Elucidating the Multitrait Association between Parkinson’s Disease and Respiratory Disorders: HLA gene complex as a causal nexus

Elucidating the Multitrait Association between Parkinson’s Disease and Respiratory Disorders: HLA gene complex as a causal nexus | 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 Research Article Elucidating the Multitrait Association between Parkinson’s Disease and Respiratory Disorders: HLA gene complex as a causal nexus Xinhao Wang, Jintao Li, Zihao Wang, Zifeng Huang, Xiaobo Wei, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7234326/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Respiratory disorders are gaining recognition as common comorbidities in Parkinson's disease (PD) patients, and these comorbidities have significant implications for PD patient outcomes and mortality. However, the genetic basis and potential causal relationships between PD and respiratory dysfunction remain unclear. Understanding these associations could provide insights into shared pathophysiological mechanisms and identify potential therapeutic targets. Method We conducted a genetic association study using large-scale genome-wide association study (GWAS) summary data for PD (n = 482,730), lung function (n = 321,047), chronic obstructive pulmonary disease (COPD; n = 325,027), idiopathic pulmonary fibrosis (IPF;n = 953,873), obstructive sleep apnoea (OSA;n = 159,255) and asthma (n = 1,376,071) in individuals of European ancestry. We employed Mendelian randomization (MR), colocalization and summary data-based Mendelian randomization (SMR) analysis to evaluate potential causal relationships and identify shared genetic loci. Besides, we conductedsingle-cell RNA sequencing (scRNA-seq) and enrichment analysis to investigate cell type-specific gene expression patterns and their potential roles in PD and respiratory disorders. Result MR indicates that obstructive ventilatory dysfunction predicts greater motor impairment, whereas restrictive ventilatory dysfunction predicts cognitive decline in PD. Genetically predicted PD increases IPF risk (odds ratio [OR] = 1.14) and reduce the risk of OSA (OR = 0.97). Colocalization identifies 26 loci with shared causal variants; the HLA-DQA1 and HLA-DQB1 genes emerge as key candidates. SMRlinks coupled with expression quantitative trait loci from lung, blood and brain regions demonstrates that altered expression of these genes is associated with disease risk. Single-cell RNA sequencing of peripheral blood mononuclear cells and substantia nigra pars compacta samples shows distinct expression patterns of HLA-DQA1 and HLA-DQB1 in B cells, T cells and microglia from patients with PD and COPD. Enrichment analyses implicate major histocompatibility complex class II binding, T-cell activation and pro-inflammatory cytokine production. Conclusion We conducted a multitrait analysis focusing on PD and respiratory disorder traits, and further identified two shared causal variants that are prioritized between these traits. These findings suggest that shared genetic mechanisms underlie PD and respiratory disorders, highlighting the potential immunomodulatory role of the HLA gene complex and its interactome in mediating these associations. Parkinson’s disease respiratory disorders genetic association human leukocyte antigen neuroinflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Our and other previous studies have shown that Parkinson’s disease (PD) presents motor (e.g., bradykinesia, rigidity, rest tremor, gait disturbances) and nonmotor (e.g., impaired olfaction, rapid eye movement sleep behaviour disorder, and constipation) features [1,2,3] , in which neuroinflammation and immunomodulation play crucial roles [4,5]. Importantly, in addition to the well-documented symptoms, respiratory dysfunction is also an observed symptom of PD [6]. Respiratory dysfunction has been associated with PD since its initial documentation and is a well-established predictor of mortality and morbidity in PD patients. Moreover, pneumonia is frequently cited as the leading cause of death in PD patients, and a review of hospital admissions for individuals with PD revealed that 33% of admissions were due to respiratory system diseases. In addition to aspiration pneumonia, there are four main types of respiratory disorders associated with PD: airway obstruction, restrictive respiratory dysfunction, obstructive sleep apnoea (OSA) and asthma [7,8]. Importantly, an observational study has shown that significant differences in all commonly examined lung function parameters are already present in patients in the early stages of PD compared with controls, underscoring that pulmonary function begins to deteriorate at the very onset of the disease [9]. Despite growing attention to the associations between respiratory disorders and PD, the underlying mechanisms remain poorly understood. In this study, we examined the relationships between PD and lung function, as assessed by forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), peak expiratory flow (PEF), and the FEV1/FVC ratio. We also investigated the associations between PD and respiratory disorders, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), OSA and asthma. To achieve this goal, we utilized large-scale genome-wide association study (GWAS) summary data, encompassing 482,730 individuals for Parkinson`s disease and more than 1.3 million individuals for respiratory disorders, and employed various statistical genetic approaches to investigate pleiotropic associations sequentially from the gene level to the biological pathway level. This approach helped elucidate the underlying shared genetic aetiology between PD and respiratory disorders. By elucidating the genetic relationship between PD and respiratory disorders, we also identified specific genomic regions of interest especially like HLA-DQA1 and HLA-DQB1 genes and their potential immunomodulation role for future molecular studies. Methods GWAS summary statistics This study analyzed summary-level data from six European-ancestry datasets. For PD onset risk, data from the Medical Research Council Integrative Epidemiology Unit (MRC IEU) OpenGWAS database were used, including 15,056 cases, 18,618 proxy cases, and 449,056 controls [10]. PD progression data were derived from a GWAS analysis by Iwaki et al., covering 25 clinical phenotypes of 4,093 PD patients across 12 cohorts [11]. GWAS summary data on lung function were obtained from a meta-analysis by Shrine N et al., based on the UK Biobank and SpiroMeta Federation, comprising 321,047 individuals [12]. COPD data were sourced from a GWAS analysis by Cosentino J et al., using the UK Biobank database, with 325,027 individuals [13]. IPF and asthma data were from a meta-analysis by Wei Zhou et al., covering nine cohorts, with 953,873 individuals for IPF and 1,376,071 individuals for asthma [14]. OSA data were from a meta-analysis by Campos AI et al., based on five cohorts, with 159,255 individuals. All studies were conducted between 2019 and 2023 [15]. The study design is presented in Figure 1 , and the characteristics of the GWAS summary statistics data are shown in Table S1 in Supplementary Tables . All analyses were conducted between September 2024 and May 2025. This genome-wide pleiotropic association study followed the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guidelines. Statistical analysis Mendelian randomization (MR) and sensitivity analyses Instrumental variables (IVs) for both phenotypes were selected by filtering for variants achieving genome-wide significant variants (p < 5× 10 −8 ). The remaining variants were clumped using an R 2 threshold of 0.001 and a distance of 10,000 kilobase pairs to ensure independence. To generate and confirm strong instruments for the exposures, F statistics were calculated for each IV, with values above 10, the recommended threshold for determining strong IVs. A P value less than 0.05 was considered statistically significant. The primary MR analysis was performed using the inverse variance weighted (IVW) method [16]. The MR-Egger [17] and weighted median methods [18] were utilized to complement and enhance the reliability of the MR findings. Egger intercept analysis was used to test for horizontal pleiotropic effects, with a p value less than 0.05 considered indicative of possible horizontal pleiotropy. MR-Egger and IVW methods using Cochran's Q statistics were employed to identify potential heterogeneity among the single-nucleotide polymorphisms (SNPs), with a p value less than 0.05 suggesting potential heterogeneity [19]. Leave-one-out sensitivity analysis was conducted to determine whether the causal effect was driven by single SNPs. Significant changes in causal effects after excluding any specific SNP indicated the presence of heterogeneity [20]. The MR-PRESSO test [21] was also used to address directional pleiotropy by removing outliers. Additionally, the Steiger test [22] was applied to confirm the effect direction from exposure to outcome. Finally, a funnel plot was used to detect directional pleiotropy. The asymmetry of the plot indicated strong effects of certain SNPs on the outcome, despite their low precision, suggesting the potential presence of pleiotropy. These analyses were conducted using the TwoSampleMR R package (version 0.6.8) within the R software environment (version 4.4.3). Genetic correlation analysis To quantify the genetic correlation between PD and seven respiratory traits at the whole-genome level, we utilized the linkage-disequilibrium score regression method (LDSC) [23] and high-definition likelihood (HDL) [24]. We performed LDSC using well-imputed HapMap3 variants and precomputed LD scores of European ancestry from the 1000 Genomes Project Phase 3. We executed HDL using the R package HDL-v1.4.0. Gene-level analysis We conducted pleiotropic analysis under the composite null hypothesis (PLACO) [25] analyses on PD and respiratory disorder traits (COPD, IPF, OSA and asthma) to detect the pleiotropic associations between genetic variants and traits by considering a composite null hypothesis. For each trait pair, we denoted trait1 and trait2 as PD and respiratory disorder, beta trait1 and beta trait2 as the effect sizes of an SNP on two traits, and Ztrait1 and Ztrait2 as the observed Z scores of an SNP from corresponding GWAS summary data. Rejection of H0 statistically implied that the SNP could be a potential pleiotropic variant shared between two traits. Overlapping SNPs between GWASs of each pairwise trait were included, and the summary statistics were harmonized to align with the same effect allele. SNPs with squared Z scores above 80 were excluded, as extremely large effect sizes might generate false signals [25]. To account for potential sample overlap, we decorrelated the Z scores using the correlation matrix estimated from GWAS summary statistics. The generated GWAS results were subsequently subjected to FUMA analysis (https://fuma.ctglab.nl/snp2gene) [26] to characterize potential pleiotropic genes between two diseases by setting the P PLACO value to 0.2 in a single genetic locus. We first performed gene-level Multi-marker Analysis of GenoMic Annotation (MAGMA) [27] on the genes located in or overlapping with the pleiotropic loci on the basis of both the PLACO and single-trait GWAS results to identify candidate pleiotropic genes. MAGMA gene IDs and locations of 19,427 protein-coding genes identified using NCBI build 37.3 were obtained from the CTG-CNCR [27] website. Significance was declared at both the locus-specific Bonferroni-corrected p value < 0.05 for MAGMA analysis of the PLACO results and p values < 0.05 for both MAGMA analyses on the basis of the original single-trait GWAS results of patients with corresponding PD and respiratory disorders. Colocalization analysis We performed colocalization using COLOC [28], a Bayesian statistical method designed to assess the probability of two phenotypes sharing a causal variant within a predefined genomic region. Within the colocalization framework, posterior probabilities are evaluated for five hypotheses: H₀: No causal variant exists in the region for either phenotype. H₁: A causal variant exists in the region for the first phenotype. H₂: A causal variant exists in the region for the second phenotype. H₃: Distinct causal variants exist for each trait within the region. H₄: Both traits share a causal variant. Colocalization analysis was conducted using the COLOC-reporter pipeline [29]. COLOC-reporter extracts variants within user-defined genomic regions and calculates the LD matrix for this region from a user-defined reference panel. In our study, the panel consisted of samples from patients of European ancestry from the 1000 Genomes Phase 3 trial (N = 503). It then harmonizes the summary statistics to match the allele order of the reference panel, flipping effect directions accordingly. The observed versus expected z scores are assessed using the diagnostic tools provided in the susie [30] R package. The z score outliers are omitted. Following this quality control, susie fine-mapping is subsequently conducted to identify 95% credible sets in these regions for each phenotype. For both phenotypes, the identification of credible sets allows for relaxation of the single causal variant assumption. All possible credible sets are then assessed pairwise for a shared signal between phenotypes, which improves the resolution for colocalization inference in regions containing multiple signals. If no 95% credible set is identified or only identified for one phenotype, colocalization under the single causal variant assumption is performed using coloc [31]. A posterior probability of ≥80% for H₃+H₄ is considered strong evidence of colocalization between two traits (PP.H₃+PP. H₄ ≥ 0.8). A posterior probability of ≥60% for H₃+H₄ is considered possible evidence of colocalization between two traits (PP.H₃+PP. H₄ ≥ 0.6). The SuSiE model assumes at most ten causal variants (L = 10) per credible set. We used default priors (P1 = 1×10⁻⁴, P2 = 1×10⁻⁴, P12 = 5×10⁻⁶). Summary data-based M endelian randomization (SMR) We applied SMR [32] and heterogeneity in dependent instruments (HEIDI) using SMR software v1.3.1 to investigate whether the expression of the shared PD and respiratory disorders genes identified in lung, blood and brain tissue data via MAGMA were causally associated with disease risk. SMR was performed by integrating summary eQTL data from GTEx version 8 [33] (SNPs within 1 Mb of the transcription start site with P<1×10 −5 ) and summary statistics from MAGMA. SMR integrates expression quantitative trait loci (eQTLs) and GWAS summary data within an MR framework to identify genes whose expression levels are linked to a phenotype of interest through pleiotropy. The resulting associations can be interpreted as assessing whether the effect of a variant on a phenotype is mediated through gene expression. Single-cell and single-nuclei data acquisition We retrieved data from Gene Expression Omnibus (GEO) [34] single-cell RNA datasets of peripheral blood mononuclear cells (PBMCs) from 4 patients with Parkinson’s disease and 2 matched healthy controls [35], PBMCs from 8 patients with COPD and 7 matched healthy controls [36], PBMCs from 25 patients with IPF and 13 matched healthy controls [37], and postmortem substantia nigra pars compacta (SNpc) samples from 15 patients with PD and 14 healthy controls [38]. The unique molecular identifier (UMI) count matrix was processed utilizing the Seurat [39] R package (version 2.3.4). Single-cell RNA-seq data analysis pipeline We employed a comprehensive single-cell RNA-seq data analysis workflow implemented in R using the Seurat [39] package. To ensure the quality and reliability of the single-cell RNA sequencing data, we retained cells with a total molecule count between 0 and 50000, numerous detected genes between 0 and 4000, a percentage of mitochondrial genes less than 15%, and a percentage of ribosomal genes less than 40%. We normalized the data using the log normalization method and identified highly variable genes to focus our subsequent analyses on the most informative features. We conducted dimensionality reduction through principal component analysis (PCA) and corrected for batch effects using the Harmony method. On the basis of gene expression profiles, we clustered the cells and identified marker genes for each cluster to characterize the cell populations. Then, we visualized the results using uniform manifold approximation and projection (UMAP) plots to clearly illustrate the cell clusters and their relationships. Finally, following clustering, we used the mLLMCelltype [40] algorithm to perform a cell-type prediction on the cell clusters. Differential gene expression analysis Differential gene expression (DGE) analysis was performed separately for each cell-specific sample for all candidate genes detected via MAGMA gene analysis. We examined the expression of the genes in the scRNA-seq data and included only genes expressed in at least one cell type of the examined tissues. We followed a generalized linear mixed model approach implemented in the MAST [41] R package after excluding genes expressed in fewer than 10% of the cells. Units for differential expression were defined as the log2-fold change (log2FC) per unit change in the respective contrast. We considered genes with a log2FC ≥ 0.25 and a adjusted p value < 0.05 as significantly differentially expressed. Weighted gene coexpression network and enrichment analysis We utilized the high-dimensional weighted gene coexpression network analysis (hdWGCNA) [42] R package to construct cell type-specific coexpression networks for the selected candidate genes. Initially, we applied the k-nearest neighbours’ algorithm to identify groups of similar cells on the basis of their transcriptomic profiles, creating metacells. We subsequently generated a metacell gene expression matrix to serve as the foundation for constructing the coexpression network. The network was built using the lowest soft power threshold that achieved a scale-free topology model fit of at least 0.8, ensuring robust network construction. In this process, genes that did not group into any coexpression module were excluded from further analysis and assigned to the "grey" module. Following the construction of the coexpression network, we conducted a pathway enrichment analysis using the clusterProfiler [43] R package, focusing on gene sets with at least 20 genes to ensure meaningful biological insights. To further explore the interactions among the genes within these modules, we utilized the STRINGdb [44] R package to analyse the full protein‒protein interaction network data from the STRING database, aiming to uncover potential functional relationships and pathways. Results Associations between genetically predicted lung function and PD risk and progression Our MR results demonstrated no significant bidirectional effect of lung function on the risk of PD. However, in the primary inverse-variance weighted (IVW) MR analysis, a genetic predisposition to lower FEV1/FVC was also positively associated with motor impairment in PD patients, as indicated by higher HY3 staging and higher UPDRS III scores ( Figure 2 and Table S2 in Supplementary Tables ). Specifically, a lower FEV1/FVC was associated with an increased likelihood of HY3 staging (odds ratio [OR] = 0.362, 95% confidence interval [CI] = 0.155–0.846, p = 0.019) and higher UPDRS III scores (OR = 0.807, CI = 0.664–0.980, p = 0.030). Additionally, a genetic predisposition to lower FVC was positively associated with cognitive impairment in PD patients, as indicated by lower MMSE scores (OR = 0.583, CI = 0.341–0.996, p = 0.048). Associations between genetically predicted respiratory disorders and PD risk and progression Our MR results demonstrated a significant causal effect of PD on the risk of IPF and OSA ( Figure 2 and Table S 3 in Supplementary Tables ). According to the primary IVW MR analysis, a genetic predisposition towards a greater risk of PD was causally associated with an increased risk of developing IPF (OR = 1.138, CI = 1.040–1.257, P = 0.0048) and a decreased risk of developing OSA (OR = 0.972, CI = 0.945–0.998, P = 0.0402). However, there was no clear association between genetically predicted lung function and the risk of chronic obstructive pulmonary disease (COPD) in this study. Additionally, we did not have sufficient instrumental variables to determine the relationship between PD and asthma. With respect to PD progression, we observed a statistically significant association between COPD and an increased risk of more severe motor impairment in PD patients, as indicated by higher UPDRS III scores. Additionally, we found a statistically significant association between IPF and an increased risk of cognitive impairment in PD patients, as evidenced by a greater risk of dementia and lower MMSE and MOCA scores. Our findings indicate that OSA is associated with a range of nonmotor symptoms, including olfactory dysfunction, cognitive impairment, and higher UPDRS IV scores, in PD patients ( Figure 2 and Table S 3 in Supplementary Tables ). In sensitivity analyses, the zero-intercept test of MR‒Egger regression revealed no evidence of horizontal pleiotropy across SNPs for the associations between PD risk and COPD and between PD progression and respiratory trait ( Table S 2 and S3 in Supplementary Tables ). In addition, there was no heterogeneity in causal estimates according to MR‒Egger or IVW methods using Cochran’s Q statistics ( Table S 2 in Supplementary Tables ). For the associations between PD and IPF and OSA, while the MR-PRESSO test detected significant horizontal pleiotropy, both the MR‒Egger regression ( Table S 2 in Supplementary Tables ) and the leave-one-out sensitivity analysis ( Figure S 1 in Supplementary Figures ) indicated that the impact of this pleiotropy on the causal effect estimates was minimal. This suggests that the causal effect estimates are robust. Genetic correlations We examined the bivariate genetic correlation between PD and respiratory traits using LDSC and HDL. There was no strong genetic correlation between PD and respiratory traits. PD and asthma show significant weak genetic correlation ( Table S 4 in Supplementary Tables ). Shared loci between PD and respiratory disorders between PD and A total of 1211 SNPs were identified as potential pleiotropic variants between PD and respiratory disorders by PLACO. FUMA identified 16 independent genomic risk loci as pleiotropic loci, involving 13 unique chromosomal regions. ANNOVAR category annotation revealed that 51.9% of the variants (629 of 1211 SNPs) were intronic and that 24.7% of the variants were intergenic (300 of 1211 SNPs).( Table S 6 in Supplementary Tables ) MAGMA analysis of potential pleiotropic genes located in or overlapping 83 pleiotropic loci revealed 26 significant pleiotropic genes. Among these pleiotropic genes,HLA-DQA1 was concurrently detected in COPD, IPF, and asthma trait pairs. ( Table 1 and Figure S2 in Supplementary Figures ). Using the colocalization method on 26 MAGMA-reported loci, we identified 12 loci that colocalized between PD and respiratory disorders traits with a PPH3+PPH4 (posterior probability)>0.8 ( Figure S5 and Table S7 in Supplementary Tables ). Most colocalized loci were found between PD patients and COPD patients (5 loci). Among the 12 loci with evidence for colocalization, two loci for which a single candidate causal variant explained a large proportion of the association was mapped in SETDA (position: chr16:30968615-30996437; colocalized with the PD-COPD trait; PPH4= 0.9104; Figure S4 in Supplementary Figures ) and PIGL (position: chr17:16120505-16252115; colocalized with the PD-asthma trait; PPH4= 0.8058; Figure S4 in Supplementary Figures ). Two loci were suggestive of colocalization (PIGL colocalized with PD-IPF, and CNTN1 colocalized with PD-OSA, PPH3+PPH4>0.6). Validation of expression of the pleiotropic causal genes shared between PD and respiratory disorders To further validate the associations of the significant pleiotropic gene regions, SMR and HEIDI were applied using GWAS data for the traits and expression quantitative trait locus (eQTL) data from lung, blood and 13 brain tissue samples in the GTEx datasets. In total, we identified 10 shared risk genes ( Table S8 in Supplementary Tables ), all of which passed the HEIDI outlier test (PHEIDI>0.05, PSMR <0.05), with HLA-DQA1 and HLA-DQB1 having the highest expression levels. The high levels of these genes in the peripheral blood was associated with PD, COPD, IPF and asthma, although the direction of their effects was not consistent across these traits. Additionally, multiple genes associated with PD, such as CDC123 and SPNS1, exhibited significantly increase levels in peripheral blood, although the direction of their effects was inconsistent. In COPD patients, genes such as HLA-DQA1, HLA-DQB1, BTNL2, and SPNS1 were significantly downregulated in various brain regions. In contrast, HLA-DQA1 and HLA-DQB1 were significantly upregulated in various brain regions in asthma patients. Understanding the specific cell types in which target genes are expressed, as well as the direction of their expression changes associated with disease risk and progression, is crucial for predicting the potential impact of therapeutic modulation. To this end, we conducted a detailed evaluation of gene expression patterns with single-cell RNA sequencing (scRNA-seq) of GEO datasets derived from individuals with PD, COPD and IPF compared with healthy controls. Our analysis revealed that the HLA-DQA1 gene was upregulated in B cells from both PD patients and COPD patients. In contrast, HLA-DQB1 was downregulated in T cells from both PD patients and COPD patients compared with healthy controls. Conversely, in the microglia of PD patients, both HLA-DQA1 and HLA-DQB1 were downregulated ( Table S 9 in Supplementary Tables ). HLA-DQ interactomes are enriched in different cells with PD and COPD traits To further investigate the differential expression of HLA-DQA1 and HLA-DQB1 in PD and respiratory disorders, we employed high-dimensional weighted gene coexpression network analysis (hdWGCNA) to identify modules of highly correlated genes across different cell types. Specifically, we constructed two gene coexpression modules in B cells, two in T cells, and one in microglia. Gene Ontology (GO) enrichment analysis was performed to identify the most highly enriched pathways in the modules containing HLA-DQA1 and HLA-DQB1 ( Table S 11 in Supplementary Tables ). In PD patients, the most enriched pathways included “MHC class II protein complex assembly” and several other pathways related to MHC class II immunological processes ( Figure 3 C ). However, in COPD patients, in addition to MHC-related immunological mechanisms, enrichment analysis revealed pathways associated with mitochondrial protein-containing complexes ( Figure 4 C ). In the T cells of PD patients, pathways related to leukocyte-mediated immunity, such as antigen processing and presentation, were enriched ( Figure 3 D ). In COPD patients, the enriched pathways in T cells were similar to those in B cells, with a focus on mitochondrial protein-containing complexes ( Figure 4 D ). In the microglia of PD patients, the HLA-DQ genes were associated with pathways related to the methylation of histone lysine and peptidyl-lysine ( Figure 5 C and Table S 11 in Supplementary Tables ). The protein‒protein interaction (PPI) network and enrichment analysis further corroborated findings similar to those of the GO enrichment analysis ( Table S1 2 in Supplementary Tables ), which are related to the MHC class II protein complex and antigen processing and presentation. This provides a comprehensive view of the gene expression landscape and its functional implications in this disease. Discussion We undertook an extensive association study to elucidate the connections between PD (n = 482,730) and respiratory abnormalities (n > 1.3 million), encompassing causal relationships, genetic correlations, shared genetic risk loci, and the expression of prioritized causal genes. Our findings offer novel insights into the genetic pleiotropic effects and potential shared mechanisms underlying both PD and respiratory disorders. To our knowledge, this is the first study to establish a significant causal link between respiratory dysfunction-related traits and the development of PD via Mendelian randomization (MR) analysis and a genetics-based approach. Leveraging the largest available genome-wide association study (GWAS) datasets for PD, lung function and respiratory disorders, we sought to reveal potential genetic associations. Our results indicate that lung function is not causally related to PD risk at the genetic level. However, we cannot dismiss the potential links between respiratory abnormalities and PD. Through a comprehensive two-sample bidirectional MR analysis, we found evidence suggesting that idiopathic pulmonary fibrosis (IPF) and sleep apnoea are associated with PD development. Moreover, our findings highlight that obstructive ventilatory dysfunction may exacerbate motor symptoms in PD patients, whereas restrictive ventilatory dysfunction could worsen cognitive symptoms in PD patients. Previous studies have suggested that respiratory disorders in PD patients may be related to abnormal function of the accessory respiratory muscles, abnormal ventilatory control, and increased chest wall rigidity [ 6 ]. However, recent research has shown that individuals with PD exhibit reduced lung function and respiratory dysfunction even in the very early stages of the disease [ 9 ]. These findings suggest that respiratory issues may not merely be a late-stage complication of PD. Through this multitrait research, we uncovered additional potential mechanisms underlying these conditions. Our study identified 16 potential functional genes using the PLACO and MAGMA methods. Among these 16 genes, CDC123, HLA-DQA1, HLA-DQB1, TAP2 have been previously reported to be related to both PD and respiratory disorders [ 10 , 45 – 49 ]. After SMR, colocalization, and single-cell RNA sequencing analyses were conducted, we identified a shared genetic determinant that may contribute to the pathophysiology of PD and COPD. Our findings indicate that the genes HLA-DQA1 and HLA-DQB1 exhibit the strongest associations with these two diseases. The human leukocyte antigen (HLA) region on chromosome 6 harbours genes encoding components of the major histocompatibility complex (MHC). The HLA-DQ family, a subset of the HLA class II loci, encodes proteins crucial for antigen presentation. These proteins are also expressed in microglia and play dual roles in both clearing pathological protein deposits and producing proinflammatory factors, reactive oxygen species (ROS), and reactive nitrogen species, thereby causing neuronal death. This dual function highlights the critical role of the HLA region in neurodegenerative processes [ 50 ]. Several genome-wide association studies have shown an association between the HLA locus and the risk of PD, while the significant downregulation of the HLA-DQA1 and HLA-DQB1 genes in PD patients further suggests that these genes may play a protective role in this disease [ 46 , 51 ]. Previous research has demonstrated that α-synuclein fragments can bind to MHC molecules, thereby increasing T-cell reactivity. This interaction is proinflammatory and may occur prior to the onset of motor symptoms, suggesting that inflammation may play a role in the early pathogenesis of PD [ 52 ]. Immunity and inflammation play a crucial role in PD, with the activation of microglia in the central nervous system and changes in peripheral immune cells being closely associated with neurodegeneration and disease progression [ 53 , 54 ]. In our previous study, neuroinflammation is closely related to the motor symptoms of PD and may represent an important target for alleviating these symptoms [ 55 ]. We also identified plasma fibronectin [ 56 ] as a biomarker associated with the neuroinflammation, which may be involved in the motor symptom progression in PD. The potential mechanism is that fibronectin enters neurons through theαvβ3 integrin receptor, which in turn exacerbatesα-synuclein aggregation and mitochondrial dysfunction, ultimately promoting neurodegeneration [ 57 ]. Microglia, as key regulators of neuroinflammation, modulate it through their activation and release of inflammatory mediators, interacting with neurons, and influencing the propagation of α-synuclein [ 58 ]. However, no studies to date have demonstrated the expression of HLA-DQ genes in the tissues or cells of PD patients. In our study, an analysis of the scRNA-seq data revealed that HLA-DQA1 was upregulated in blood B cells from PD patients, whereas its expression was downregulated in brain microglia, and HLA-DQB1 was downregulated in blood T cells and brain microglia. In light of our protein‒protein network and enrichment analysis findings, we propose that the primary functional mechanisms of the HLA‒DQ gene complex in PD may involve MHC class II complex binding and peptide antigen assembly with the MHC class II protein complex in B cells. Additionally, in T cells, enrichment analysis suggested that the complex is associated mainly with leukocyte-mediated immunity, including T-cell activation and antigen processing. Furthermore, in microglia, enrichment analysis suggested that the HLA-DQ gene complex is associated mainly with the methylation of histone-lysine and peptidyl-lysine, which is related to the production of proinflammatory cytokines [ 59 , 60 ]. Our results indicated that the expression of the HLA-DQ gene complex is altered not only in peripheral blood cells but also in microglia in PD. Combined with enrichment analysis, these findings suggested that HLA-DQ gene complex may contribute to the modulation of immune responses in both peripheral and central inflammation. The HLA complex plays a significant immunological role in lung function and respiratory diseases such as COPD and asthma [ 48 , 61 , 62 ]. However, different studies have yielded varying results regarding the specific HLA subtypes involved. For instance, the HLA-DQA1*0301 subtype is associated with the risk of both PD and asthma. The underlying mechanisms remain unclear but may be related to immune modulation and tolerance, as well as a reduction in immune-mediated tissue damage. Similar to Parkinson's disease, studies demonstrating the expression of HLA genes in different tissues or cells of patients with COPD are lacking. Interestingly, through SMR analysis, we found that in COPD and asthma patients, the expression of the HLA-DQA1 and HLA-DQB1 genes is not only related to lung tissue and blood cells but also changed in multiple brain regions, indicating possible neural damage in respiratory disorders. Moreover, through scRNA-seq analysis, we also observed a similar pattern in COPD patients, with upregulation of HLA-DQA1 in B cells and downregulation of HLA-DQB1 in T cells, mirroring the findings in PD. In light of our enrichment analysis findings, we discovered that the primary functional mechanisms of the HLA-DQ gene in COPD patients are similar to those in PD patients and are associated with MHC II complex binding and leukocyte-mediated immunity. Therefore, in accordance with the hypothesized mechanisms linking the HLA-DQ gene to PD described above, we posit that the HLA-DQ gene plays a series of important immune-related roles in mediating respiratory dysfunction in PD patients. Further investigations are needed in the future. The intricate relationship between the lungs and brain, often referred to as the "lung-brain axis," highlights the potential for respiratory dysfunction to influence neurological health. Lung diseases frequently co-occur with neurological disorders, possibly due to shared inflammatory pathways and the pathogenesis of the lung-brain axis [ 63 ]. Possible mechanisms include lung inflammation triggering brain inflammation and disrupting the blood-brain barrier, contributing to neurodegenerative processes [ 64 ]. Our findings underscore the importance of this axis in the context of PD, where respiratory disorders may exacerbate neurodegenerative processes through shared immune-mediated pathways. This highlights the need for further research into the lung-brain axis as a potential therapeutic target for mitigating the progression of PD and associated respiratory complications. Our study has several strengths. First, the use of large-scale GWAS summary data for the analysis of associations among PD, lung function, and respiratory disorders provides the power to detect small but significant genetic associations that might be missed by using smaller datasets. This extensive dataset allows for a comprehensive investigation of the genetic underpinnings of the comorbidity between PD and respiratory disorders. Second, the application of MR analysis helps to establish potential causal relationships between PD and respiratory traits. By using genetic variants as instrumental variables, we can mitigate the impact of confounding factors and provide more robust evidence for the associations observed. Third, our colocalization analysis identified genetic loci shared between PD and respiratory disorders, allowing us to pinpoint specific genomic regions that may harbour causal variants. This approach provides a more precise understanding of the genetic overlap between these conditions. Finally, the integration of scRNA-seq data enables us to investigate cell type-specific gene expression patterns. By examining the expression of key candidate genes in B cells, T cells, and microglia, we can elucidate the potential biological mechanisms underlying comorbidities, such as immune-related pathways. Limitations There are several inherent limitations to our study. First, many previous studies have indicated that expiratory dysfunction in PD patients is closely related to on-off periods. However, our study was unable to investigate the differences in ventilatory dysfunction during the on-off periods in PD patients. Second, although the study provided evidence of changes in gene expression, there was a lack of functional validation of these changes. This makes the biological significance and clinical application prospects of the study results less clear. Third, we cannot exclude the potential influence of survival bias, as advanced idiopathic pulmonary fibrosis (IPF) may affect patient mortality before PD is diagnosed. Fourth, the GWAS databases for respiratory disorder-related traits and PD were mostly derived from individuals of European ancestry. Causal and genetic associations may differ according to ethnicity. Conclusion Our data support the causal influence of respiratory disorder–related traits on the increased risk of progression in PD from a genetic perspective. We performed a multitrait analysis of PD and respiratory disorder traits, further prioritizing two shared causal variants between these traits. Our findings suggest possible shared mechanisms underlying PD and respiratory disorders. Our work indicates that the HLA gene complex and members of its interactome could offer new and potentially promising targets for preventive and therapeutic interventions aimed at alleviating respiratory symptoms in PD patients and slowing the progression of PD symptoms. Abbreviations COLOC: colocalization COPD:chronic obstructive pulmonary disease FEV1: forced expiratory volume in the frst second FVC: forced vital capacity HDL: high-definition likelihood hdWGCNA: high-dimensional weighted gene co-expression network analysis HY: Hoehn-Yahr stage HY3: Hoehn-Yahr stage of 3 or more IPF: idiopathic pulmonary fibrosis LDSC: linkage-disequilibrium score regression method MAGMA: multimarker analysis of GenoMic annotation MMSE: Mini-Mental State Examination MoCA: Montreal Cognitive Assessment OSA: obstructive sleep apnoea PEF: peak expiratory fow PD: Parkinson`s Disease PPI: protein protein interaction SEADL: Schwab and England Activities of Daily Living Scale seRNA-seq: single-cell RNA sequencing SMR: summary data-based Mendelian randomization UPDRS: Unified Parkinson`s Disease Rating Scale Declarations Ethics approval This study was based on previously published data. In all original studies, ethical approval and consent to participate were obtained. Contributions Conceived and designed the study: Xinhao Wang, Qing Wang. Performed the study: Xinhao Wang, Jintao Li, Hang Zhou, Zihao Wang, Zifeng Huang, Hailing Liu, Chunguang Li, Bihan Chi and Xiaobo Wei. Revised the paper for intellectual content: Xinhao Wang,Jintao Li, Yinghua Yu, Xiaoying Cui, Deng Chao and Qing Wang. Data statistics and analysis: Xinhao Wang and Zihao Wang. Wrote the paper: Xinhao Wang and Qing Wang. Xinhao Wang and Qing Wang had accessed and verified the data reported in the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (NO: U24A20694, 82471433), and Scientific Research Foundation of Guangzhou (NO: 202206010005) to QW; and Science and Technology Program of Guangzhou (No. 2025B01J3018) to XBW. Competing Interests All authors declare no financial or non-financial competing interests. Data availability All data used in this study are publicly available, and the source of each dataset is described in the main text and Supplementary. Code availability Software used in this study are publicly available, and the version of each software has been specified in the main text and Supplementary. References Ben-Shlomo Y, Darweesh S, Llibre-Guerra J, Marras C, San Luciano M, Tanner C. The epidemiology of Parkinson's disease. Lancet 2024; 403: 283–92. Schapira AHV, Chaudhuri KR, Jenner P. Non-motor features of Parkinson disease. Nat Rev Neurosci 2017; 18: 435–50. Zhou H, Shen B, Huang Z, et al. Mendelian randomization reveals association between retinal thickness and non-motor symptoms of Parkinson's disease. NPJ Parkinsons Dis. 2023; 9: 163. Wang Q, Zheng J, Pettersson S, Reynolds R, Tan EK. The link between neuroinflammation and the neurovascular unit in synucleinopathies. Sci Adv. 2023; 9: eabq1141. Liu H, Deng B, Zhou H, et al. QEEG indices are associated with inflammatory and metabolic risk factors in Parkinson's disease dementia: An observational study. EClinicalMedicine. 2022; 52: 101615. Kaczyńska K, Orłowska ME, Andrzejewski K. Respiratory abnormalities in Parkinson's disease: what do we know from studies in humans and animal models? Int J Mol Sci 2022; 23: 3499. Aquino YC, Cabral LM, Miranda NC, Naccarato MC, Falquetto B, Moreira TS, et al. Respiratory disorders of Parkinson's disease. J Neurophysiol 2022; 127: 1–15. Cheng CM, Wu YH, Tsai SJ, et al. Risk of developing Parkinson's disease among patients with asthma: a nationwide longitudinal study. Allergy . 2015; 70: 1605-1612. Zhang C, Deng J, Li Y, Niu G, Li M, Zhang B, et al. Abnormal pulmonary function in early Parkinson's disease: a preliminary prospective observational study. Lung 2022; 200: 325–9. Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies. Lancet Neurol 2019; 18: 1091–102. Iwaki H, Blauwendraat C, Leonard HL, Kim JJ, Liu G, Maple-Grødem J, et al. Genomewide association study of Parkinson's disease clinical biomarkers in 12 longitudinal patients' cohorts. Mov Disord 2019; 34: 1839–50. Shrine N, Guyatt AL, Erzurumluoglu AM, Jackson VE, Hobbs BD, Melbourne CA, et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. Nat Genet 2019; 51: 481–93. Cosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An T-H, et al. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. Nat Genet 2023; 55: 787–95. Zhou W, Kanai M, Wu KH, Rasheed H, Tsuo K, Hirbo JB, et al. Global biobank meta-analysis initiative: powering genetic discovery across human disease. Cell Genom 2022; 2: 100192. Campos AI, Ingold N, Huang Y, Mitchell BL, Kho PF, Han X, et al. Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring. Sleep 2023; 46: zsac308. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013; 37: 658–65. Bowden J, Smith GD, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015; 44: 512–25. Bowden J, Smith GD, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 2016; 40: 304–14. Bowden J, Spiller W, Del Greco M F, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol 2018; 47: 2100. Burgess S, Smith GD, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4: 186. Zheng J, Baird D, Borges M-C, Bowden J, Hemani G, Haycock P, et al. Recent developments in Mendelian randomization studies. Curr Epidemiol Rep 2017; 4: 330–45. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018; 50: 693–8. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh P-R, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet 2015; 47: 1236–41. Ning Z, Pawitan Y, Shen X. High-definition likelihood inference of genetic correlations across human complex traits. Nat Genet 2020; 52: 859–64. Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer. PLoS Genet 2020; 16: e1009218. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun 2017; 8: 1826. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 2015; 11: e1004219. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 2014; 10: e1004383. Spargo TP, Gilchrist L, Hunt GP, Dobson RJB, Proitsi P, Al-Chalabi A, et al. Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics. eLife 2024; 12: RP88768. Zou Y, Carbonetto P, Wang G, Stephens M. Fine-mapping from summary data with the "sum of single effects" model. PLoS Genet 2022; 18: e1010299. Wallace C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet 2021; 17: e1009440. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 2016; 48: 481–7. GTEx Consortium. The GTEx consortium atlas of genetic regulatory effects across human tissues. Science 2020; 369: 1318–30. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013; 41: D991–5. Xiong L-L, Du R-L, Niu R-Z, Xue L-L, Chen L, Huangfu L-R, et al. Single-cell RNA sequencing reveals peripheral immunological features in Parkinson's disease. NPJ Parkinsons Dis 2024; 10: 185. Heo Y, Kim J, Hong SH, Kim WJ. Single cell transcriptomics in blood of patients with chronic obstructive pulmonary disease. BMC Pulm Med 2025; 25: 19. Unterman A, Zhao AY, Neumark N, Schupp JC, Ahangari F, Cosme C, Jr., et al. Single-cell profiling reveals immune aberrations in progressive idiopathic pulmonary fibrosis. Am J Respir Crit Care Med 2024; 210: 484–96. Martirosyan A, Ansari R, Pestana F, Hebestreit K, Gasparyan H, Aleksanyan R, et al. Unravelling cell type-specific responses to Parkinson's Disease at single cell resolution. Mol Neurodegener 2024; 19: 7. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018; 36: 411–20. Yang C, Zhang X, Chen J. Large language model consensus substantially improves the cell type annotation accuracy for scRNA-seq data. bioRxiv 2025. doi: 10.1101/2025.04.10.647852. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 2015; 16: 278. Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep Methods 2023; 3: 100498. Xu S, Hu E, Cai Y, Xie Z, Luo X, Zhan L, et al. Using clusterProfiler to characterize multiomics data. Nat Protoc 2024; 19: 3292–320. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 2021; 49: D605–12. Chung SJ, Kim MJ, Kim J, Ryu HS, Kim YJ, Kim SY, et al. Association of type 2 diabetes GWAS loci and the risk of Parkinson's and Alzheimer's diseases. Parkinsonism Relat Disord 2015; 21: 1435–40. Yu E, Ambati A, Andersen MS, Krohn L, Estiar MA, Saini P, et al. Fine mapping of the HLA locus in Parkinson's disease in Europeans. NPJ Parkinsons Dis 2021; 7: 84. Artigas MS, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat Genet 2011; 43: 1082–90. Wain LV, Shrine N, Miller S, Jackson VE, Ntalla I, Soler Artigas M, et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir Med 2015; 3: 769–81. Fawcett KA, Demidov G, Shrine N, et al. Exome-wide analysis of copy number variation shows association of the human leukocyte antigen region with asthma in UK Biobank. BMC Med Genomics. 2022; 15 :119. Aliseychik MP, Andreeva TV, Rogaev EI. Immunogenetic factors of neurodegenerative diseases: the role of HLA class II. Biochemistry 2018; 83: 1104–16. Kulski JK, Suzuki S, Shiina T, Pfaff AL, Kõks S. Regulatory SVA retrotransposons and classical HLA genotyped-transcripts associated with Parkinson's disease. Front Immunol. 2024; 15 :1349030. Sulzer D, Alcalay RN, Garretti F, Cote L, Kanter E, Agin-Liebes J, et al. T cells from patients with Parkinson's disease recognize α-synuclein peptides. Nature 2017; 546: 656–61. Roodveldt C, Bernardino L, Oztop-Cakmak O, et al. The immune system in Parkinson's disease: what we know so far. Brain. 2024; 147 :3306-3324. Shi FD, Yong VW. Neuroinflammation across neurological diseases. Science . 2025; 388 : eadx0043. Xie F, Shen B, Luo Y, Zhou H, Xie Z, Zhu S, et al. Repetitive transcranial magnetic stimulation alleviates motor impairment in Parkinson's disease: association with peripheral inflammatory regulatory T-cells and SYT6. Mol Neurodegener 2024; 19: 80. Zhu S, Li H, Huang Z, Zeng Y, Huang J, Li G, et al. Plasma fibronectin is a prognostic biomarker of disability in Parkinson's disease: a prospective, multicenter cohort study. NPJ Parkinsons Dis 2025; 11: 1. Huang Z, Zheng J, Yuan F, et al. Fibrinogen exacerbates α-synuclein aggregation and mitochondrial dysfunction via alpha5beta3 integrin in Parkinson's disease. J Adv Res. Published online May 25, 2025. Isik S, Yeman Kiyak B, Akbayir R, Seyhali R, Arpaci T. Microglia Mediated Neuroinflammation in Parkinson's Disease. Cells . 2023; 12 :1012. Qu L, Yin T, Zhao Y, Lv W, Liu Z, Chen C, et al. Histone demethylases in the regulation of immunity and inflammation. Cell Death Discov 2023; 9: 188. Lanouette S, Mongeon V, Figeys D, Couture JF. The functional diversity of protein lysine methylation. Mol Syst Biol 2014; 10: 724. Mkorombindo T, Tran-Nguyen TK, Yuan K, Zhang Y, Xue J, Criner GJ, et al. HLA-C and KIR permutations influence chronic obstructive pulmonary disease risk. JCI Insight 2021; 6: e150187. Clay SM, Schoettler N, Goldstein AM, et al. Fine-mapping studies distinguish genetic risks for childhood- and adult-onset asthma in the HLA region. Genome Med. 2022; 14 (1):55. Park H, Lee CH. The Impact of Pulmonary Disorders on Neurological Health (Lung-Brain Axis). Immune Netw . 2024; 24 (3):e20. Mapunda JA, Tibar H, Regragui W, Engelhardt B. How does the immune system enter the brain? Front Immunol. 2022; 13 :805657. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure.pdf SupplementaryTables.xlsx Checklist.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7234326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492726585,"identity":"cb24790c-1b18-4ba6-8860-14c7f4333878","order_by":0,"name":"Xinhao Wang","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinhao","middleName":"","lastName":"Wang","suffix":""},{"id":492726586,"identity":"5dac2267-3ebe-43e7-9c09-2d9e0459edbf","order_by":1,"name":"Jintao Li","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jintao","middleName":"","lastName":"Li","suffix":""},{"id":492726587,"identity":"7c0e0e3b-5fc9-433d-a6a2-5a92462af888","order_by":2,"name":"Zihao Wang","email":"","orcid":"","institution":"Ipswich Grammar School","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Wang","suffix":""},{"id":492726588,"identity":"06df2887-e570-4d3f-b148-38c2b3327742","order_by":3,"name":"Zifeng Huang","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zifeng","middleName":"","lastName":"Huang","suffix":""},{"id":492726589,"identity":"e6439d07-c8c3-4233-892a-938a08e17cab","order_by":4,"name":"Xiaobo Wei","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Wei","suffix":""},{"id":492726590,"identity":"a5d8fc37-544c-4cb3-990b-c0e0959a9b6f","order_by":5,"name":"Hang Zhou","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Zhou","suffix":""},{"id":492726592,"identity":"6f248c35-e475-46e5-8685-455d4a96c16a","order_by":6,"name":"Hailing Liu","email":"","orcid":"","institution":"Maoming People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hailing","middleName":"","lastName":"Liu","suffix":""},{"id":492726594,"identity":"f02e0430-b37c-457a-8a26-fa8ec417c3d9","order_by":7,"name":"Bihan Chi","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bihan","middleName":"","lastName":"Chi","suffix":""},{"id":492726597,"identity":"58936c32-96fe-4f1e-ac76-cb8102ffc143","order_by":8,"name":"Chunguang Li","email":"","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunguang","middleName":"","lastName":"Li","suffix":""},{"id":492726599,"identity":"60b5ea44-701b-4ec5-8fe7-30308e752091","order_by":9,"name":"Xiaoying Cui","email":"","orcid":"","institution":"Queensland Centre for Mental Health Research, University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Cui","suffix":""},{"id":492726602,"identity":"7dce3a7c-cbe5-4990-87b5-7ab67cf0875f","order_by":10,"name":"Chao Deng","email":"","orcid":"","institution":"University of Wollongong","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Deng","suffix":""},{"id":492726604,"identity":"21f97ac0-fbf9-4059-9611-c47743986588","order_by":11,"name":"Yinghua Yu","email":"","orcid":"","institution":"University of Wollongong","correspondingAuthor":false,"prefix":"","firstName":"Yinghua","middleName":"","lastName":"Yu","suffix":""},{"id":492726605,"identity":"fdff72c1-6362-4fcb-839c-3fc9a2508c22","order_by":12,"name":"Dennis Qing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYHACAyCWYOCXAHMkZIjXIjmDgbEByOAhVguQvAHWwkBYi8Hx5I2PC3dY2Bvfbj7+6EaNBQ8D++GjG/BqOfOs2HjmGYnEbXeOJTbnHAM6jCct7QY+LWY3csykedskEoAMw+YcNqAWCR4zQlrMfwO12BvPAGn5R5wWM2agFsYNEkAtuW1EaLEH+gXksMQZN9ISZ+f2SfCwEfKLZHvyxs+8bXX2/DOSD3zO+VYnx89++BheLQwMCWh8NvzKsWkZBaNgFIyCUYAOAC7aRQy2WR3AAAAAAElFTkSuQmCC","orcid":"","institution":"Zhujiang Hospital of Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dennis","middleName":"Qing","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-28 13:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7234326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7234326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88239477,"identity":"01e7e306-b5fd-4aba-8eb3-5c31d3e6b18e","added_by":"auto","created_at":"2025-08-04 10:58:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":497563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the Study Design and Analyses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study consists of four key steps. First, large-scale GWAS summary data for PD, lung function, and respiratory disorders in individuals of European ancestry were used to conduct a genetic association study. Second, Mendelian randomization (MR) analysis was performed to assess causal relationships between respiratory traits and PD risk/progression. Third, gene-level analysis using MAGMA and colocalization analysis were conducted to identify shared genetic loci, followed by SMR analysis to explore the causal association of gene expression with disease risk. Finally, single-cell RNA sequencing and enrichment analysis were used to investigate cell type-specific gene expression patterns and their roles in PD and respiratory disorders. Abbreviations: COLOC: colocalization; HDL: high-definition likelihood; hdWGCNA: high-dimensional weighted gene co-expression network analysis; LDSC: linkage-disequilibrium score regression method; MAGMA: multimarker analysis of GenoMic annotation; PD: Parkinson`s disease; PPI: protein protein interaction; seRNA-seq: single-cell RNA sequencing; SMR: summary data-based Mendelian randomization\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/d25c6e08e567d7bf1a8066f7.jpg"},{"id":88239805,"identity":"50b9f752-870e-4675-b7eb-8ccdb4af7bea","added_by":"auto","created_at":"2025-08-04 11:06:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":412439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMendelian Randomization Analysis Estimated by IVW Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe instrumental variables (IVs) used in this Mendelian randomization analysis were derived from genome-wide association studies (GWAS) of respiratory traits and Parkinson’s disease risk and progression. Abbreviations: COPD:chronic obstructive pulmonary disease; FEV1: forced expiratory volume in the frst second; FVC: forced vital capacity; HY: Hoehn-Yahr stage; HY3: Hoehn-Yahr stage of 3 or more; IPF: idiopathic pulmonary fibrosis; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; OSA: obstructive sleep apnoea; PEF: peak expiratory fow; PD: Parkinson’s Disease; SEADL: Schwab and England Activities of Daily Living Scale; UPDRS: Unified Parkinson’s Disease Rating Scale\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/68a3f372288af5b538edb5ea.jpg"},{"id":88238110,"identity":"244be83f-5071-4953-a16a-6af59d5d8b5b","added_by":"auto","created_at":"2025-08-04 10:42:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3599683,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nuclei transcriptomes of peripheral blood mononuclear cells (PBMC) for HLA-DQ genes in PD patient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA)The transcriptomes form discrete cell-specific clusters using Uniform Manifold Approximation and Projection (UMAP) of PBMC in PD patient. B) Expression of HLA-DQA1 and HLA-DQB1 genes across cell-specific clusters from UMAP. Red indicates higher expression. C) Gene Ontology enriched pathways of the HLA-DQA1-containing module in blood B cells of PD patient. D) Gene Ontology enriched pathways of the HLA-DQB1-containing module in blood T cells of PD patient. E) Protein protein interaction network of HLA-DQA1-containing module in blood B cells of PD patient. F) Protein-protein interaction enrichment analysis of the HLA-DQA1 containing module in B cells with Parkinson`s disease. G) Protein-protein interaction network of HLA-DQA1-containing module in blood B cells of PD patient. H) Protein protein interaction enrichment analysis of the HLA-DQB1 containing module in T cells with Parkinson`s disease.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/e5c7f2aa0c98bbd885ca2feb.jpg"},{"id":88238484,"identity":"ec2f1961-cc17-4f3e-b016-0f8247ad881f","added_by":"auto","created_at":"2025-08-04 10:50:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4360350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nuclei transcriptomes of peripheral blood mononuclear cells (PBMC) for HLA-DQ genes in COPD patient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA)The transcriptomes form discrete cell-specific clusters using Uniform Manifold Approximation and Projection (UMAP) of PBMC in COPD patient. B) Expression of HLA-DQA1 and HLA-DQB1 genes across cell-specific clusters from UMAP. Red indicates higher expression. C) Gene Ontology enriched pathways of the HLA-DQA1-containing module in blood B cell of COPD patient. D) Gene Ontology enriched pathways of the HLA-DQB1-containing module in blood T cells of COPD patient.\u003c/p\u003e\n\u003cp\u003eE) Protein-protein interaction network of HLA-DQA1 containing module in blood B cells of COPD patient. F) Protein-protein interaction enrichment analysis of the HLA-DQA1 containing module in B cells of COPD patient. G) Protein-protein interaction network of HLA-DQA1 containing module in blood B cells of COPD patient. H) Protein-protein interaction enrichment analysis of the HLA-DQB1 containing module in T cells of COPD patient.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/9984b11e1ad95341ea251970.jpg"},{"id":88238116,"identity":"dae7ba13-1e01-48ac-9fd0-94b9312b4f1b","added_by":"auto","created_at":"2025-08-04 10:42:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2077684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nuclei transcriptomes of brain cells for HLA-DQ genes in PD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA)The transcriptomes form discrete cell-specific clusters using Uniform Manifold Approximation and Projection (UMAP) of brain cells in PD patient. B)Expression of HLA-DQA1 and HLA-DQB1 genes across cell-specific clusters from UMAP. Red indicates higher expression. C) Gene Ontology enriched pathways of the HLA-DQ gene containing module of PD patient.D) Protein-protein interaction network of HLA-DQ gene containing module in brain cells of PD patient. E) Protein-protein interaction enrichment analysis of the HLA-DQ gene containing module in brain cells of PD patient.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/393454666cbfce38c6c034a4.jpg"},{"id":99792811,"identity":"4b671012-0c4d-4e3a-baef-9c3a5c9549d5","added_by":"auto","created_at":"2026-01-08 13:26:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12325718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/539cc8ca-3443-4a7c-b956-e882042f8cec.pdf"},{"id":88238114,"identity":"b0eaaf6e-2f7f-45de-b529-d48eee29a849","added_by":"auto","created_at":"2025-08-04 10:42:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2690572,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/396cad0ad67aa7b12e3d35f5.pdf"},{"id":88238112,"identity":"483a98f8-9afa-4f76-a1ed-a79d43ab9bcd","added_by":"auto","created_at":"2025-08-04 10:42:05","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":515013,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/ccace29d088a9f5f591e8fd9.xlsx"},{"id":88238479,"identity":"95adf6f0-51d3-414d-add2-ba763d3202e2","added_by":"auto","created_at":"2025-08-04 10:50:05","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":83979,"visible":true,"origin":"","legend":"","description":"","filename":"Checklist.doc","url":"https://assets-eu.researchsquare.com/files/rs-7234326/v1/fb40eeb83f3dbe52822c74c6.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elucidating the Multitrait Association between Parkinson’s Disease and Respiratory Disorders: HLA gene complex as a causal nexus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOur and other previous studies have shown that Parkinson\u0026rsquo;s disease (PD) presents motor (e.g., bradykinesia, rigidity, rest tremor, gait disturbances) and nonmotor (e.g., impaired olfaction, rapid eye movement sleep behaviour disorder, and constipation) features [1,2,3]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e, in which neuroinflammation and \u003cstrong\u003eimmunomodulation\u003c/strong\u003e play crucial roles [4,5]. Importantly, in addition to the well-documented symptoms, respiratory dysfunction is also an observed symptom of PD [6]. Respiratory dysfunction has been associated with PD since its initial documentation and is a well-established predictor of mortality and morbidity in PD patients. Moreover, pneumonia is frequently cited as the leading cause of death in PD patients, and a review of hospital admissions for individuals with PD revealed that 33% of admissions were due to respiratory system diseases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to aspiration pneumonia, there are four main types of respiratory disorders associated with PD: airway obstruction, restrictive respiratory dysfunction, obstructive sleep apnoea (OSA) and asthma [7,8]. Importantly, an observational study has shown that significant differences in all commonly examined lung function parameters are already present in patients in the early stages of PD compared with controls, underscoring that pulmonary function begins to deteriorate at the very onset of the disease [9]. Despite growing attention to the associations between respiratory disorders and PD, the underlying mechanisms remain poorly understood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we examined the relationships between PD and lung function, as assessed by forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), peak expiratory flow (PEF), and the FEV1/FVC ratio. We also investigated the associations between PD and respiratory disorders, including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), OSA and asthma. To achieve this goal, we utilized large-scale genome-wide association study (GWAS) summary data, encompassing 482,730 individuals for Parkinson`s disease and more than 1.3 million individuals for respiratory disorders, and employed various statistical genetic approaches to investigate pleiotropic associations sequentially from the gene level to the biological pathway level. This approach helped elucidate the underlying shared genetic aetiology between PD and respiratory disorders. By elucidating the genetic relationship between PD and respiratory disorders, we also identified specific genomic regions of interest especially like HLA-DQA1 and HLA-DQB1 genes and their potential immunomodulation role for future molecular studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGWAS summary statistics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed summary-level data from six European-ancestry datasets. For PD onset risk, data from the Medical Research Council Integrative Epidemiology Unit (MRC IEU) OpenGWAS database were used, including 15,056 cases, 18,618 proxy cases, and 449,056 controls [10]. PD progression data were derived from a GWAS analysis by Iwaki et al., covering 25 clinical phenotypes of 4,093 PD patients across 12 cohorts [11]. GWAS summary data on lung function were obtained from a meta-analysis by Shrine N et al., based on the UK Biobank and SpiroMeta Federation, comprising 321,047 individuals [12]. COPD data were sourced from a GWAS analysis by Cosentino J et al., using the UK Biobank database, with 325,027 individuals [13]. IPF and asthma data were from a meta-analysis by Wei Zhou et al., covering nine cohorts, with 953,873 individuals for IPF and 1,376,071 individuals for asthma [14]. OSA data were from a meta-analysis by Campos AI et al., based on five cohorts, with 159,255 individuals. All studies were conducted between 2019 and 2023 [15]. The study design is presented in \u003cstrong\u003eFigure 1\u003c/strong\u003e, and the characteristics of the GWAS summary statistics data are shown in \u003cstrong\u003eTable S1\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e. All analyses were conducted between September 2024 and May 2025. This genome-wide pleiotropic association study followed the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMendelian randomization (MR) and sensitivity analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstrumental variables (IVs) for both phenotypes were selected by filtering for variants achieving genome-wide significant variants (p \u0026lt; 5\u0026times;\u0026nbsp;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e). The remaining variants were clumped using an R\u003csup\u003e2\u003c/sup\u003e threshold of 0.001 and a distance of 10,000 kilobase pairs to ensure independence. To generate and confirm strong instruments for the exposures, F statistics were calculated for each IV, with values above 10, the recommended threshold for determining strong IVs. A P value less than 0.05 was considered statistically significant. The primary MR analysis was performed using the inverse variance weighted (IVW) method [16]. The\u003csup\u003e\u0026nbsp;\u003c/sup\u003eMR-Egger [17] and weighted median methods [18] were utilized to complement and enhance the reliability of the MR findings. Egger intercept analysis was used to test for horizontal pleiotropic effects, with a p value less than 0.05 considered indicative of possible horizontal pleiotropy. MR-Egger and IVW methods using Cochran\u0026apos;s Q statistics were employed to identify potential heterogeneity among the single-nucleotide polymorphisms (SNPs), with a p value less than 0.05 suggesting potential heterogeneity [19]. Leave-one-out sensitivity analysis was conducted to determine whether the causal effect was driven by single SNPs. Significant changes in causal effects after excluding any specific SNP indicated the presence of heterogeneity [20]. The MR-PRESSO test [21] was also used to address directional pleiotropy by removing outliers. Additionally, the Steiger test\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[22] was applied to confirm the effect direction from exposure to outcome. Finally, a funnel plot was used to detect directional pleiotropy. The asymmetry of the plot indicated strong effects of certain SNPs on the outcome, despite their low precision, suggesting the potential presence of pleiotropy. These analyses were conducted using the TwoSampleMR R package (version 0.6.8) within the R software environment (version 4.4.3).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenetic correlation analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify the genetic correlation between PD and seven respiratory traits at the whole-genome level, we utilized the linkage-disequilibrium score regression method (LDSC) [23] and high-definition likelihood (HDL) [24]. We performed LDSC using well-imputed HapMap3 variants and precomputed LD scores of European ancestry from the 1000 Genomes Project Phase 3. We executed HDL using the R package HDL-v1.4.0.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGene-level analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted pleiotropic analysis under the composite null hypothesis (PLACO)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[25] analyses on PD and respiratory disorder traits (COPD, IPF, OSA and asthma) to detect the pleiotropic associations between genetic variants and traits by considering a composite null hypothesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each trait pair, we denoted trait1 and trait2 as PD and respiratory disorder, beta trait1 and beta trait2 as the effect sizes of an SNP on two traits, and Ztrait1 and Ztrait2 as the observed Z scores of an SNP from corresponding GWAS summary data. Rejection of H0 statistically implied that the SNP could be a potential pleiotropic variant shared between two traits. Overlapping SNPs between GWASs of each pairwise trait were included, and the summary statistics were harmonized to align with the same effect allele. SNPs with squared Z scores above 80 were excluded, as extremely large effect sizes might generate false signals [25]. To account for potential sample overlap, we decorrelated the Z scores using the correlation matrix estimated from GWAS summary statistics. The generated GWAS results were subsequently subjected to FUMA analysis (https://fuma.ctglab.nl/snp2gene)\u003csup\u003e\u0026nbsp;\u0026nbsp;\u003c/sup\u003e[26] \u0026nbsp;to characterize potential pleiotropic genes between two diseases by setting the P\u003csub\u003ePLACO\u003c/sub\u003e value to \u0026lt;10E\u003csup\u003e-6\u003c/sup\u003e within a \u0026plusmn;250 kb radius and the linkage disequilibrium (LD) r\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eto\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026gt;0.2 in a single genetic locus.\u003c/p\u003e\n\u003cp\u003eWe first performed gene-level Multi-marker Analysis of GenoMic Annotation (MAGMA)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[27] on the genes located in or overlapping with the pleiotropic loci on the basis of both the PLACO and single-trait GWAS results to identify candidate pleiotropic genes. MAGMA gene IDs and locations of 19,427 protein-coding genes identified using NCBI build 37.3 were obtained from the CTG-CNCR [27] website. Significance was declared at both the locus-specific Bonferroni-corrected p value \u0026lt; 0.05 for MAGMA analysis of the PLACO results and p values \u0026lt; 0.05 for both MAGMA analyses on the basis of the original single-trait GWAS results of patients with corresponding PD and respiratory disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eColocalization analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe performed colocalization using COLOC [28], a Bayesian statistical method designed to assess the probability of two phenotypes sharing a causal variant within a predefined genomic region. Within the colocalization framework, posterior probabilities are evaluated for five hypotheses:\u003c/p\u003e\n\u003cp\u003eH₀: No causal variant exists in the region for either phenotype.\u003c/p\u003e\n\u003cp\u003eH₁: A causal variant exists in the region for the first phenotype.\u003c/p\u003e\n\u003cp\u003eH₂: A causal variant exists in the region for the second phenotype.\u003c/p\u003e\n\u003cp\u003eH₃: Distinct causal variants exist for each trait within the region.\u003c/p\u003e\n\u003cp\u003eH₄: Both traits share a causal variant.\u003c/p\u003e\n\u003cp\u003eColocalization analysis was conducted using the COLOC-reporter pipeline [29]. COLOC-reporter extracts variants within user-defined genomic regions and calculates the LD matrix for this region from a user-defined reference panel. In our study, the panel consisted of samples from patients of European ancestry from the 1000 Genomes Phase 3 trial (N = 503). It then harmonizes the summary statistics to match the allele order of the reference panel, flipping effect directions accordingly. The observed versus expected z scores are assessed using the diagnostic tools provided in the susie [30] R package. The z score outliers are omitted. Following this quality control, susie fine-mapping is subsequently conducted to identify 95% credible sets in these regions for each phenotype. For both phenotypes, the identification of credible sets allows for relaxation of the single causal variant assumption. All possible credible sets are then assessed pairwise for a shared signal between phenotypes, which improves the resolution for colocalization inference in regions containing multiple signals. If no 95% credible set is identified or only identified for one phenotype, colocalization under the single causal variant assumption is performed using coloc [31]. A posterior probability of \u0026ge;80% for H₃+H₄ is considered strong evidence of colocalization between two traits (PP.H₃+PP. H₄ \u0026ge; 0.8). A posterior probability of \u0026ge;60% for H₃+H₄ is considered possible evidence of colocalization between two traits (PP.H₃+PP. H₄ \u0026ge; 0.6). The SuSiE model assumes at most ten causal variants (L = 10) per credible set. We used default priors (P1 = 1\u0026times;10⁻⁴, P2 = 1\u0026times;10⁻⁴, P12 = 5\u0026times;10⁻⁶).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSummary data-based\u0026nbsp;\u003c/em\u003e\u003cem\u003eM\u003c/em\u003e\u003cem\u003eendelian randomization (SMR)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe applied SMR [32] and heterogeneity in dependent instruments (HEIDI) using SMR software v1.3.1 to investigate whether the expression of the shared PD and respiratory disorders genes identified in lung, blood and brain tissue data via MAGMA were causally associated with disease risk. SMR was performed by integrating summary eQTL data from GTEx version 8 [33] (SNPs within 1 Mb of the transcription start site with P\u0026lt;1\u0026times;10\u003csup\u003e\u0026minus;5\u003c/sup\u003e) and summary statistics from MAGMA. SMR integrates expression quantitative trait loci (eQTLs) and GWAS summary data within an MR framework to identify genes whose expression levels are linked to a phenotype of interest through pleiotropy. The resulting associations can be interpreted as assessing whether the effect of a variant on a phenotype is mediated through gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSingle-cell and single-nuclei data acquisition\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe retrieved data from Gene Expression Omnibus (GEO) [34] single-cell RNA datasets of peripheral blood mononuclear cells (PBMCs) from 4 patients with Parkinson\u0026rsquo;s disease and 2 matched healthy controls [35], PBMCs from 8 patients with COPD and 7 matched healthy controls [36], PBMCs from 25 patients with IPF and 13 matched healthy controls [37], and postmortem substantia nigra pars compacta (SNpc) samples from 15 patients with PD and 14 healthy controls [38]. The unique molecular identifier (UMI) count matrix was processed utilizing the Seurat [39] R package (version 2.3.4).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSingle-cell RNA-seq data analysis pipeline\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe employed a comprehensive single-cell RNA-seq data analysis workflow implemented in R using the Seurat [39] package. To ensure the quality and reliability of the single-cell RNA sequencing data, we retained cells with a total molecule count between 0 and 50000, numerous detected genes between 0 and 4000, a percentage of mitochondrial genes less than 15%, and a percentage of ribosomal genes less than 40%. We normalized the data using the log normalization method and identified highly variable genes to focus our subsequent analyses on the most informative features. We conducted dimensionality reduction through principal component analysis (PCA) and corrected for batch effects using the Harmony method. On the basis of gene expression profiles, we clustered the cells and identified marker genes for each cluster to characterize the cell populations. Then, we visualized the results using uniform manifold approximation and projection (UMAP) plots to clearly illustrate the cell clusters and their relationships. Finally, following clustering, we used the mLLMCelltype [40] algorithm to perform a cell-type prediction on the cell clusters.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDifferential gene expression analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression (DGE) analysis was performed separately for each cell-specific sample for all candidate genes detected via MAGMA gene analysis. We examined the expression of the genes in the scRNA-seq data and included only genes expressed in at least one cell type of the examined tissues. We followed a generalized linear mixed model approach implemented in the MAST [41] R package after excluding genes expressed in fewer than 10% of the cells. Units for differential expression were defined as the log2-fold change (log2FC) per unit change in the respective contrast. We considered genes with a log2FC \u0026ge; 0.25 and a adjusted p value \u0026lt; 0.05 as significantly differentially expressed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWeighted gene coexpression network and enrichment analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the high-dimensional weighted gene coexpression network analysis (hdWGCNA) [42] R package to construct cell type-specific coexpression networks for the selected candidate genes. Initially, we applied the k-nearest neighbours\u0026rsquo; algorithm to identify groups of similar cells on the basis of their transcriptomic profiles, creating metacells. We subsequently generated a metacell gene expression matrix to serve as the foundation for constructing the coexpression network. The network was built using the lowest soft power threshold that achieved a scale-free topology model fit of at least 0.8, ensuring robust network construction. In this process, genes that did not group into any coexpression module were excluded from further analysis and assigned to the \u0026quot;grey\u0026quot; module. Following the construction of the coexpression network, we conducted a pathway enrichment analysis using the clusterProfiler [43] R package, focusing on gene sets with at least 20 genes to ensure meaningful biological insights. To further explore the interactions among the genes within these modules, we utilized the STRINGdb [44] R package to analyse the full protein‒protein interaction network data from the STRING database, aiming to uncover potential functional relationships and pathways.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociations between genetically predicted lung function and PD risk and progression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur MR results demonstrated no significant bidirectional effect of lung function on the risk of PD. However, in the primary inverse-variance weighted (IVW) MR analysis, a genetic predisposition to lower FEV1/FVC was also positively associated with motor impairment in PD patients, as indicated by higher HY3 staging and higher UPDRS III scores (\u003cstrong\u003eFigure 2\u003c/strong\u003e and \u003cstrong\u003eTable S2\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e). Specifically, a lower FEV1/FVC was associated with an increased likelihood of HY3 staging (odds ratio [OR] = 0.362, 95% confidence interval [CI] = 0.155\u0026ndash;0.846, p = 0.019) and higher UPDRS III scores (OR = 0.807, CI = 0.664\u0026ndash;0.980, p = 0.030). Additionally, a genetic predisposition to lower FVC was positively associated with cognitive impairment in PD patients, as indicated by lower MMSE scores (OR = 0.583, CI = 0.341\u0026ndash;0.996, p = 0.048).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociations between genetically predicted respiratory disorders and PD risk and progression\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur MR results demonstrated a significant causal effect of PD on the risk of IPF and OSA (\u003cstrong\u003eFigure 2\u003c/strong\u003e and \u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e). According to the primary IVW MR analysis, a genetic predisposition towards a greater risk of PD was causally associated with an increased risk of developing IPF (OR = 1.138, CI = 1.040\u0026ndash;1.257, P = 0.0048) and a decreased risk of developing OSA (OR = 0.972, CI = 0.945\u0026ndash;0.998, P = 0.0402). However, there was no clear association between genetically predicted lung function and the risk of chronic obstructive pulmonary disease (COPD) in this study. Additionally, we did not have sufficient instrumental variables to determine the relationship between PD and asthma.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith respect to PD progression, we observed a statistically significant association between COPD and an increased risk of more severe motor impairment in PD patients, as indicated by higher UPDRS III scores. Additionally, we found a statistically significant association between IPF and an increased risk of cognitive impairment in PD patients, as evidenced by a greater risk of dementia and lower MMSE and MOCA scores. Our findings indicate that OSA is associated with a range of nonmotor symptoms, including olfactory dysfunction, cognitive impairment, and higher UPDRS IV scores, in PD patients (\u003cstrong\u003eFigure 2\u003c/strong\u003e and \u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn sensitivity analyses, the zero-intercept test of MR‒Egger regression revealed no evidence of horizontal pleiotropy across SNPs for the associations between PD risk and COPD and between PD progression and respiratory trait (\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e2 and S3\u003c/strong\u003e in\u003cstrong\u003e\u0026nbsp;Supplementary Tables\u003c/strong\u003e). In addition, there was no heterogeneity in causal estimates according to MR‒Egger or IVW methods using Cochran\u0026rsquo;s Q statistics (\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e in\u003cstrong\u003e\u0026nbsp;Supplementary Tables\u003c/strong\u003e). For the associations between PD and IPF and OSA, while the MR-PRESSO test detected significant horizontal pleiotropy, both the MR‒Egger regression (\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e in\u003cstrong\u003e\u0026nbsp;Supplementary Tables\u003c/strong\u003e) and the leave-one-out sensitivity analysis (\u003cstrong\u003eFigure S\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e in\u003cstrong\u003e\u0026nbsp;Supplementary Figures\u003c/strong\u003e) indicated that the impact of this pleiotropy on the causal effect estimates was minimal. This suggests that the causal effect estimates are robust.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGenetic correlations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the bivariate genetic correlation between PD and respiratory traits using LDSC and HDL. There was no strong genetic correlation between PD and respiratory traits. PD and asthma show significant weak genetic correlation (\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eShared loci between PD and respiratory disorders\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ebetween PD and A total of 1211 SNPs were identified as potential pleiotropic variants between PD and respiratory disorders by PLACO. FUMA identified 16 independent genomic risk loci as pleiotropic loci, involving 13 unique chromosomal regions. ANNOVAR category annotation revealed that 51.9% of the variants (629 of 1211 SNPs) were intronic and that 24.7% of the variants were intergenic (300 of 1211 SNPs).(\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAGMA analysis of potential pleiotropic genes located in or overlapping 83 pleiotropic loci revealed 26 significant pleiotropic genes. Among these pleiotropic genes,HLA-DQA1 was concurrently detected in COPD, IPF, and asthma trait pairs. (\u003cstrong\u003eTable 1\u003c/strong\u003e and \u003cstrong\u003eFigure S2\u003c/strong\u003e in \u003cstrong\u003eSupplementary\u0026nbsp;Figures\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing the colocalization method on 26 MAGMA-reported loci, we identified 12 loci that colocalized between PD and respiratory disorders traits with a PPH3+PPH4 (posterior probability)\u0026gt;0.8 (\u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;S5\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable S7\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e). Most colocalized loci were found between PD patients and COPD patients (5 loci). Among the 12 loci with evidence for colocalization, two loci for which a single candidate causal variant explained a large proportion of the association was mapped in SETDA (position: chr16:30968615-30996437; colocalized with the PD-COPD trait; PPH4= 0.9104; \u003cstrong\u003eFigure S4\u003c/strong\u003e in \u003cstrong\u003eSupplementary\u0026nbsp;Figures\u003c/strong\u003e) and PIGL (position: chr17:16120505-16252115; colocalized with the PD-asthma trait; PPH4= 0.8058; \u003cstrong\u003eFigure S4\u003c/strong\u003e in \u003cstrong\u003eSupplementary\u0026nbsp;Figures\u003c/strong\u003e). Two loci were suggestive of colocalization (PIGL colocalized with PD-IPF, and CNTN1 colocalized with PD-OSA, PPH3+PPH4\u0026gt;0.6).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eValidation of expression of the pleiotropic causal genes shared between PD and respiratory disorders\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further validate the associations of the significant pleiotropic gene regions, SMR and HEIDI were applied using GWAS data for the traits and expression quantitative trait locus (eQTL) data from lung, blood and 13 brain tissue samples in the GTEx datasets. In total, we identified 10 shared risk genes (\u003cstrong\u003eTable S8\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e), all of which passed the HEIDI outlier test (PHEIDI\u0026gt;0.05, PSMR \u0026lt;0.05), with HLA-DQA1 and HLA-DQB1 having the highest expression levels. The high levels of these genes in the peripheral blood was associated with PD, COPD, IPF and asthma, although the direction of their effects was not consistent across these traits. Additionally, multiple genes associated with PD, such as CDC123 and SPNS1, exhibited significantly increase levels in peripheral blood, although the direction of their effects was inconsistent. In COPD patients, genes such as HLA-DQA1, HLA-DQB1, BTNL2, and SPNS1 were significantly downregulated in various brain regions. In contrast, HLA-DQA1 and HLA-DQB1 were significantly upregulated in various brain regions in asthma patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnderstanding the specific cell types in which target genes are expressed, as well as the direction of their expression changes associated with disease risk and progression, is crucial for predicting the potential impact of therapeutic modulation. To this end, we conducted a detailed evaluation of gene expression patterns with single-cell RNA sequencing (scRNA-seq) of GEO datasets derived from individuals with PD, COPD and IPF compared with healthy controls. Our analysis revealed that the HLA-DQA1 gene was upregulated in B cells from both PD patients and COPD patients. In contrast, HLA-DQB1 was downregulated in T cells from both PD patients and COPD patients compared with healthy controls. Conversely, in the microglia of PD patients, both HLA-DQA1 and HLA-DQB1 were downregulated (\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e9\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHLA-DQ interactomes are enriched in different cells with PD and COPD traits\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the differential expression of HLA-DQA1 and HLA-DQB1 in PD and respiratory disorders, we employed high-dimensional weighted gene coexpression network analysis (hdWGCNA) to identify modules of highly correlated genes across different cell types. Specifically, we constructed two gene coexpression modules in B cells, two in T cells, and one in microglia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis was performed to identify the most highly enriched pathways in the modules containing HLA-DQA1 and HLA-DQB1 (\u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e11\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e). In PD patients, the most enriched pathways included \u0026ldquo;MHC class II protein complex assembly\u0026rdquo; and several other pathways related to MHC class II immunological processes (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e). However, in COPD patients, in addition to MHC-related immunological mechanisms, enrichment analysis revealed pathways associated with mitochondrial protein-containing complexes (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e). In the T cells of PD patients, pathways related to leukocyte-mediated immunity, such as antigen processing and presentation, were enriched (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003eD\u003c/strong\u003e). In COPD patients, the enriched pathways in T cells were similar to those in B cells, with a focus on mitochondrial protein-containing complexes (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003eD\u003c/strong\u003e). In the microglia of PD patients, the HLA-DQ genes\u0026nbsp;were associated with pathways related to the methylation of histone lysine and peptidyl-lysine (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eTable S\u003c/strong\u003e\u003cstrong\u003e11\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe protein‒protein interaction (PPI) network and enrichment analysis further corroborated findings similar to those of the GO enrichment analysis (\u003cstrong\u003eTable S1\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e in \u003cstrong\u003eSupplementary Tables\u003c/strong\u003e), which are related to the MHC class II protein complex and antigen processing and presentation. This provides a comprehensive view of the gene expression landscape and its functional implications in this disease.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe undertook an extensive association study to elucidate the connections between PD (n\u0026thinsp;=\u0026thinsp;482,730) and respiratory abnormalities (n\u0026thinsp;\u0026gt;\u0026thinsp;1.3\u0026nbsp;million), encompassing causal relationships, genetic correlations, shared genetic risk loci, and the expression of prioritized causal genes. Our findings offer novel insights into the genetic pleiotropic effects and potential shared mechanisms underlying both PD and respiratory disorders. To our knowledge, this is the first study to establish a significant causal link between respiratory dysfunction-related traits and the development of PD via Mendelian randomization (MR) analysis and a genetics-based approach.\u003c/p\u003e\u003cp\u003eLeveraging the largest available genome-wide association study (GWAS) datasets for PD, lung function and respiratory disorders, we sought to reveal potential genetic associations. Our results indicate that lung function is not causally related to PD risk at the genetic level. However, we cannot dismiss the potential links between respiratory abnormalities and PD. Through a comprehensive two-sample bidirectional MR analysis, we found evidence suggesting that idiopathic pulmonary fibrosis (IPF) and sleep apnoea are associated with PD development. Moreover, our findings highlight that obstructive ventilatory dysfunction may exacerbate motor symptoms in PD patients, whereas restrictive ventilatory dysfunction could worsen cognitive symptoms in PD patients.\u003c/p\u003e\u003cp\u003ePrevious studies have suggested that respiratory disorders in PD patients may be related to abnormal function of the accessory respiratory muscles, abnormal ventilatory control, and increased chest wall rigidity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, recent research has shown that individuals with PD exhibit reduced lung function and respiratory dysfunction even in the very early stages of the disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These findings suggest that respiratory issues may not merely be a late-stage complication of PD. Through this multitrait research, we uncovered additional potential mechanisms underlying these conditions. Our study identified 16 potential functional genes using the PLACO and MAGMA methods. Among these 16 genes, CDC123, HLA-DQA1, HLA-DQB1, TAP2 have been previously reported to be related to both PD and respiratory disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46 CR47 CR48\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. After SMR, colocalization, and single-cell RNA sequencing analyses were conducted, we identified a shared genetic determinant that may contribute to the pathophysiology of PD and COPD. Our findings indicate that the genes HLA-DQA1 and HLA-DQB1 exhibit the strongest associations with these two diseases. The human leukocyte antigen (HLA) region on chromosome 6 harbours genes encoding components of the major histocompatibility complex (MHC). The HLA-DQ family, a subset of the HLA class II loci, encodes proteins crucial for antigen presentation. These proteins are also expressed in microglia and play dual roles in both clearing pathological protein deposits and producing proinflammatory factors, reactive oxygen species (ROS), and reactive nitrogen species, thereby causing neuronal death. This dual function highlights the critical role of the HLA region in neurodegenerative processes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Several genome-wide association studies have shown an association between the HLA locus and the risk of PD, while the significant downregulation of the HLA-DQA1 and HLA-DQB1 genes in PD patients further suggests that these genes may play a protective role in this disease [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Previous research has demonstrated that α-synuclein fragments can bind to MHC molecules, thereby increasing T-cell reactivity. This interaction is proinflammatory and may occur prior to the onset of motor symptoms, suggesting that inflammation may play a role in the early pathogenesis of PD [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Immunity and inflammation play a crucial role in PD, with the activation of microglia in the central nervous system and changes in peripheral immune cells being closely associated with neurodegeneration and disease progression [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In our previous study, neuroinflammation is closely related to the motor symptoms of PD and may represent an important target for alleviating these symptoms [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. We also identified plasma fibronectin [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] as a biomarker associated with the neuroinflammation, which may be involved in the motor symptom progression in PD. The potential mechanism is that fibronectin enters neurons through theαvβ3 integrin receptor, which in turn exacerbatesα-synuclein aggregation and mitochondrial dysfunction, ultimately promoting neurodegeneration [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Microglia, as key regulators of neuroinflammation, modulate it through their activation and release of inflammatory mediators, interacting with neurons, and influencing the propagation of α-synuclein [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, no studies to date have demonstrated the expression of HLA-DQ genes in the tissues or cells of PD patients. In our study, an analysis of the scRNA-seq data revealed that HLA-DQA1 was upregulated in blood B cells from PD patients, whereas its expression was downregulated in brain microglia, and HLA-DQB1 was downregulated in blood T cells and brain microglia. In light of our protein‒protein network and enrichment analysis findings, we propose that the primary functional mechanisms of the HLA‒DQ gene complex in PD may involve MHC class II complex binding and peptide antigen assembly with the MHC class II protein complex in B cells. Additionally, in T cells, enrichment analysis suggested that the complex is associated mainly with leukocyte-mediated immunity, including T-cell activation and antigen processing. Furthermore, in microglia, enrichment analysis suggested that the HLA-DQ gene complex is associated mainly with the methylation of histone-lysine and peptidyl-lysine, which is related to the production of proinflammatory cytokines [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Our results indicated that the expression of the HLA-DQ gene complex is altered not only in peripheral blood cells but also in microglia in PD. Combined with enrichment analysis, these findings suggested that HLA-DQ gene complex may contribute to the modulation of immune responses in both peripheral and central inflammation.\u003c/p\u003e\u003cp\u003eThe HLA complex plays a significant immunological role in lung function and respiratory diseases such as COPD and asthma [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. However, different studies have yielded varying results regarding the specific HLA subtypes involved. For instance, the HLA-DQA1*0301 subtype is associated with the risk of both PD and asthma. The underlying mechanisms remain unclear but may be related to immune modulation and tolerance, as well as a reduction in immune-mediated tissue damage. Similar to Parkinson's disease, studies demonstrating the expression of HLA genes in different tissues or cells of patients with COPD are lacking. Interestingly, through SMR analysis, we found that in COPD and asthma patients, the expression of the HLA-DQA1 and HLA-DQB1 genes is not only related to lung tissue and blood cells but also changed in multiple brain regions, indicating possible neural damage in respiratory disorders. Moreover, through scRNA-seq analysis, we also observed a similar pattern in COPD patients, with upregulation of HLA-DQA1 in B cells and downregulation of HLA-DQB1 in T cells, mirroring the findings in PD. In light of our enrichment analysis findings, we discovered that the primary functional mechanisms of the HLA-DQ gene in COPD patients are similar to those in PD patients and are associated with MHC II complex binding and leukocyte-mediated immunity. Therefore, in accordance with the hypothesized mechanisms linking the HLA-DQ gene to PD described above, we posit that the HLA-DQ gene plays a series of important immune-related roles in mediating respiratory dysfunction in PD patients. Further investigations are needed in the future.\u003c/p\u003e\u003cp\u003eThe intricate relationship between the lungs and brain, often referred to as the \"lung-brain axis,\" highlights the potential for respiratory dysfunction to influence neurological health. Lung diseases frequently co-occur with neurological disorders, possibly due to shared inflammatory pathways and the pathogenesis of the lung-brain axis [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Possible mechanisms include lung inflammation triggering brain inflammation and disrupting the blood-brain barrier, contributing to neurodegenerative processes [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Our findings underscore the importance of this axis in the context of PD, where respiratory disorders may exacerbate neurodegenerative processes through shared immune-mediated pathways. This highlights the need for further research into the lung-brain axis as a potential therapeutic target for mitigating the progression of PD and associated respiratory complications.\u003c/p\u003e\u003cp\u003eOur study has several strengths. First, the use of large-scale GWAS summary data for the analysis of associations among PD, lung function, and respiratory disorders provides the power to detect small but significant genetic associations that might be missed by using smaller datasets. This extensive dataset allows for a comprehensive investigation of the genetic underpinnings of the comorbidity between PD and respiratory disorders. Second, the application of MR analysis helps to establish potential causal relationships between PD and respiratory traits. By using genetic variants as instrumental variables, we can mitigate the impact of confounding factors and provide more robust evidence for the associations observed. Third, our colocalization analysis identified genetic loci shared between PD and respiratory disorders, allowing us to pinpoint specific genomic regions that may harbour causal variants. This approach provides a more precise understanding of the genetic overlap between these conditions. Finally, the integration of scRNA-seq data enables us to investigate cell type-specific gene expression patterns. By examining the expression of key candidate genes in B cells, T cells, and microglia, we can elucidate the potential biological mechanisms underlying comorbidities, such as immune-related pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere are several inherent limitations to our study. First, many previous studies have indicated that expiratory dysfunction in PD patients is closely related to on-off periods. However, our study was unable to investigate the differences in ventilatory dysfunction during the on-off periods in PD patients. Second, although the study provided evidence of changes in gene expression, there was a lack of functional validation of these changes. This makes the biological significance and clinical application prospects of the study results less clear. Third, we cannot exclude the potential influence of survival bias, as advanced idiopathic pulmonary fibrosis (IPF) may affect patient mortality before PD is diagnosed. Fourth, the GWAS databases for respiratory disorder-related traits and PD were mostly derived from individuals of European ancestry. Causal and genetic associations may differ according to ethnicity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur data support the causal influence of respiratory disorder\u0026ndash;related traits on the increased risk of progression in PD from a genetic perspective. We performed a multitrait analysis of PD and respiratory disorder traits, further prioritizing two shared causal variants between these traits. Our findings suggest possible shared mechanisms underlying PD and respiratory disorders. Our work indicates that the HLA gene complex and members of its interactome could offer new and potentially promising targets for preventive and therapeutic interventions aimed at alleviating respiratory symptoms in PD patients and slowing the progression of PD symptoms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCOLOC: colocalization\u003c/p\u003e\n\u003cp\u003eCOPD:chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eFEV1: forced expiratory volume in the frst second\u003c/p\u003e\n\u003cp\u003eFVC: forced vital capacity\u003c/p\u003e\n\u003cp\u003eHDL: high-definition likelihood\u003c/p\u003e\n\u003cp\u003ehdWGCNA: high-dimensional weighted gene co-expression network analysis\u003c/p\u003e\n\u003cp\u003eHY: Hoehn-Yahr stage\u003c/p\u003e\n\u003cp\u003eHY3: Hoehn-Yahr stage of 3 or more\u003c/p\u003e\n\u003cp\u003eIPF: idiopathic pulmonary fibrosis\u003c/p\u003e\n\u003cp\u003eLDSC: linkage-disequilibrium score regression method\u003c/p\u003e\n\u003cp\u003eMAGMA: multimarker analysis of GenoMic annotation\u003c/p\u003e\n\u003cp\u003eMMSE: Mini-Mental State Examination\u003c/p\u003e\n\u003cp\u003eMoCA: Montreal Cognitive Assessment\u003c/p\u003e\n\u003cp\u003eOSA: obstructive sleep apnoea\u003c/p\u003e\n\u003cp\u003ePEF: peak expiratory fow\u003c/p\u003e\n\u003cp\u003ePD: Parkinson`s Disease\u003c/p\u003e\n\u003cp\u003ePPI: protein protein interaction\u003c/p\u003e\n\u003cp\u003eSEADL: Schwab and England Activities of Daily Living Scale\u003c/p\u003e\n\u003cp\u003eseRNA-seq: single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003eSMR: summary data-based Mendelian randomization\u003c/p\u003e\n\u003cp\u003eUPDRS: Unified Parkinson`s Disease Rating Scale\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on previously published data. In all original studies, ethical approval and consent to participate were obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceived and designed the study: Xinhao Wang, Qing Wang. Performed the study: Xinhao Wang, Jintao Li, Hang Zhou, Zihao Wang, Zifeng Huang, Hailing Liu, Chunguang Li, Bihan Chi and Xiaobo Wei. Revised the paper for intellectual content: Xinhao Wang,Jintao Li, Yinghua Yu, Xiaoying Cui, Deng Chao and Qing Wang. Data statistics and analysis: Xinhao Wang and Zihao Wang. Wrote the paper: Xinhao Wang and Qing Wang. Xinhao Wang and Qing Wang had accessed and verified the data reported in the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (NO: U24A20694, 82471433), and Scientific Research Foundation of Guangzhou (NO: 202206010005) to QW; and Science and Technology Program of Guangzhou (No. 2025B01J3018) to XBW. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available, and the source of each dataset is described in the main text and Supplementary. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoftware used in this study are publicly available, and the version of each software has been specified in the main text and Supplementary.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBen-Shlomo Y, Darweesh S, Llibre-Guerra J, Marras C, San Luciano M, Tanner C. The epidemiology of Parkinson\u0026apos;s disease. \u003cem\u003eLancet\u003c/em\u003e 2024; \u003cstrong\u003e403:\u003c/strong\u003e 283\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eSchapira AHV, Chaudhuri KR, Jenner P. Non-motor features of Parkinson disease. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e 2017; \u003cstrong\u003e18:\u003c/strong\u003e 435\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eZhou H, Shen B, Huang Z, et al. Mendelian randomization reveals association between retinal thickness and non-motor symptoms of Parkinson\u0026apos;s disease. \u003cem\u003eNPJ Parkinsons Dis.\u003c/em\u003e 2023;\u003cstrong\u003e9:\u003c/strong\u003e163.\u003c/li\u003e\n\u003cli\u003eWang Q, Zheng J, Pettersson S, Reynolds R, Tan EK. The link between neuroinflammation and the neurovascular unit in synucleinopathies. \u003cem\u003eSci Adv. \u003c/em\u003e2023;\u003cstrong\u003e9:\u003c/strong\u003eeabq1141.\u003c/li\u003e\n\u003cli\u003eLiu H, Deng B, Zhou H, et al. QEEG indices are associated with inflammatory and metabolic risk factors in Parkinson\u0026apos;s disease dementia: An observational study. \u003cem\u003eEClinicalMedicine.\u003c/em\u003e 2022; \u003cstrong\u003e52:\u003c/strong\u003e101615.\u003c/li\u003e\n\u003cli\u003eKaczyńska K, Orłowska ME, Andrzejewski K. Respiratory abnormalities in Parkinson\u0026apos;s disease: what do we know from studies in humans and animal models? \u003cem\u003eInt J Mol Sci\u003c/em\u003e 2022; \u003cstrong\u003e23:\u003c/strong\u003e 3499.\u003c/li\u003e\n\u003cli\u003eAquino YC, Cabral LM, Miranda NC, Naccarato MC, Falquetto B, Moreira TS, et al. Respiratory disorders of Parkinson\u0026apos;s disease. \u003cem\u003eJ Neurophysiol\u003c/em\u003e 2022; \u003cstrong\u003e127:\u003c/strong\u003e 1\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eCheng CM, Wu YH, Tsai SJ, et al. Risk of developing Parkinson\u0026apos;s disease among patients with asthma: a nationwide longitudinal study. \u003cem\u003eAllergy\u003c/em\u003e. 2015;\u003cstrong\u003e70:\u003c/strong\u003e1605-1612. \u003c/li\u003e\n\u003cli\u003eZhang C, Deng J, Li Y, Niu G, Li M, Zhang B, et al. Abnormal pulmonary function in early Parkinson\u0026apos;s disease: a preliminary prospective observational study. \u003cem\u003eLung\u003c/em\u003e 2022; \u003cstrong\u003e200:\u003c/strong\u003e 325\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eNalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson\u0026apos;s disease: a meta-analysis of genome-wide association studies. \u003cem\u003eLancet Neurol\u003c/em\u003e 2019; \u003cstrong\u003e18:\u003c/strong\u003e 1091\u0026ndash;102.\u003c/li\u003e\n\u003cli\u003eIwaki H, Blauwendraat C, Leonard HL, Kim JJ, Liu G, Maple-Gr\u0026oslash;dem J, et al. Genomewide association study of Parkinson\u0026apos;s disease clinical biomarkers in 12 longitudinal patients\u0026apos; cohorts. \u003cem\u003eMov Disord\u003c/em\u003e 2019; \u003cstrong\u003e34:\u003c/strong\u003e 1839\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eShrine N, Guyatt AL, Erzurumluoglu AM, Jackson VE, Hobbs BD, Melbourne CA, et al. New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries. \u003cem\u003eNat Genet\u003c/em\u003e 2019; \u003cstrong\u003e51:\u003c/strong\u003e 481\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eCosentino J, Behsaz B, Alipanahi B, McCaw ZR, Hill D, Schwantes-An T-H, et al. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. \u003cem\u003eNat Genet\u003c/em\u003e 2023; \u003cstrong\u003e55:\u003c/strong\u003e 787\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eZhou W, Kanai M, Wu KH, Rasheed H, Tsuo K, Hirbo JB, et al. Global biobank meta-analysis initiative: powering genetic discovery across human disease. \u003cem\u003eCell Genom\u003c/em\u003e 2022; \u003cstrong\u003e2:\u003c/strong\u003e 100192.\u003c/li\u003e\n\u003cli\u003eCampos AI, Ingold N, Huang Y, Mitchell BL, Kho PF, Han X, et al. Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring. \u003cem\u003eSleep\u003c/em\u003e 2023; \u003cstrong\u003e46:\u003c/strong\u003e zsac308.\u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. \u003cem\u003eGenet Epidemiol\u003c/em\u003e 2013; \u003cstrong\u003e37:\u003c/strong\u003e 658\u0026ndash;65.\u003c/li\u003e\n\u003cli\u003eBowden J, Smith GD, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 2015; \u003cstrong\u003e44:\u003c/strong\u003e 512\u0026ndash;25.\u003c/li\u003e\n\u003cli\u003eBowden J, Smith GD, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. \u003cem\u003eGenet Epidemiol\u003c/em\u003e 2016; \u003cstrong\u003e40:\u003c/strong\u003e 304\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eBowden J, Spiller W, Del Greco M F, Sheehan N, Thompson J, Minelli C, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 2018; \u003cstrong\u003e47:\u003c/strong\u003e 2100.\u003c/li\u003e\n\u003cli\u003eBurgess S, Smith GD, Davies NM, Dudbridge F, Gill D, Glymour MM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. \u003cem\u003eWellcome Open Res\u003c/em\u003e 2023; \u003cstrong\u003e4:\u003c/strong\u003e 186.\u003c/li\u003e\n\u003cli\u003eZheng J, Baird D, Borges M-C, Bowden J, Hemani G, Haycock P, et al. Recent developments in Mendelian randomization studies. \u003cem\u003eCurr Epidemiol Rep\u003c/em\u003e 2017; \u003cstrong\u003e4:\u003c/strong\u003e 330\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. \u003cem\u003eNat Genet\u003c/em\u003e 2018; \u003cstrong\u003e50:\u003c/strong\u003e 693\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eBulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh P-R, et al. An atlas of genetic correlations across human diseases and traits. \u003cem\u003eNat Genet\u003c/em\u003e 2015; \u003cstrong\u003e47:\u003c/strong\u003e 1236\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eNing Z, Pawitan Y, Shen X. High-definition likelihood inference of genetic correlations across human complex traits. \u003cem\u003eNat Genet\u003c/em\u003e 2020; \u003cstrong\u003e52:\u003c/strong\u003e 859\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eRay D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer. \u003cem\u003ePLoS Genet\u003c/em\u003e 2020; \u003cstrong\u003e16:\u003c/strong\u003e e1009218.\u003c/li\u003e\n\u003cli\u003eWatanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. \u003cem\u003eNat Commun\u003c/em\u003e 2017; \u003cstrong\u003e8:\u003c/strong\u003e 1826.\u003c/li\u003e\n\u003cli\u003ede Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. \u003cem\u003ePLoS Comput Biol\u003c/em\u003e 2015; \u003cstrong\u003e11:\u003c/strong\u003e e1004219.\u003c/li\u003e\n\u003cli\u003eGiambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. \u003cem\u003ePLoS Genet\u003c/em\u003e 2014; \u003cstrong\u003e10:\u003c/strong\u003e e1004383.\u003c/li\u003e\n\u003cli\u003eSpargo TP, Gilchrist L, Hunt GP, Dobson RJB, Proitsi P, Al-Chalabi A, et al. Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics. \u003cem\u003eeLife\u003c/em\u003e 2024; \u003cstrong\u003e12:\u003c/strong\u003e RP88768.\u003c/li\u003e\n\u003cli\u003eZou Y, Carbonetto P, Wang G, Stephens M. Fine-mapping from summary data with the \u0026quot;sum of single effects\u0026quot; model. \u003cem\u003ePLoS Genet\u003c/em\u003e 2022; \u003cstrong\u003e18:\u003c/strong\u003e e1010299.\u003c/li\u003e\n\u003cli\u003eWallace C. A more accurate method for colocalisation analysis allowing for multiple causal variants. \u003cem\u003ePLoS Genet\u003c/em\u003e 2021; \u003cstrong\u003e17:\u003c/strong\u003e e1009440.\u003c/li\u003e\n\u003cli\u003eZhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. \u003cem\u003eNat Genet\u003c/em\u003e 2016; \u003cstrong\u003e48:\u003c/strong\u003e 481\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eGTEx Consortium. The GTEx consortium atlas of genetic regulatory effects across human tissues. \u003cem\u003eScience\u003c/em\u003e 2020; \u003cstrong\u003e369:\u003c/strong\u003e 1318\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eBarrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. \u003cem\u003eNucleic Acids Res\u003c/em\u003e 2013; \u003cstrong\u003e41:\u003c/strong\u003e D991\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eXiong L-L, Du R-L, Niu R-Z, Xue L-L, Chen L, Huangfu L-R, et al. Single-cell RNA sequencing reveals peripheral immunological features in Parkinson\u0026apos;s disease. \u003cem\u003eNPJ Parkinsons Dis\u003c/em\u003e 2024; \u003cstrong\u003e10:\u003c/strong\u003e 185.\u003c/li\u003e\n\u003cli\u003eHeo Y, Kim J, Hong SH, Kim WJ. Single cell transcriptomics in blood of patients with chronic obstructive pulmonary disease. \u003cem\u003eBMC Pulm Med\u003c/em\u003e 2025; \u003cstrong\u003e25:\u003c/strong\u003e 19.\u003c/li\u003e\n\u003cli\u003eUnterman A, Zhao AY, Neumark N, Schupp JC, Ahangari F, Cosme C, Jr., et al. Single-cell profiling reveals immune aberrations in progressive idiopathic pulmonary fibrosis. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e 2024; \u003cstrong\u003e210:\u003c/strong\u003e 484\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eMartirosyan A, Ansari R, Pestana F, Hebestreit K, Gasparyan H, Aleksanyan R, et al. Unravelling cell type-specific responses to Parkinson\u0026apos;s Disease at single cell resolution. \u003cem\u003eMol Neurodegener\u003c/em\u003e 2024; \u003cstrong\u003e19:\u003c/strong\u003e 7.\u003c/li\u003e\n\u003cli\u003eButler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. \u003cem\u003eNat Biotechnol\u003c/em\u003e 2018; \u003cstrong\u003e36:\u003c/strong\u003e 411\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eYang C, Zhang X, Chen J. Large language model consensus substantially improves the cell type annotation accuracy for scRNA-seq data. \u003cem\u003ebioRxiv\u003c/em\u003e 2025. doi: 10.1101/2025.04.10.647852.\u003c/li\u003e\n\u003cli\u003eFinak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. \u003cem\u003eGenome Biol\u003c/em\u003e 2015; \u003cstrong\u003e16:\u003c/strong\u003e 278.\u003c/li\u003e\n\u003cli\u003eMorabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. \u003cem\u003eCell Rep Methods\u003c/em\u003e 2023; \u003cstrong\u003e3:\u003c/strong\u003e 100498.\u003c/li\u003e\n\u003cli\u003eXu S, Hu E, Cai Y, Xie Z, Luo X, Zhan L, et al. Using clusterProfiler to characterize multiomics data. \u003cem\u003eNat Protoc\u003c/em\u003e 2024; \u003cstrong\u003e19:\u003c/strong\u003e 3292\u0026ndash;320.\u003c/li\u003e\n\u003cli\u003eSzklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. \u003cem\u003eNucleic Acids Res\u003c/em\u003e 2021; \u003cstrong\u003e49:\u003c/strong\u003e D605\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eChung SJ, Kim MJ, Kim J, Ryu HS, Kim YJ, Kim SY, et al. Association of type 2 diabetes GWAS loci and the risk of Parkinson\u0026apos;s and Alzheimer\u0026apos;s diseases. \u003cem\u003eParkinsonism Relat Disord\u003c/em\u003e 2015; \u003cstrong\u003e21:\u003c/strong\u003e 1435\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eYu E, Ambati A, Andersen MS, Krohn L, Estiar MA, Saini P, et al. Fine mapping of the HLA locus in Parkinson\u0026apos;s disease in Europeans. \u003cem\u003eNPJ Parkinsons Dis\u003c/em\u003e 2021; \u003cstrong\u003e7:\u003c/strong\u003e 84.\u003c/li\u003e\n\u003cli\u003eArtigas MS, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, et al. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. \u003cem\u003eNat Genet\u003c/em\u003e 2011; \u003cstrong\u003e43:\u003c/strong\u003e 1082\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eWain LV, Shrine N, Miller S, Jackson VE, Ntalla I, Soler Artigas M, et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. \u003cem\u003eLancet Respir Med\u003c/em\u003e 2015; \u003cstrong\u003e3:\u003c/strong\u003e 769\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eFawcett KA, Demidov G, Shrine N, et al. Exome-wide analysis of copy number variation shows association of the human leukocyte antigen region with asthma in UK Biobank. \u003cem\u003eBMC Med Genomics.\u003c/em\u003e 2022;\u003cstrong\u003e15\u003c/strong\u003e:119. \u003c/li\u003e\n\u003cli\u003eAliseychik MP, Andreeva TV, Rogaev EI. Immunogenetic factors of neurodegenerative diseases: the role of HLA class II. \u003cem\u003eBiochemistry\u003c/em\u003e 2018; \u003cstrong\u003e83:\u003c/strong\u003e 1104\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eKulski JK, Suzuki S, Shiina T, Pfaff AL, K\u0026otilde;ks S. Regulatory SVA retrotransposons and classical HLA genotyped-transcripts associated with Parkinson\u0026apos;s disease. \u003cem\u003eFront Immunol.\u003c/em\u003e 2024;\u003cstrong\u003e15\u003c/strong\u003e:1349030.\u003c/li\u003e\n\u003cli\u003eSulzer D, Alcalay RN, Garretti F, Cote L, Kanter E, Agin-Liebes J, et al. T cells from patients with Parkinson\u0026apos;s disease recognize \u0026alpha;-synuclein peptides. \u003cem\u003eNature\u003c/em\u003e 2017; \u003cstrong\u003e546:\u003c/strong\u003e 656\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eRoodveldt C, Bernardino L, Oztop-Cakmak O, et al. The immune system in Parkinson\u0026apos;s disease: what we know so far. \u003cem\u003eBrain.\u003c/em\u003e 2024;\u003cstrong\u003e147\u003c/strong\u003e:3306-3324.\u003c/li\u003e\n\u003cli\u003eShi FD, Yong VW. Neuroinflammation across neurological diseases. \u003cem\u003eScience\u003c/em\u003e. 2025; \u003cstrong\u003e388\u003c/strong\u003e: eadx0043.\u003c/li\u003e\n\u003cli\u003eXie F, Shen B, Luo Y, Zhou H, Xie Z, Zhu S, et al. Repetitive transcranial magnetic stimulation alleviates motor impairment in Parkinson\u0026apos;s disease: association with peripheral inflammatory regulatory T-cells and SYT6. \u003cem\u003eMol Neurodegener\u003c/em\u003e 2024; \u003cstrong\u003e19:\u003c/strong\u003e 80.\u003c/li\u003e\n\u003cli\u003eZhu S, Li H, Huang Z, Zeng Y, Huang J, Li G, et al. Plasma fibronectin is a prognostic biomarker of disability in Parkinson\u0026apos;s disease: a prospective, multicenter cohort study. \u003cem\u003eNPJ Parkinsons Dis\u003c/em\u003e 2025; \u003cstrong\u003e11:\u003c/strong\u003e 1.\u003c/li\u003e\n\u003cli\u003eHuang Z, Zheng J, Yuan F, et al. Fibrinogen exacerbates \u0026alpha;-synuclein aggregation and mitochondrial dysfunction via alpha5beta3 integrin in Parkinson\u0026apos;s disease. \u003cem\u003eJ Adv Res.\u003c/em\u003e Published online May 25, 2025. \u003c/li\u003e\n\u003cli\u003eIsik S, Yeman Kiyak B, Akbayir R, Seyhali R, Arpaci T. Microglia Mediated Neuroinflammation in Parkinson\u0026apos;s Disease. \u003cem\u003eCells\u003c/em\u003e. 2023;\u003cstrong\u003e12\u003c/strong\u003e:1012. \u003c/li\u003e\n\u003cli\u003eQu L, Yin T, Zhao Y, Lv W, Liu Z, Chen C, et al. Histone demethylases in the regulation of immunity and inflammation. \u003cem\u003eCell Death Discov\u003c/em\u003e 2023; \u003cstrong\u003e9:\u003c/strong\u003e 188.\u003c/li\u003e\n\u003cli\u003eLanouette S, Mongeon V, Figeys D, Couture JF. The functional diversity of protein lysine methylation. \u003cem\u003eMol Syst Biol\u003c/em\u003e 2014; \u003cstrong\u003e10:\u003c/strong\u003e 724.\u003c/li\u003e\n\u003cli\u003eMkorombindo T, Tran-Nguyen TK, Yuan K, Zhang Y, Xue J, Criner GJ, et al. HLA-C and KIR permutations influence chronic obstructive pulmonary disease risk. \u003cem\u003eJCI Insight\u003c/em\u003e 2021; \u003cstrong\u003e6:\u003c/strong\u003e e150187.\u003c/li\u003e\n\u003cli\u003eClay SM, Schoettler N, Goldstein AM, et al. Fine-mapping studies distinguish genetic risks for childhood- and adult-onset asthma in the HLA region. \u003cem\u003eGenome Med.\u003c/em\u003e 2022;\u003cstrong\u003e14\u003c/strong\u003e(1):55. \u003c/li\u003e\n\u003cli\u003ePark H, Lee CH. The Impact of Pulmonary Disorders on Neurological Health (Lung-Brain Axis). \u003cem\u003eImmune Netw\u003c/em\u003e. 2024;\u003cstrong\u003e24\u003c/strong\u003e(3):e20. \u003c/li\u003e\n\u003cli\u003eMapunda JA, Tibar H, Regragui W, Engelhardt B. How does the immune system enter the brain? \u003cem\u003eFront Immunol.\u003c/em\u003e 2022;\u003cstrong\u003e13\u003c/strong\u003e:805657.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, respiratory disorders, genetic association, human leukocyte antigen, neuroinflammation","lastPublishedDoi":"10.21203/rs.3.rs-7234326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7234326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Respiratory disorders are gaining recognition as common comorbidities in Parkinson's disease (PD) patients, and these comorbidities have significant implications for PD patient outcomes and mortality. However, the genetic basis and potential causal relationships between PD and respiratory dysfunction remain unclear. Understanding these associations could provide insights into shared pathophysiological mechanisms and identify potential therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e We conducted a genetic association study using large-scale genome-wide association study (GWAS) summary data for PD (n = 482,730), lung function (n = 321,047), chronic obstructive pulmonary disease (COPD; n = 325,027), idiopathic pulmonary fibrosis (IPF;n = 953,873), obstructive sleep apnoea (OSA;n = 159,255) and asthma (n = 1,376,071) in individuals of European ancestry. We employed Mendelian randomization (MR), colocalization and summary data-based Mendelian randomization (SMR) analysis to evaluate potential causal relationships and identify shared genetic loci. Besides, we conductedsingle-cell RNA sequencing (scRNA-seq) and enrichment analysis to investigate cell type-specific gene expression patterns and their potential roles in PD and respiratory disorders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e MR indicates that obstructive ventilatory dysfunction predicts greater motor impairment, whereas restrictive ventilatory dysfunction predicts cognitive decline in PD. Genetically predicted PD increases IPF risk (odds ratio [OR] = 1.14) and reduce the risk of OSA (OR = 0.97). Colocalization identifies 26 loci with shared causal variants; the HLA-DQA1 and HLA-DQB1 genes emerge as key candidates. SMRlinks coupled with expression quantitative trait loci from lung, blood and brain regions demonstrates that altered expression of these genes is associated with disease risk. Single-cell RNA sequencing of peripheral blood mononuclear cells and substantia nigra pars compacta samples shows distinct expression patterns of HLA-DQA1 and HLA-DQB1 in B cells, T cells and microglia from patients with PD and COPD. Enrichment analyses implicate major histocompatibility complex class II binding, T-cell activation and pro-inflammatory cytokine production.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e We conducted a multitrait analysis focusing on PD and respiratory disorder traits, and further identified two shared causal variants that are prioritized between these traits. These findings suggest that shared genetic mechanisms underlie PD and respiratory disorders, highlighting the potential immunomodulatory role of the HLA gene complex and its interactome in mediating these associations.\u003c/p\u003e","manuscriptTitle":"Elucidating the Multitrait Association between Parkinson’s Disease and Respiratory Disorders: HLA gene complex as a causal nexus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 10:42:01","doi":"10.21203/rs.3.rs-7234326/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb76e943-326f-42bc-b150-5bcfd6b3d72e","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T17:24:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-04 10:42:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7234326","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7234326","identity":"rs-7234326","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}