to the moderate AD group \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Biallelic \u003cem\u003eGRM7\u003c/em\u003e variants cause epilepsy, microcephaly, and cerebral atrophy \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eGRM7\u003c/em\u003e prevents glutamate release from pre-synaptic vesicles \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eGRM7\u003c/em\u003e variants predict the risk of schizophrenia and antipsychotic effect of seven common drugs \u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUsing scRNA-seq data, we demonstrated significantly differential expression of 241 VD genes including 24 VD GWAS loci and 9 cross-trait GWAS loci especially \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eCDH18\u003c/em\u003e, \u003cem\u003eATP1A1\u003c/em\u003e, and \u003cem\u003eSLC24A4\u003c/em\u003e. Interestingly, evidence supported the dysregulation of these genes in other neurological diseases. \u003cem\u003eCLU\u003c/em\u003e was significantly downregulated in dementia with Lewy bodies (DLB) microglia (Log\u003csub\u003e2\u003c/sub\u003eFC=-3.76 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=2.87E-08), and OPC (Log\u003csub\u003e2\u003c/sub\u003eFC=-1.42 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=3.08E-02), downregulated in Parkinson\u0026rsquo;s disease with dementia (PDD) excitatory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC=-1.34 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=1.30E-27), microglia (Log\u003csub\u003e2\u003c/sub\u003eFC=-1.36 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=4.12E-02), and OPC (Log\u003csub\u003e2\u003c/sub\u003eFC=-1.16 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=7.60E-04), upregulated in Parkinson\u0026rsquo;s disease (PD) excitatory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.91 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=5.85E-116), inhibitory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.75 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=8.03E-24), oligodendrocyte (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;1.15 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=3.86E-29) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, dopaminergic neuron (Log\u003csub\u003e2\u003c/sub\u003eFC=-1.58 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=1.12E-03), OPC (Log\u003csub\u003e2\u003c/sub\u003eFC=-1.12 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=3.40.E-02), and pericyte (Log\u003csub\u003e2\u003c/sub\u003eFC=-4.57 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=1.08.E-10) \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCDH18\u003c/em\u003e was significantly upregulated in DLB excitatory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;1.37 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=3.26E-08), and PDD excitatory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.94 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=3.00E-09), but downregulated in PD excitatory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC=-0.96 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=8.64E-15) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eATP1A1\u003c/em\u003e was significantly downregulated in DLB (astrocyte, excitatory neuron, inhibitory neuron, microglia, oligodendrocyte, OPC, vascular), PDD (astrocyte, excitatory neuron) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and dopaminergic neuron (Log\u003csub\u003e2\u003c/sub\u003eFC=-2.6 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=1.49.E-02) \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSLC24A4\u003c/em\u003e was significantly downregulated in DLB excitatory neuron (Log\u003csub\u003e2\u003c/sub\u003eFC=-0.66 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=4.20E-04) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Meanwhile \u003cem\u003eAPOE\u003c/em\u003e was significantly upregulated in dementia with DLB astrocyte (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;1.12 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=2.42E-38) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and PD microglia (Log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;1.748 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e=2.76.E-02) \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDrug-gene interaction analysis highlights \u003cem\u003eAPOE\u003c/em\u003e, \u003cem\u003eCDH18\u003c/em\u003e, \u003cem\u003eATP1A1\u003c/em\u003e, \u003cem\u003eHSD3B1\u003c/em\u003e, \u003cem\u003eNT5E\u003c/em\u003e, \u003cem\u003eATP10A\u003c/em\u003e, and \u003cem\u003eSLC24A4\u003c/em\u003e, as the potential therapeutic targets for VD (Supplementary Table\u0026nbsp;13). \u003cem\u003eAPOE\u003c/em\u003e is the target of 37 drugs especially FDA approved drugs for the treatment of AD including lecanemab \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e, donepezil \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, rivastigmine \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, and galantamine \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Importantly, randomized controlled trials (RCTs) demonstrated that donepezil and galantamine effectively improved cognition in VD patients with good safety and tolerability \u003csup\u003e\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCDH18\u003c/em\u003e indicated the strongest interaction (interaction score\u0026thinsp;=\u0026thinsp;26.25) with thiamine (vitamin B1). Two cross-sectional observational studies using data from the National Health and Nutrition Examination Survey (NHANES) showed that the increase in dietary intake of vitamin B1 contribute to better cognitive function in individuals aged over 60, and a decreased risk of stroke in older individuals \u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e,\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. In cognitively healthy and older Chinese individuals, there is a J-shaped association between dietary vitamin B1 intake and cognitive decline \u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. These findings highlight the potential importance of adequate dietary vitamin B1 intake to prevent cognitive decline and stroke in the aging population.\u003c/p\u003e \u003cp\u003e \u003cem\u003eATP1A1\u003c/em\u003e shows the strong interaction with almitrine (interaction score\u0026thinsp;=\u0026thinsp;10.50), which was approved to treat chronic obstructive lung disease. A meta-analysis of three RCTs showed that Duxil (a combination of almitrine and raubasine) significantly improved the cognitive function in VD patients measured by MMSE \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eATP10A\u003c/em\u003e showed a strong interaction with Duloxetine (interaction score\u0026thinsp;=\u0026thinsp;20.2), a type of antidepressant medicine to treat depression and anxiety \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. Recent study identified duloxetine as a highly potent selective competitive inhibitor of butyrylcholinesterase, which was involved in the regulation of the nervous system that affects memory and cognition, and may have positive effects on memory and cognitive functions in the elderly \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. A RCT demonstrated that duloxetine was effective to treat patients with moderate to severe central post-stroke pain \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSLC24A4\u003c/em\u003e indicated the strong interaction with salbutamol (interaction score\u0026thinsp;=\u0026thinsp;8.08), which was approved to treat asthma and COPD. Interestingly, salbutamol is effective at reducing the accumulation and rate of the tau protein formation \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. Our current findings broaden the potential therapeutic scope of the available drugs, and may offer potential as a new treatment for VD.\u003c/p\u003e \u003cp\u003eTissue enrichment analysis showed evidence of enrichment of VD heritability in lung and spleen, and VD\u0026thinsp;+\u0026thinsp;AD heritability in liver, blood, lung, spleen, and small intestine. In fact, large-scale GWAS showed that AD heritability was enriched in whole blood, spleen and lung \u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Population-based cohort studies from the Atherosclerosis Risk in Communities (ARIC) study \u003csup\u003e\u003cspan additionalcitationids=\"CR96\" citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e, the Rotterdam study \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e, the Rush Memory and Aging Project (MAP) \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e, Swedish National Study on Aging and Care in Kungsholmen (SNAC-K, 2001\u0026ndash;2004 to 2016\u0026ndash;2019) \u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e, and Health and Retirement study \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e collectively demonstrated that poor lung function increased the risk of both mild cognitive impairment (MCI) and dementia, accelerated progression from MCI to dementia, associated with AD pathology and cerebral vascular disease pathology, brain microvascular damage and global brain atrophy. Impaired lung function, defined as peak expiratory flow\u0026thinsp;\u0026lt;\u0026thinsp;80% predicted, was associated with a higher risk of dementia (HR\u0026thinsp;=\u0026thinsp;1.74, 95% CI:1.34\u0026ndash;2.25) \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Compared to those without impaired lung function, individuals with impaired lung function had 0.10 SD higher NfL and 0.09 SD higher p-Tau 181, which mediated 7.3% and 5% of the total effect of impaired lung function on dementia \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. A prospective cohort study of 431,834 non-demented individuals from the UK Biobank indicated that lung function decrease was associated with increased risk for all-cause dementia, AD and VD \u003csup\u003e102\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTogether, our large-scale GWAS meta-analysis and integrative analysis uncovered novel VD loci, genes and pathways. Our current findings demonstrated that the use of both common and rare genetic variants in large-scale VD GWAS could (1) enhance the ability to identify new loci, (2) identify rare variants of large effects, and (3) increase the proportion of VD heritability. These genetic findings provide valuable insights into the potential underlying mechanisms of VD and inform some potential clinically actionable drugs for the treatment of VD, which deserve further investigation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVD GWAS datasets\u003c/h2\u003e \u003cp\u003eWe selected four independent GWAS datasets from UKBB (European, 2,074 VD and 456,366 controls) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, FinnGen R12 (European, 3,624 VD and 475,484 controls) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, GH (South Asian, 119 VD and 43,659 controls) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and MGBB (European, South Asian, African, and Admixed American, 69 VD and 52,374 controls) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUKBB cohort represents a population-based, longitudinal study of over 500,000 volunteers aged 40\u0026ndash;70 years, recruited from England, Scotland, and Wales between 2006 and 2010 \u003csup\u003e103\u003c/sup\u003e. Extensive participant data were gathered through surveys, interviews, physiological assessments, and genetic profiling \u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. VD GWAS included 2,074 VD and 456,366 controls of non-Finnish European ancestry about 16,477,695 variants with position information from the Genome Reference Consortium Human Build 37 (GRCh37) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. VD were diagnosed using the available medical records, which were ascertained from Hospital Episode Statistics and recorded as International Classification of Disease version 10 (ICD-10) codes \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinnGen study encompasses six regional and three nationwide Finnish biobanks, with participants\u0026rsquo; health outcomes meticulously tracked through linkages to the comprehensive national health registries, spanning from birth to death \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Using ICD-9 and ICD-10 codes, 3,624 individuals were identified as VD cases, and 475,484 individuals were identified as controls in FinnGen R12 \u003csup\u003e16\u003c/sup\u003e. FinnGen VD GWAS included 21,306,039 genetic variants with position information from GRCh38 \u003csup\u003e16\u003c/sup\u003e. Here, we converted the position information from GRCh38 to GRCh37. After a rigorous quality control process to eliminate mismatched variants between GRCh38 and GRCh37, we obtained 13,092,808 genetic variants for meta-analysis.\u003c/p\u003e \u003cp\u003eGH is a community-based population genomics and health study comprising about 50,000 British individuals of South Asian ancestry (British Bangladeshi and British Pakistani) recruited in the United Kingdom including East London and Bradford \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. GH identified 119 VD cases and 43,659 controls using ICD-10, and included 37,686,810 genetic variants with position information from GRCh38 \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMGBB was established based on Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5\u0026nbsp;million patients. MGBB currently included 142,238 participants from European, South Asian, African, and Admixed American ancestries with 69 VD and 52,305 controls \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGWAS meta-analysis\u003c/h2\u003e \u003cp\u003eGWAS meta-analysis was performed using the fixed-effects IVW implemented by METAL, weighted by effect size and standard error (SE), with genomic control correction \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In Stage 1, a GWAS meta-analysis was performed in participants of European ancestry from UKBB and FinnGen R12. In stage 2, a GWAS meta-analysis was performed in participants of European, South Asian, African, and Admixed American ancestries from UKBB, FinnGen R12, GH and MGBB. In addition to METAL, a sensitivity GWAS meta-analysis using GWAMA (v2.2.2) was performed \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. To assess the genomic inflation, we calculated the genomic inflation factor (λ\u003csub\u003eGC\u003c/sub\u003e) using LDSC (v1.0.1) \u003csup\u003e18\u003c/sup\u003e. We defined the statistically significant SNPs using the genome-wide significant threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of genetic risk loci\u003c/h2\u003e \u003cp\u003eTo identify genetic risk loci from the GWAS meta‑analysis and perform functional annotation, we used FUMA v1.5.2 with ancestry-specific reference panels: the 1000 Genomes Phase 3 European panel for stage 1 European-specific GWAS meta-analysis, and the 1000 Genomes Phase 3 ALL panel for stage 2 cross-ancestry GWAS meta-analysis \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Initially, FUMA identified SNPs with a significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08 and LD threshold \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6 as the independent significant SNPs using LD-based clumping \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The independent genetic risk loci were characterized by considering all SNPs in LD (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.6) with one of the independent significant SNPs within a region of 250 kilobase (kb) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Within each genetic risk locus, FUMA further distinguished the lead SNPs, which are a subset of the independent significant SNPs in LD with each other (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) \u003csup\u003e20\u003c/sup\u003e. Each locus was represented by the lead SNP with the most significant \u003cem\u003eP\u003c/em\u003e value. Genetic loci were classified as novel if they were beyond a 1,000 kb from previously recognized loci \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The nearest gene corresponding to the lead SNPs in each locus was annotated using the get_nearest_gene () function from the gwasRtools package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/lcpilling/gwasRtools\u003c/span\u003e\u003cspan address=\"https://github.com/lcpilling/gwasRtools\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConditional analysis\u003c/h2\u003e \u003cp\u003eTo identify independent genetic signals at each genomic locus, we performed a step-wise conditional analysis of the VD GWAS meta-analysis summary statistics using GCTA (v 1.94.1) COJO analysis with the --cojo-slct function \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The parameters set for this function included a significance threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.00E-08, a distance of 10,000 kb, and a co-linearity threshold of 0.9. LD information was from the 1000 Genomes European reference panel \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHeritability analysis\u003c/h2\u003e \u003cp\u003eWe used LDSC (v1.0.1) to calculate the SNP-based heritability of VD GWAS meta-analysis summary statistics \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. LDSC is a computationally efficient tool that leverages GWAS summary statistics to estimate the SNP-based heritability and genetic correlation among multiple genetic traits, while accounting for potential sample overlap \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The SNP-based heritability measures the proportion of phenotypic variance explained by the additive effects of all common SNPs (i.e, the proportion of variance in disease liability due to genetic factors) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. LD scores are from the 1000 genomes phase 3 European reference panel (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.broadinstitute.org/alkesgroup/\u003c/span\u003e\u003cspan address=\"https://data.broadinstitute.org/alkesgroup/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using a population prevalence estimate 1.16% \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation\u003c/h2\u003e \u003cp\u003eFUMA offers a comprehensive annotation framework by integrating diverse external data sources including ANNOVAR \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, CADD \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, RegulomeDB \u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e and 15-core chromatin state \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e,\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e. Here, we conducted a functional annotation of all genome-wide significant SNPs (or independent significant SNPs) and their tagged SNPs with LD \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.6 using FUMA v1.5.2 and 1000 Genomes European reference panel \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGene mapping\u003c/h2\u003e \u003cp\u003eWe assigned the genome-wide significant loci to specific genes using positional mapping, eQTLs mapping, and chromatin interaction mapping implemented in FUMA v1.5.2 \u003csup\u003e20\u003c/sup\u003e. Positional mapping pinpointed protein-coding genes located within a 10 kb range of significant SNPs (either genome-wide significant or independently significant) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. eQTLs mapping assigned the significant SNPs to their corresponding protein-coding genes using significant eQTLs (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. eQTLs datasets are from the eQTLGen \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, Blood eQTL \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e, BIOS QTL \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e, GTEx v8 \u003csup\u003e109\u003c/sup\u003e, PsychENCODE \u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e, xQTLServer \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, CMC \u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e, eQTL catalogue \u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e and BRAINEAC \u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGene-based association test, gene set and tissue enrichment analyses\u003c/h2\u003e \u003cp\u003eWe performed the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of stage 1 VD GWAS meta-analysis summary data using MAGMA v1.08 \u003csup\u003e31\u003c/sup\u003e. MAGMA aggregates SNP-level association statistics into gene scores by mapping all SNPs from VD GWAS meta-analysis to 17,903 protein-coding genes using the SNP-wise mean model, genomic location and boundary information from human reference genome build 37, and the ancestry-matched LD information from the 1000 Genomes Project phase 3 reference panel \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Finally, a gene-based association score was calculated by the aggregate of all SNPs inside each gene \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. MAGMA performed a gene set enrichment analysis through competitive analysis to identify the genes in a gene set that are more strongly associated with the phenotype of interest than other genes \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Here, we focused on 16,228 GO terms including biological processes, cellular components and molecular functions from the Molecular Signatures Database (MSigDB) (v7.0, version 2025.1.Hs) \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e. MAGMA determined whether VD heritability is enriched in specific tissues by integrating the stage 1 VD GWAS meta-analysis summary data with gene expression data from 30 GTEx v8 tissues \u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e. BH-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for gene-based association test, gene set enrichment analysis and tissue enrichment analysis.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eGenetic association between VD and lung function traits\u003c/h2\u003e \u003cp\u003eUsing LDSC default parameters and the precomputed LD scores from the 1000 Genomes European reference panel \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, we performed a genetic association analysis to investigate the genetic correlation between VD (stage 1) and 6 lung function traits including FEV1, FVC, FEV1/FVC, PEF, asthma and COPD using large scale GWAS summary datasets from UKBB \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, as provided in Supplementary Table\u0026nbsp;18. We defined the statistically significant genetic association using the Bonferroni-corrected threshold \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;8.33E-03 (0.05/6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003ePolygenic Priority Score\u003c/h2\u003e \u003cp\u003eTo pinpoint the most likely causal genes at VD GWAS loci, we calculated the polygenic priority score using PoPS (v.0.2) and the gene-level results from MAGMA analysis of stage 1 VD GWAS meta-analysis summary statistics \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. PoPS computes gene-level z-scores from GWAS summary statistics with an LD reference panel using MAGMA, and learns trait-relevant gene features from cell-type specific gene expression, biological pathways, and protein-protein interactions \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In each genome-wide significant locus, genes within \u0026plusmn;\u0026thinsp;1 Mb of the lead variant were assigned a polygenic priority score. Within each region, genes ranking in the top 10% of the polygenic priority scores indicate a higher probability of being causal for VD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eTranscriptome-wide association study\u003c/h2\u003e \u003cp\u003eWe performed a TWAS to identify the genes whose expression levels are significantly associated with VD using FUSION v3 \u003csup\u003e33\u003c/sup\u003e. TWAS leverages the correlation between genotype and expression to identify eQTLs that modulate gene expression and are associated with the phenotype of interest \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Using FUSION v3, we integrated the VD GWAS meta-analysis dataset with the gene expression data from GTEx v8 in human tissues and cell types, a whole blood eQTLs dataset from YFS (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,264) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and a brain dorsolateral prefrontal cortex eQTLs dataset from CMC (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;452) \u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. A Benjamini \u0026amp; Hochberg FDR-corrected threshold of 0.05 was considered statistically significant for each dataset (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eColocalization analysis\u003c/h2\u003e \u003cp\u003eUsing COLOC implemented in FUSION v3, we performed a Bayesian colocalization analysis to identify a subset of TWAS significant genes that had the same single variant associated with both VD and gene expression with a high posterior probability (PP) \u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. Basically, COLOC calculated five kinds of PPs \u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. The PP for the null hypothesis (PP.H\u003csub\u003e0\u003c/sub\u003e): neither trait has a genetic association in the region, PP for the first alternative hypothesis (PP.H\u003csub\u003e1\u003c/sub\u003e): only trait 1 has a genetic association in the region, PP for the second alternative hypothesis (PP.H\u003csub\u003e2\u003c/sub\u003e): only trait 2 has a genetic association in the region, PP for the third alternative hypothesis (PP.H\u003csub\u003e3\u003c/sub\u003e): both traits are associated, but with different causal variants, PP for the fourth alternative hypothesis (PP.H\u003csub\u003e4\u003c/sub\u003e): both traits are associated and share a single causal variant \u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e. A PP.H\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.70 indicated evidence of colocalization \u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eSummary-data-based Mendelian randomization\u003c/h2\u003e \u003cp\u003eSMR is a complementary method of TWAS to verify the causal role of TWAS genes \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Here, we used SMR v1.3.1 to integrate the VD GWAS meta-analysis dataset with multiple large-scale eQTLs datasets from GTEx tissues \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, a large blood eQTLs dataset from the eQTLGen consortium including 31,684 human blood samples \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and a brain eQTLs dataset including 2,865 human brain cortex samples \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. We defined the statistically significant SMR genes using \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05 and the heterogeneity in dependent instruments (HEIDI) test for pleiotropy\u0026thinsp;\u0026gt;\u0026thinsp;0.01 \u003csup\u003e118\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCross-trait meta-analysis of VD and AD\u003c/h3\u003e\n\u003cp\u003eWe selected the largest clinically diagnosed AD GWAS in individuals of European ancestry from the International Genomics of Alzheimer\u0026rsquo;s Project (IGAP) stage 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63,926) including a total of 9,456,058 common variants and 2,024,574 rare variants \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Using LDSC default parameters and the precomputed LD scores from the 1000 Genomes European reference panel \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, we first estimated the genetic correlation between VD (stage 1) and AD \u003csup\u003e22\u003c/sup\u003e. We further conducted a cross-trait meta-analysis of VD and AD GWAS datasets using a fixed-effects IVW method implemented by METAL \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Meanwhile, we performed a sensitivity cross-trait meta-analysis of AD and VD GWAS datasets using MTAG (1.0.8) \u003csup\u003e41\u003c/sup\u003e. MTAG extends the standard single-trait GWAS by jointly analyzing multiple genetically related traits, thereby increasing the statistical power to detect pleiotropic loci while accounting for sample overlap \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. To identify robust cross-trait associations, our analysis focused on SNPs exhibiting: (i) concordant effect directions (identical risk alleles) across both diseases; (ii) a meta-analysis \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08, reflecting association with the cross-trait phenotype; and (iii) a single-trait \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each trait independently. Independent genomic loci were then defined using FUMA. We also conducted the gene-based association test, gene set enrichment analysis, and tissue enrichment analysis of the cross-trait GWAS meta-analysis summary data using MAGMA \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eGene prioritization\u003c/h2\u003e \u003cp\u003eTo prioritize the most probable causative genes for VD, we integrated 29 lines of evidence including GWAS significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5.00E-08), gene mapping (position mapping, eQTLs mapping, and chromatin interaction mapping), variant annotation (exonic SNPs, CADD/RDB/pLI), gene level association test (MAGMA, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05), PoPS (top 10%), TWAS (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05), colocalization analysis (COLOC, \u003cem\u003ePPH\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u0026gt;0.70), SMR (eQTLs/sc-eQTLs, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05). For each gene, the prioritization score is the sum of multiple lines of evidence by counting \u0026ldquo;0\u0026rdquo; if the gene was \u0026lsquo;not prioritized\u0026rsquo; and as \u0026ldquo;1\u0026rdquo; if the gene was \u0026lsquo;prioritized\u0026rsquo;. This approach ensured that genes with stronger cumulative evidence were more likely to be causally associated with VD, and reflected a higher degree of confidence in the VD etiology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis in human brain cells\u003c/h2\u003e \u003cp\u003eWe performed a differential gene expression analysis of VD risk genes using snRNA-seq data in human periventricular white matter from 5 VD patients with lesion, 5 VD patients adjacent to the lesion, and 5 normal control subjects (GEO accession: GSE213897) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, 4 VD patients and 4 age and sex-matched healthy controls (GEO accession: GSE282111) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Using \u0026ldquo;Seurat\u0026rdquo; package, we preprocessed and transformed the raw scRNA-seq data, excluding cells with fewer than three genes and fewer than 50 unique features counted per cell. Subsequently, we used the NormalizeData and ScaleData functions to normalize and scale the RNA transcripts per million (TPM). Using \u0026ldquo;SingleR\u0026rdquo; package, we annotated the cell types, which were classified into 6 primary cell types including astrocytes, endothelial cells, microglia, neurons, oligodendrocytes and oligodendrocyte precursor cells (OPCs), as well as 30 clusters \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Finally, we conducted the differential expression analysis using Wilcoxon rank sum test. In a specific cell type, we defined the significantly differential expression in VD patients compared to normal controls with |Log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;0.5 and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eFDR\u003c/em\u003e\u003c/sub\u003e\u0026lt;0.05.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eDrug-gene interaction analysis\u003c/h2\u003e \u003cp\u003eTo assess whether VD risk genes could serve as potential therapeutic targets, we performed a drug-gene interaction analysis using DGIdb v5.0 \u003csup\u003e44\u003c/sup\u003e. DGIdb v5.0 is an online database that integrates information from drug\u0026ndash;gene interaction databases (accessed December 2024) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. DGIdb contains over 10,000 genes and 20,000 drugs involved in nearly 70,000 drug-gene interactions or belonging to one of 43 potentially druggable gene categories \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. A interaction score is used to rank results in an interaction search result set \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data underlying the findings are fully available without restriction. GWAS summary statistics of UK Biobank is available at https://www.ebi.ac.uk/gwas (GCST90473240); GWAS summary statistics of FinnGen is available at https://r12.finngen.fi/; The Mass General Brigham Biobank (MGBB) GWAS summary statistics is available at https://api.kpndataregistry.org/api/d/BPEif3; Genes \u0026amp; Health (GH) GWAS summary statistics is available at https://www.genesandhealth.org/research/gwas-data-downloads; The 1000 Genomes Phase 3 ancestry-specific LD reference are obtained from the FUMA website at https://fuma.ctglab.nl; TWAS weights are available at http://gusevlab.org/projects/fusion/; eQTL summary datasets used for SMR are available at https://yanglab.westlake.edu.cn/software/smr/#eQTLsummarydata; Single cell gene expression datasets are available at GSE213897 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213897) and GSE282111 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE282111). AD GWAS summary statistics is available at https://www.niagads.org/igap-rv-summary-stats-kunkle-p-value-data, GWAS summary statistics of forced expiratory volume in 1-second (FEV1) is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/continuous-20150-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of forced vital capacity (FVC) is available at https://pan-ukb-us-east-1.s3.amazonaws.com/ sumstats_flat_files_tabix/continuous-20151-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of FEV1/FVC is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/continuous-FEV1FVC-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of peak expiratory flow (PEF) is available at https://pan-ukb-us-east-1.s3.amazonaws.com/ sumstats_flat_files_tabix/continuous-3064-both_sexes-irnt.tsv.bgz.tbi, GWAS summary statistics of asthma is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/phecode-495-both_sexes.tsv.bgz.tbi, GWAS summary statistics of COPD is available at https://pan-ukb-us-east-1.s3.amazonaws.com/sumstats_flat_files_tabix/categorical-22130-both_sexes-22130.tsv.bgz.tbi. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo custom code was used in this study. As detailed in the Methods section, we used previously released software to generate the analysis and cite them throughout the manuscript references. METAL (March 25, 2011 release) is available at https://csg.sph.umich.edu/abecasis/Metal/; GWAMA (v2.2.2) is available at https://genomics.ut.ee/en/tools; FUMA (1.5.2) is available at https://fuma.ctglab.nl/; LocusZoom is available at http://locuszoom.sph.umich.edu/; MTAG (v1.0.8) is available at https://github-wiki-see.page/m/JonJala/mtag/; GCTA-COJO (v1.94.1) is available at https://yanglab.westlake.edu.cn/software/gcta/; MAGMA (v1.08) is available at https://cncr.nl/research/magma/; LDSC is available at https:// github.com/bulik/ldsc; PoPS (v0.2) is available at https://github. com/FinucaneLab/pops; FUSION (v3) is available at http://gusevlab.org/projects/fusion/; SMR (v1.3.1) is available at https://yanglab.westlake.edu.cn/software/smr/; DGIdb (v5.0): https://beta.dgidb.org/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article involves human individuals from prior investigations. All individuals provided informed consent in all of the corresponding original investigations, as reported in the Materials and methods. Our analysis is based on publicly available, large-scale datasets rather than individual-level data. Thus, ethical approval was not sought.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper. The authors confirm that all data underlying the findings are either fully available without restriction through consortia websites, or may be made available from consortia upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by funding from the National Key Research and Development Program of China (Grant No. 2023YFC3605200), Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 2023ZD0505300, 2023ZD0505302), National Natural Science Foundation of China (Grant No. 82471449).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. G.Y.L. conceived and initiated the project. G.Y.L. and G.S analyzed the data and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoman, G.C., Erkinjuntti, T., Wallin, A., Pantoni, L. \u0026amp; Chui, H.C. Subcortical ischaemic vascular dementia. \u003cem\u003eLancet Neurol\u003c/em\u003e 1, 426\u0026ndash;36 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkat, P., Chopp, M. \u0026amp; Chen, J. 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Here, we firstly performed the largest cross-ancestry genome-wide association study meta-analysis comprising 5,886 VD and 1,027,883 controls of European, East Asian, South Asian, African, and Admixed American ancestry. We identified 37 genome-wide significant loci including \u003cem\u003eCLU\u003c/em\u003e and \u003cem\u003eAPOE\u003c/em\u003e tagged by common variants and 35 loci tagged by rare variants, and demonstrated enrichment of VD heritability in lung and genetic association between VD and lung function traits. We further conducted a cross-trait of VD and Alzheimer\u0026rsquo;s disease, and identified 13 genome-wide significant loci including \u003cem\u003eCR1\u003c/em\u003e, \u003cem\u003eBIN1\u003c/em\u003e, \u003cem\u003eGRM7\u003c/em\u003e, \u003cem\u003eHLA-DRA\u003c/em\u003e, \u003cem\u003eTREM2\u003c/em\u003e, \u003cem\u003eCLU\u003c/em\u003e, \u003cem\u003eECHDC3\u003c/em\u003e, \u003cem\u003eAGBL2\u003c/em\u003e, \u003cem\u003eMS4A4E\u003c/em\u003e, \u003cem\u003ePICALM\u003c/em\u003e, \u003cem\u003eSLC24A4\u003c/em\u003e, \u003cem\u003eABCA7\u003c/em\u003e, and \u003cem\u003eAPOE.\u003c/em\u003e A multi-omics integrative analysis identified 619 genes. 241 genes were significantly differentially expressed in VD cells and 21 exhibited strong evidence of interaction with FDA-approved drugs. Collectively, our findings provide valuable insights into the potential underlying mechanisms of VD.\u003c/p\u003e","manuscriptTitle":"Genome-wide cross-trait analysis of vascular dementia and Alzheimer’s disease highlights novel loci and lung-brain axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 05:33:32","doi":"10.21203/rs.3.rs-8988189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-21T06:27:04+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-20T17:35:24+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-04-17T23:35:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T15:23:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T11:45:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2026-02-27T12:49:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9e0ee09-d3f7-456b-86c3-aff46e14e37e","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63784000,"name":"Biological sciences/Genetics"},{"id":63784001,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-17T23:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 05:33:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8988189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8988189","identity":"rs-8988189","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}