Proteome-wide association study identifies novel Alzheimer's disease- associated proteins

Proteome-wide association study identifies novel Alzheimer's disease- associated proteins | 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 Proteome-wide association study identifies novel Alzheimer's disease- associated proteins Lingyun Sun, Guikang Wei, Feiyang Ji, Yihong Ding, Jiayao Fan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4648743/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 Alzheimer's disease (AD) is a progressive neurodegenerative disease, with a critical shortage of effective prevention and treatment options. Here, we aimed to identify proteins whose genetically regulated plasma levels were associated with AD and its related phenotypes. Methods An integrative proteome-wide search using Olink-based plasma proteomes (N = 45,540) from the UK Biobank Pharma Proteomics Project (UKB-PPP) and a large-scale genome-wide association study (GWAS) for AD (N case = 111,326, N control = 677,663) was employed to identify AD-associated proteins. Cohort studies for AD or mild cognitive disorder (MCD) with average follow-ups of 13.7 years, alongside cross-sectional studies for the volume of whole hippocampus (WH) and white matter hyperintensities (WMH) were performed to provide additional supports. Results We identified 30 AD-associated proteins through a genetic-informed proteome-wide association study (PWAS). Among these, 14 proteins (including TREM2 and GRN) have been previously reported to be associated with AD. No clear evidence has linked the remaining 16 proteins (including PILRB, FES, and HDGF) with AD. PILRB and FES were further supported by cohort studies for AD and/or MCD. A higher plasma abundance of HDGF was found to be associated with a lower volume of whole-hippocampus and an increased risk of AD, consistent with a previous study which showed a potentially risk role of HDGF for AD in both brain tissues and cerebrospinal fluid. The protein-protein interaction analysis linked PILRB with ABCA7, an AD-related protein involved in the immune system. Conclusions The integrative genetic-informed proteome-wide scan provides promising AD-associated proteins for further mechanistic studies. Alzheimer’s disease PWAS plasma protein mild cognitive disorder brain imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Alzheimer's disease is a neurodegenerative disorder characterized by cognitive decline and is the leading cause of dementia among the elderly population. It is estimated that there are now more than 50 million people with dementia worldwide[ 1 ], and the prevalence of dementia is expected to double in Europe and triple globally by 2050[ 2 ]. AD is undoubtedly a public health problem in the world, which not only causes serious physical and psychological burdens on the patients but also imposes substantial challenges and burdens on families and society. Compared with the heavy burden of AD, the available drugs for AD treatment are extremely scarce. Over the past two decades, the FDA has fully approved only a few drugs for the early treatment of AD[ 3 , 4 ]. Consequently, there is an urgent need to identify potential causal proteins for AD which can be further investigated as candidate therapeutic targets for altering the clinical trajectory of AD. Previous studies have found some biomarkers of AD through cohort studies or case-control studies[ 5 – 7 ]. Although, biomarkers identified by prospective cohort studies could predict the risk of diseases, they may not have a causal role on diseases[ 8 , 9 ]. A genetic-informed association study such as TWAS and PWAS studies can avoid reverse causation and are less susceptible to confounding factors. TWAS and PWAS can be regarded as "natural gene or protein knockdown experiments at the population level" where the risk of long-term events can be compared between groups with genetically regulated low expression and high expression of a particular molecule. Many studies have analyzed the association of AD from the aspects of transcriptome and proteome, and found some potential AD-associated molecules[ 10 , 11 ]. However, purely genetic-informed approaches may result in false positive findings due to widespread pleiotropic effects of genetic variants in linkage disequilibrium (LD) with the variants in prediction models[ 12 , 13 ]. Consequently, genetic-informed association studies may complement cohort studies by leveraging their respective strengths. Here, we sought to find potentially causal proteins in plasma for AD by combining the study designs of a genetically informed PWAS and a cohort study. Additionally, we considered early-stage mild cognitive disorder and brain imaging features that may occur in early stage of Alzheimer's disease as secondary outcomes to support the identification of AD-related proteins. Methods The UK Biobank The UK Biobank comprises data from a population-based cohort study that recruited more than 500,000 participants aged from 40–69 who attended 1 of the 22 assessment centers across England, Scotland, and Wales for baseline assessment between 2006 and 2010[ 14 ]. The UKB study received ethical approval from the National Health and Social Care Information Management Board and the NHS North West Multi-Centre Research Ethics Board. Participants were given written informed consent to collect questionnaires and biological data. Clinical outcomes, including AD and mild cognitive disorder diagnosis, were accessed through the hospital admission records, death certificates, primary care records, and self-reports during the follow-up period from 2006 to 2023. Data acquisition and analyses in this study were conducted under UKB Application No.102158. In order to reduce the impact of population structure on genetic data analysis[ 15 ], only ancestrally European populations were included in this study. The UK Biobank Pharma Proteomics Project (UKB-PPP)[ 16 ] characterized the plasma proteomics of 54,219 UKB participants at baseline. UKB-PPP used the antibody-based Olink Explore 3072 PEA, measuring 2,941 protein analytes and capturing 2,923 unique proteins in plasma samples collected from UK Biobank participants. The proteins were measured across eight protein panels: cardiometabolic, cardiometabolic II, inflammation, inflammation II, neurology, neurology II, oncology and oncology II. Details of the protein data processing and quality control are provided in the UKB protocol ( https://biobank.ndph.ox.ac.uk/ukb/ukb/docs/Olink_1536_B0_to_B7_Normalization.pdf ). We imputed the missing values using random forests method provided by the “missForest” R package ( https://cran.r-project.org/web/packages/missForest/ ). After excluding non-European populations and proteins with a deletion rate greater than 50%[ 17 ], we finally analyzed 2,817 proteins from 45,540 participants. GWAS data source for AD We adopted the latest AD summary statistics from a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls[ 18 ]. Stage I samples (39,106 clinically diagnosed AD cases, 46,828 proxy-AD cases and 401,577 controls) came from EADB, GR@ACE, EADI, GERAD/PERADES, DemGene, Bonn, the Rotterdam study, the CCHS study, the NxC, and the UKB. Stage II samples (25,392 AD cases and 276,086 controls) are from the ADGC, CHARGE, and FinnGen consortia. They performed standard quality control on variants and samples from all datasets individually. Ethical approval and written informed consent to participate had been obtained in all studies. Ascertainment of AD and mild cognitive disorder Cases of AD and mild cognitive disorder were identified through hospital admissions and death registry records of the participants. Inpatient admissions records were sourced from the Hospital Episode Statistics for England, the Scottish Morbidity Record for Scotland, and the Patient Episode Database for Wales. Death registry records were obtained from the NHS England for England and Wales, and from the Information and Statistics Division for Scotland. Both primary and secondary hospital diagnoses, as well as causes of death, were recorded using the International Classification of Diseases (ICD-10) coding system. The ICD codes were used to identify cases of AD and mild cognitive disorder (Supplementary Table 12). These cases were selected and validated by the UK Biobank outcome adjudication group. Brain magnetic resonance imaging Brain imaging data from magnetic resonance imaging (MRI) was acquired by standardized image acquisition protocols and analysis pipeline. Detailed information of the procedure can be found in the UKB protocol ( https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/brain_mri.pdf ). Participants underwent MRI scan from 2014 to 2023 at three imaging analysis centers that utilize similar scanners (3T Siemens Skyra with a 32-channel head coil). T1 structural is acquired using straight sagittal orientation (i.e., with the field-of-view aligned to the scanner axes). FSL and FreeSurfer were used to standard Siemens on-scanner conversion of complex multicoil data was carried out for the T1 data. Quality-controlled T1-weighted magnetic resonance imaging data were used for the analysis of the associations between proteins and hippocampus volume as well as the volume of white matter hyperintensities. Proteome-wide association study for AD To identify proteins whose genetically regulated expression is associated with AD, we performed PWAS analyses by integrating GWAS summary statistics of AD from Céline et al[ 18 ]. and plasma protein data from UK Biobank Pharma Proteomics Project using the PrediXcan framework[ 19 ]. PrediXcan is a gene-based association test that prioritizes genes that are likely to be causal for the phenotype. The whole working process of PrediXcan can be subdivided into three steps. Firstly, the model is trained by an elastic net regression model using the genetic variants and plasma protein expression data from a reference panel, which refers to 45,540 participants from UKB-PPP in our study. Second, the prediction model can be applied to a large GWAS data to predict gene-regulated protein expression levels. Finally, the associations between genetically regulated expression and trait of interests can be estimated. Here, we implemented the summary statistics-based PrediXcan framework to probe AD-associated plasma proteins. SMR/HEIDI and Bayesian colocalization analysis SMR[ 20 ] was used to estimate the signal sharing between protein quantitative trait loci (pQTL) and GWAS for AD while taking into account the local linkage disequilibrium. We estimated SNP-protein abundance association and identified protein quantitative trait loci (pQTL) from the UKB-PPP dataset by linear regression. Next, we used the pQTL results and the AD GWAS summary statistics to perform SMR and HEIDI test. Since we considered the SMR test as complementary analysis for PWAS, we used nominally significant genes at P < 0.05. P ≥ 0.05 from HEIDI indicated “linkage” as suggested by the method paper[ 20 ]. To determine whether the same genetic variant is responsible for associations observed in GWAS for AD and in pQTL studies, Bayesian colocalization analysis was conducted by implementing the “coloc” R package ( http://cran.r-project.org/web/packages/coloc ). COLOC provides the probability of five hypotheses: H0 refers to no pQTL and no GWAS association, H1 and H2 refer to association with pQTL but no GWAS association or association with GWAS but no pQTL association, H3 refers to pQTL and GWAS association but independent signals, and finally H4 refers to shared pQTL and GWAS association. P0, P1, P2, P3, and P4 are the corresponding probabilities for each configuration. We focused on posterior probability hypothesis 4 (PPH4), with strong evidence of colocalization at PPH4 ≥ 0.5[ 11 ]. Perspective cohort study To assess the association between plasma protein levels and the risk of AD as well as the risk of MCD, we conducted cohort studies. The baseline plasma protein levels were considered as quantitative exposure. The demographic characteristics of the participants from the UK Biobank were considered as covariates in regression models. Participants were followed up, and the disease onset were recorded from baseline to the last AD or MCD patient diagnosis in 2023. For the cohort study of AD, participants of non-European descent and those with AD at baseline or within the first two years of follow-up were excluded. In total, 45,511 participants and 2,817 proteins were included in the analysis for AD. Cox regression analysis was performed, with follow-up time as the time variable and AD diagnosis status as the outcome variable, adjusting for age and sex. Hazard ratios (HR) and their 95% confidence intervals were estimated. P -values were adjusted for multiple comparisons using the Benjamini-Hochberg FDR correction. In addition, we also conducted logistic regression to identify AD-associated proteins. To assess the robustness of our findings, we conducted the following sensitivity analysis: first, to investigate the potential impact of severe baseline diseases on the results, we excluded participants who had the following diseases at baseline: cancer, type 2 diabetes, stroke, depression, epilepsy, schizophrenia, and cardiovascular diseases (including hypertension, angina, and heart attack/myocardial infarction). After excluding these participants, we estimated the effect of each protein on the risk of AD using Cox regression by adjusting age, sex, education, and physical activity. For the cohort study of mild cognitive disorder, by excluding non-European participants and those with MCD at baseline, 45,532 participants and 2,817 proteins were included. Similarly, we performed Cox regression analysis for mild cognitive disorder by adjusting for age and sex and obtained the HR for each protein and the corresponding P -value. Association study between brain imaging features and plasma proteins To access the association between plasma protein levels and brain imaging features included hippocampus volume and the volume of white matter hyperintensities, we implemented linear regression models. After excluding participants with missing data on hippocampus volume and white matter hyperintensities volume, we conducted analysis using the hippocampus volume of 5,248 participants and the white matter hyperintensities volume of 5,139 participants. Firstly, log transformation was performed on the brain image information of the participants. After the transformation, the volume of hippocampus and the volume of white matter signal enhancement showed approximately normal distribution. We then performed linear regression using the converted data. Protein-protein Interaction To connect and embed the identified AD-associated proteins with well-established AD-related proteins[ 21 – 27 ], we performed an analysis through protein-protein interactions (PPI) by implementing STRING[ 28 ]. STRING is ( https://string-db.org/ ) a database systematically collects and integrates both physical interactions as well as functional associations protein–protein interactions. The data originates from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information. Result The workflow of the study The overall study workflow was shown in Fig. 1 . To identify potentially causal proteins, we systematically linked high-throughput proteomics in the blood to AD. On the one hand, we used plasma protein data obtained from the UKB-PPP and findings from the latest AD GWAS by Céline et al[ 18 ] (111,326 AD cases, 677,663 controls) to perform a PWAS analysis using a genetically informed framework. PWAS is designed to detect gene-phenotype associations mediated by changes in protein levels. PWAS integrates protein expression levels with genome-wide association studies to estimate the associations between genetically regulated protein levels and trait of interests. Then, we used the summary data-based Mendelian randomization followed by its accompanying heterogeneity in dependent instruments (SMR and HEIDI)[ 20 ] and Bayesian colocalization analysis (COLOC)[ 29 ] to provide additional support for PWAS-identified proteins. On the other hand, we performed a cohort study to probe AD-associated proteins based on the protein levels at baseline, followed by sensitivity analyses. In addition, we provided evidence between potentially causal proteins and early-stage phenotypes of AD including mild cognitive disorder and brain imaging features. Lastly, we embedded the newly identified proteins with well-studied AD-related proteins by a protein-protein interaction network. PWAS of Alzheimer’s disease To identify genetically regulated proteins associated with AD, we performed a PWAS by integrating the AD GWAS results and human plasma proteomes profile. We utilized data from the latest large-scale AD GWAS, which included 111,326 AD cases and 677,663 controls of European descent. The UK Biobank Pharma Proteomics Project, encompassing 45,540 ancestrally European participants, served as the data source for establishing SNP-protein associations. A total of 2,817 proteins were included for analysis after quality control, among which 1,146 proteins were predictable (Pearson’s correlation r > 0.1) and their prediction models were generated by elastic net regression (Supplementary Table 1). The PWAS identified 30 proteins whose genetically predicted levels were found to be associated with AD (false discovery rate, FDR < 0.05, Fig. 2 , Table 1 and Supplementary Table 1). Of the 30 AD risk proteins identified by PWAS, 14 (CR1, PILRA, ACE, EPHX2, CD2AP, TREM2, CD55, GRN, IL34, LPA, SIRPA, ACHE, TREML2, and C1R)[ 30 – 45 ] have been reported to be associated with AD. The remaining 16 proteins (PILRB, FES, CR2, C1S, LRRC37A2, PRSS53, HAVCR2, TNFSF13B, HDGF, GC, LRP11, ITGAL, IDUA, DHRS4L2, SH3BP1, and BPIFB2) were newly discovered. Detailed information can be found in Table 1 . In summary, we confirmed the associations between 14 proteins and AD, and identified another 16 proteins whose genetically determined abundance in plasma were associated with the risk of AD. Table 1 PWAS-identified AD-associated proteins. Protein CHR Z score Effect size P value New identified CR1 1 9.407 0.164 < 1E-320 No[ 32 ] PILRB 7 8.529 0.081 < 1E-320 Yes PILRA 7 8.373 0.090 < 1E-320 No[ 46 ] ACE 17 -7.961 -0.170 1.78E-15 No[ 35 ] EPHX2 8 7.714 0.667 1.22E-14 No[ 35 ] TREM2 6 -7.518 -0.444 5.57E-14 No[ 44 ] CD2AP 6 7.153 0.266 8.48E-13 No[ 43 ] GRN 17 -5.501 -0.323 3.77E-08 No[ 38 ] CD55 1 -5.384 -0.127 7.29E-08 No[ 41 ] IL34 16 -5.071 -0.063 3.96E-07 No[ 33 ] LPA 6 -4.575 -0.052 4.77E-06 No[ 31 ] HAVCR2 5 -4.422 -0.238 9.76E-06 Yes PRSS53 16 -4.354 -0.051 1.34E-05 Yes C1S 12 4.351 0.097 1.36E-05 Yes SIRPA 20 4.162 0.029 3.16E-05 No[ 47 ] TREML2 6 4.098 0.098 4.17E-05 No[ 42 ] CR2 1 4.060 0.136 4.90E-05 Yes TNFSF13B 13 -4.046 -0.141 5.21E-05 Yes LRRC37A2 17 -4.027 -0.047 5.64E-05 Yes ACHE 7 -3.971 -0.101 7.15E-05 No[ 30 ] HDGF 1 3.774 0.029 1.61E-04 Yes IDUA 4 3.735 0.053 1.88E-04 Yes GC 4 3.629 0.034 2.85E-04 Yes C1R 12 3.568 0.109 3.60E-04 No[ 40 ] LRP11 6 -3.516 -0.053 4.39E-04 Yes DHRS4L2 14 -3.468 -0.127 5.24E-04 Yes ITGAL 16 -3.441 -0.235 5.80E-04 Yes SH3BP1 22 3.404 0.148 6.64E-04 Yes ZBTB16 11 3.321 0.088 8.98E-04 Yes FES 15 3.256 0.228 1.13E-03 Yes This table gives the z-scores and effect size for the AD PWAS associations with their corresponding P values for proteins passed the FDR correction. Additionally, to understand whether these 30 proteins identified by PWAS were specifically distributed in certain tissues, we visualized the gene expression profile across 54 tissues from the GTEx project by a heatmap. The average log-transformed transcript per million (TPM) are shown in Fig. 3 and Supplementary Table 13. The heatmap revealed genes with specific expression patterns including the ones highly expressed in brain tissues as well as other tissues. Notably, PILRB and HDGF exhibited ubiquitous expression across nearly all examined tissues. This observation implies that the regulatory mechanisms governing these genes expression are likely systemic, affecting a broad range of tissues, including brain tissues. Mendelian randomization and colocalization analyses support the role of plasma protein on AD PWAS may result in false positive identification due to linkage or pleiotropy effect, to further priority the associations identified in PWAS, we performed SMR and HEIDI[ 20 ] analysis and Bayesian colocalization analysis[ 29 ] on the basis of different hypothesis for causality inference. We firstly used the genotype data and plasma protein data for pQTL analysis. Then, we used the pQTL information and the same AD GWAS data for SMR and HEIDI analysis. Among the 30 proteins identified by PWAS, SMR analysis supported most of the findings (25 / 30, SMR P < 0.05, Table 2 and Supplementary Table 2) while the HEIDI test, a post filtering step in SMR using multiple SNPs in a cis-pQTL region to distinguish pleiotropy from linkage, suggested that the 8 of the associations may attribute to linkage (HEIDI P < 0.05, Table 2 and Supplementary Table 2). Token together, the SMR and HEIDI supported the role of 17 proteins (namely, DHRS4L2, ITGAL, C1R, CD2AP, HDGF, IL34, GRN, C1S, TNFSF13B, ZBTB16, TREM2, PILRB, GC, FES, ACHE, LRP11, and SIRPA), including eight novel ones, on the risk of AD. Furthermore, colocalization analysis was performed by implementing a Bayesian colocalization approach COLOC. COLOC analysis showed a concordant signal distribution between pQTL and GWAS for TREM2, GRN, CIR, DHRS4L2, and SH3BP1.Notablly, four of them (namely, TREM2, GRN, C1R, and DHRS4L2) were also supported by the SMR and HEIDI analysis (Table 2 and Supplementary Table 3). In summary, 18 of the 30 proteins were supported by additional genetically informed approaches with more strict assumptions, showing the robustness of the identifications. Table 2 COLOC and SMR analysis of the 30 significant proteins in AD PWAS. COLOC SMR Protein CHR PPH4 SMR P HEIDI P CR1 1 8.22E-26 3.88E-29 4.81E-06 PILRA 7 8.45E-04 1.75E-15 1.29E-03 PILRB 7 1.82E-10 1.70E-15 3.98E-01 ACE 17 2.25E-09 3.49E-11 2.92E-02 EPHX2 8 2.00E-23 1.51E-06 8.15E-03 TREM2 6 1.00E + 00 3.05E-09 3.89E-01 CD2AP 6 8.96E-02 8.72E-13 1.98E-01 GRN 17 9.92E-01 4.45E-06 2.61E-01 CD55 1 1.01E-04 5.48E-08 2.06E-03 IL34 16 4.83E-03 1.23E-05 2.32E-01 LPA 6 5.45E-02 1.48E-01 3.81E-02 HAVCR2 5 3.70E-02 5.79E-02 5.56E-02 PRSS53 16 8.60E-05 8.10E-06 2.64E-04 C1S 12 6.02E-03 2.03E-05 2.66E-01 SIRPA 20 8.22E-03 1.42E-05 9.57E-01 TREML2 6 3.66E-08 6.90E-01 1.59E-08 CR2 1 2.49E-22 4.64E-01 1.06E-04 TNFSF13B 13 2.57E-01 7.93E-03 2.83E-01 LRRC37A2 17 9.95E-03 9.91E-01 NA ACHE 7 9.74E-02 1.35E-03 5.84E-01 HDGF 1 1.04E-02 4.87E-04 2.32E-01 IDUA 4 5.44E-06 1.23E-07 7.50E-04 GC 4 5.65E-03 2.68E-04 4.89E-01 C1R 12 9.43E-01 2.14E-05 1.79E-01 LRP11 6 1.65E-02 9.02E-04 8.07E-01 DHRS4L2 14 5.59E-01 2.77E-04 1.32E-01 ITGAL 16 5.37E-02 1.42E-02 1.58E-01 SH3BP1 22 5.00E-01 7.85E-04 1.23E-02 ZBTB16 11 3.46E-02 1.43E-04 3.78E-01 FES 15 7.14E-02 1.68E-02 5.63E-01 For the 30 proteins identified by AD PWAS, the result of Bayesian posterior probability of pQTL-GWAS colocalization is represented by the regional PPH4.The P values for the SMR and HEIDI tests are presented. NA indicates not enough variants for a HEIDI test. Prospective cohort study of AD proteomics We performed a cohort study to identify AD-associated plasma proteins. At baseline, participants with no medical records or self-reported AD diagnosis were included in the study. Subjects diagnosed with AD within the first two years of follow-up were excluded. A total number of 45,511 participants with plasma protein measurement by Olink were finally included in the study. After 13.7 years follow up on average, we observed 449 new AD cases. Using a proportional Cox regression model, we tested the association between proteins and AD risk while adjusting for age and sex. As shown in Fig. 4 A and Supplementary Table 4, 204 proteins were found to be associated with AD ( P < 0.05), leading by GFAP (HR = 1.959, 95%CI = 1.763–2.178, P = 9.86E-36), APOE (HR = 0.533, 95%CI = 0.484–0.587, P = 1.78E-37), and 23 proteins passed the multiple comparison correction line ( FDR < 0.05). Notably, among the 18 potentially causal proteins of AD identified by PWAS and SMR/COLOC (Table 2 ), PILRB (HR = 1.123, 95% CI = 1.023–1.232, P = 1.49E-02) and FES (HR = 1.138, 95% CI = 1.032–1.255, P = 9.57E-03) were also evidenced by the cohort study with concordant sign of effects (Supplementary Tale 4 and 5). In addition, consistent results were observed using a Logistic regression model without taking into account the time of events. By excluding individuals with diseases (including cancer, type 2 diabetes, stroke, depression, epilepsy, schizophrenia, and cardiovascular-related diseases), that may influence plasma protein levels at baseline and adjusting for age, sex, education, and physical activity, we re-conducted proportional Cox regression and Logistic regression models to identify the association between proteins and AD. The results of sensitivity analysis were consistent with the primary findings (Fig. 4 B, Supplementary Tables 6 and 7). Similarly, GFAP (HR = 2.366, 95%CI = 1.954–2.865, P = 1.12E-18) and APOE (HR = 0.519, 95%CI = 0.441–0.611, P = 3.51E-15) showed the highest significant levels in the Cox regression model (Supplementary Table 6). The baseline abundance of PILRB in plasma was found to be positively associated with AD risk in both Cox and logistic regression (Cox: HR = 1.286, 95% CI = 1.078–1.535, P = 5.31E-03; logistic: OR = 1.291, 95%CI = 1.078–1.546, P = 5.40E-03). Detailed information can be found in Supplementary Tables 6 and 7. Association study between protein and early traits of AD In order to find the proteins associated with the early trait of AD, we first conducted a Cox regression analysis for each protein using mild cognitive disorder as the outcome. Results showed that 409 proteins were associated with mild cognitive disorder with nominal significance ( P < 0.05, Supplementary Table 8). Among the 409 proteins, the significance of 25 protein-AD associations passed the FDR correction ( FDR < 0.05). Two proteins (PILRB and GRN) with genetic-based evidence from PWAS and SMR/COLOC were also identified to be associated with mild cognitive disorder (Table 2 ). Taking PILRB as an example, a higher protein abundance of PILRB was found as a potential risk factor for mild cognitive disorder (HR = 1.178, P = 2.80E-02) which was concordant with the observations that (1) a higher genetically determined abundance of PILRB was found to be associated with an increased risk of AD (PWAS: β = 0.081, P < 1E-320), and (2) a positive association between PILRB and risk of AD by the cohort study (HR = 1.123, P = 1.49E-02). In parallel, we associated the proteins with brain imaging features including the volume of the whole hippocampus and the total volume of white matter hyperintensities. Linear regression was performed on log-transformed brain imaging data. We found 233 proteins associated with the total hippocampal volume ( P < 0.05, Supplementary Table 9, 10), of which two proteins (TREM2 and HDGF) were also probed by PWAS and SMR/COLOC for AD risk (Table 2 ). The levels of both TREM2 and HDGF were negatively associated with the hippocampus volume (TREM2: β = -0.0025, P = 4.43E-02; HDGF: β = -0.0025, P = 2.33E-02). In addition, 652 proteins were associated with the total volume of white matter hyperintensities ( P < 0.05). Among these, TREM2 and GRN were positively associated with total volume of white matter hyperintensities (TREM2: β = 0.0483, P = 8.31E-04; GRN: β = 0.0269, P = 4.54E-02) and have evidence by SMR/COLOC to be potentially causal proteins for AD (Table 2 ). AD-associated proteins substantiated by multiple lines of evidence By taking PWAS as the primary analysis, we integrated evidence from multiple complementary tests including SMR/COLOC, the association study for AD, mild cognitive disorder, and brain imaging features. We highlighted five proteins underpinned by multiple lines of evidence (Fig. 5 ). We underscored PILRB which showed consistence evidence that a higher genetically determined abundance of PILRB was associated with an increased risk of developing AD. SMR/COLOC analysis further prioritized the association by incorporating pQTL data. In addition, the positive association was further confirmed by the cohort studies for both AD and mild cognitive disorder which took the measured protein level as exposure at baseline. Both genetically and non-genetically informed association test suggested FES as a risk factor for AD. The PWAS showed a positive association between genetically predicted abundance of FES and AD. The SMR/COLOC analysis excluded potential false positive findings due to inconsistent causal variant between pQTL and GWAS for AD. The cohort study for AD supported the role of FES using the UK Biobank subjects which is largely independent of the samples used in the PWAS association test. Genetic-based PWAS revealed that a higher plasma abundance of HDGF were found to be associated with a higher risk of AD. The results from non-genetically informed cross-sectional study suggested that a higher plasma levels of HDGF were associated with a lower whole hippocampus volume, suggesting that a negative role of HDGF on memory functions. In addition, a meta-analysis conducted by Bai et al on seven AD-associated proteomic datasets from brain tissues and six AD-associated proteomic datasets from cerebrospinal fluid (CSF) revealed that the level of HDGF was higher in both brain tissues and CSF of AD patients compared to healthy controls[ 48 ]. These consistent results further supported the potentially essential role of HDGF on AD. Protein-protein interaction To connect the newly identified AD-associated proteins with well-established AD-associated proteins and to embed the existing knowledge graph, we performed an analysis through protein-protein interactions (PPI). We performed a PPI analysis by implementing STRING[ 28 ] to embed the 16 newly identified proteins by PWAS with 20 well-studied AD-related proteins (namely, ABCA7, ABI3, ADAM10, APBB3, APOE, APP, BIN1, CASP7, CR1, PILRA, TREM2, EPHA1, MS4A6A, PICALM, PLCG2, PSEN1, PSEN2, RIN3, SORL1, and SPI1)[ 21 – 27 ]. We found comprehensive interactions between TREM2, GRN, FES, and PILRB and these 20 AD pathologic proteins (Supplementary Table 11). TREM2, which interacted with 16 proteins (all are AD pathologic proteins reported in the literature) has the most interactions (Fig. 6 and Supplementary Table 11). PILRB was found to interact with ABCA7, a well-known AD-related protein involved in maintaining homeostasis of the immune system. ABCA7 dysfunction may influence the effect of microglia and increase amyloid deposition, which in turn leads to the development of AD. In brief, the PPI analysis bridged several newly identified proteins with AD-related proteins and suggested potential path through which these novel candidates may contribute to AD pathology. Discussion To identify plasma proteins that contribute to the pathogenesis of AD, we performed genetic-informed proteome-wide association study for AD and cohort studies for AD and mild cognitive disorder. PWAS confirmed 14 protein-AD associations (including TREM2 and GRN) and identified 16 novel proteins for AD (including PILRB, FES, and HDGF). The nongenetic-based studies using measured protein abundance further supported the roles of PILRB, FES and HDGF. Concordant evidence linking PILRB with AD and MCD was observed. Paired immunoglobulin-like type 2 receptor beta (PILRB) is an activated receptor interacted with DAP12 and involved in regulating immune system signal transduction and regulating the activation, proliferation, and function of immune cells[ 46 ] , [ 49 ]. Previous studies have found that the SNP of PILRB ( rs1476679 ) was associated with the susceptibility of AD and the risk allele ( rs1476679 T ) was associated with increased PILRB expression[ 50 ]. Compared with healthy controls and non-AD dementia, the level of PILRB in CSF was higher in AD patients [ 46 ]. We also found that plasma PILRB level was positively associated with the risk of mild cognitive disorder which is generally considered to be symptom manifest in prodromal phases of AD[ 51 ]. In addition, Paired Immunoglobulin-like Type 2 Receptor Alpha (PILRA) and PILRB belong to the PILR family and are involved in the regulation of the immune system[ 49 ]. Several studies showed that PILRA is AD risk gene[ 52 , 53 ]. Notably, PILRA knockdown microglia can increase the uptake of APOE, improve mitochondrial function, and increase lysosomal degradation. These findings suggest that PILRA inhibition could serve as a potential therapeutic approach[ 54 ]. Although the connection between plasma PILRB and the pathology of AD remains unclear, its ubiquitous expression across 54 tissues, including brain tissues, and its interactions with PILRA and ABCA7 suggested that PILRB may contribute to the development of AD by influencing microglial function. Feline sarcoma oncogene (FES) is a non-receptor tyrosine kinase that plays a critical role in signal transduction pathways and is involved in various biological processes, including cell proliferation, differentiation, migration, and apoptosis[ 55 ]. The c-Fes tyrosine kinase cooperates with breakpoint cluster region protein (Bcr) to induce neurite outgrowth in a Rac and Cdc42-dependent manner, which may be the mechanism by which it promotes disease progression in brain disorders[ 56 ]. Our study found that FES interacts with BIN1, a protein known to influence tau pathology and regulate the inflammatory response of microglia[ 57 , 58 ]. Interestingly, clues also have been found for the role of FES on AD in the context of protein kinase activity[ 59 ]. Notably, tyrosine kinase inhibitor Fostamatinib which targeted to FES has been approved by FDA for the treatment of Rheumatoid Arthritis and Immune Thrombocytopenic Purpura (ITP)[ 60 ], providing a candidate for drug repositioning. Although, we still know little about its involvement in AD pathogenesis, our study indicates that FES may be related to tau pathology and could potentially sever as a drug target. Further researches are needed to explain the role of FES in the occurrence and progress of AD. Both PWAS for AD and cross-sectional study for whole hippocampus volume consistently showed that HDGF may play an important role on the risk of AD. Heparin Binding Growth Factor (HDGF) is usually located in the nucleus and acts as a neurotrophic factor to prevent neuronal cell death and provide neuroprotection[ 61 ]. However, a meta-analysis conducted by Bai et al. showed that the level of HDGF is higher in both brain tissues and CSF of AD patients compared to healthy controls[ 48 ]. Consistently, our study showed that a higher genetically predicted plasma level of HDGF was found to be associated with a higher risk of AD and a lower volume of hippocampus. Notably, the widespread expression of HDGF in nearly all tissues suggested its involvement in regulatory mechanisms that affect a broad range of tissues, including brain tissues, highlighting its roles in physiological and pathophysiological processes. Together, these results indicate the complex roles of HDGF, underscoring the need for further research to elucidate its role in AD. For TREM2, our study provides additional insights based on the population level associations. Triggering receptor expressed on myeloid cells 2 (TREM2) is an innate immune receptor in microglia of the nervous system that recognizes and binds phospholipids, apoptotic cells, and lipoproteins[ 62 ] and mediates the uptake and degradation of amyloid-beta protein by microglia[ 63 ]. Many results from mouse models of AD suggest that TREM2 deficiency will increase Aβ accumulation[ 64 , 65 ], neuronal loss[ 65 ], and accelerate tau hyperphosphorylation and aggregation[ 66 ]. Conversely TREM2-dependent microglial activation can delay the onset and/or progression of AD[ 67 ]. Results from a longitudinal cohort study suggest that lower CSF sTREM2 levels may accelerate AD progression due to decrease Aβ microglial clearance in the brain[ 68 ]. In addition, TREM2 is an established therapeutic target for AD. A phase 2 clinical trial of AL002, a monoclonal antibody that enhances microglial removal ability and regulates the inflammatory response by targeting TREM2, has been initiated[ 69 ]. However, Sayed et al. observed that TREM2 defects prevented hippocampal atrophy in 9-month-old PS19 mice, while Trem2 mice showed worsening tau pathology[ 70 ]. The role of TREM2 in AD is still controversial, but our genetically informed study supports that a higher peripheral plasma TREM2 level tends to be a protective factor for AD. Our findings suggest a potential protective role for the GRN in AD at population level, offering evidence that partially clarifies the previously contradictory perspectives on GRN's function. Granulin precursor (GRN/PGRN) is a secretory pleiotropic growth factor expressed in neurons or glial cells in the nervous system, which is involved in inflammation, wound healing, and cell proliferation and also acts as a key regulator of lysosomal function[ 71 ]. The results from animal experiments show that GRN could reduce Aβ plaque load and protect against Aβ toxicity[ 72 ]. Sufficient GRN in microglia enhances the phagocytosis of Aβ deposition and overexpression of GRN can increase the survival rate of neurons[ 73 ], while insufficient GRN can lead to lysosomal dysfunction that may lead to neurodegenerative diseases[ 74 ]. Besides, both Mendelian randomization and cross-sectional studies showed that GRN levels in cerebrospinal fluid[ 75 ] and plasma[ 76 ] were negatively associated with the risk of AD. However, the results from Bai et al indicated that the level of GRN in both brain tissues and CSF is higher in AD patients compared to healthy controls[ 48 ], which seemed to contradict with the previous knowledge of a protective role of GRN[ 73 – 76 ]. However, genetic-based association analysis including PWAS, SMR, and COLOC in our study concordantly suggested a negative association between GRN and AD which is consistent with previous studies[ 73 – 76 ]. These results further indicated that associations observed in pure cross-sectional or case-control studies may result in false positive identifications, underscoring the essential role of genetically informed association studies. Our study has several strengths. First, this study performed a well-powered PWAS of AD by leveraging the largest reference human plasma proteomes from UKB-PPP and summary statistics from the latest GWAS of AD. Second, we have taken the strengths of both genetic-informed study (PWAS and SMR/COLOC) and classical non-genetic-based cohort design while compensating for their respective potential shortcomings. Finally, this study also includes the early symptoms and characteristics of AD for analysis to reveal putative molecules for AD at preclinical stage. There are some limitations to our study. First, our proteins come from eight specific panels, including 2,903 proteins that are not the whole plasma proteome. Second, elucidations of GWAS-identified sites at the translation level may not be sufficient to describe the pathogenesis of AD. Finally, functional genomics and experimental validation are necessary to uncover the molecular mechanisms. Conclusions We identified 16 plasma proteins that may contribute to the risk of AD. We underscored the roles of PILRB, FES, and HDGF which showed multiple lines of evidence including mild cognitive disorder and brain imaging features. Our study provided additional insights into the development of AD from at population level. These proteins might be potential targets for AD treatment after foundational validations. Abbreviations AD: Alzheimer's disease UKB-PPP: the UK Biobank Pharma Proteomics Project GWAS: Genome-wide association study PWAS: Proteome-wide association study MCD: Mild cognitive disorder CRP: C-reactive protein CVDs: Cardiovascular diseases LD: Linkage disequilibrium SMR: Summary data-based Mendelian randomization HEIDI: Heterogeneity in dependent instruments COLOC: Bayesian colocalization analysis WH: Whole hippocampus WMH: White matter hyperintensities FDR: False discovery rate pQTL: Protein quantitative trait loci UKB: the UK Biobank PPI: Protein-protein interactions CSF: Cerebrospinal fluid MRI: Magnetic resonance imaging PPH4: Posterior probability hypothesis 4 ITP: Immune Thrombocytopenic Purpura PILRB: Paired immunoglobulin-like type 2 receptor beta FES: Feline sarcoma oncogene HDGF: Heparin Binding Growth Factor TREM2: Triggering receptor expressed on myeloid cells 2 GRN: Granulin precursor TPM: Transcript per million Declarations Ethics approval and consent to participate This study was conducted using UK Biobank resources under Application No.102158. All the participants in UKB provided written informed consent prior to data collection. Consent for publication Not applicable. Availability of data and materials The UKB-PPP Olink proteomics data used in this manuscript are available under dataset ( https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=1838 ). Additional information about registration for access to the data is available at ( http://www.ukbiobank.ac.uk/register-apply/ ). Baseline information, follow-up outcomes, brain imaging information, and genomic information of study subjects were obtained from the UK biobank ( https://www.ukbiobank.ac.uk/ ). The AD GWAS summary data used in this manuscript are available via the European Bioinformatics Institute GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ) under accession no. GCST90027158. Competing interests No competing interests. Funding This research is supported by the National Natural Sciences Foundation of China 82204118 (D.Z.) and 82370612 (Z.S.), the Fundamental Research Funds from the Central Universities of Zhejiang University (D.Z.), and Shandong Provincial Laboratory Project SYS202202 (Z.S.). Authors contributions D.Z. conceptualized and designed the study. L.S. conducted analyses and Y.Z. check the codes. L.S., G.W. and D.Z interpreted the data. L.S. and G.W. drafted of the manuscript. All authors critically reviewed and revised the manuscript. Acknowledgements We thank the research participants and employees of the UKB, UKB-PPP and the AD GWAS summary data for their time and participation to make the work possible. We also are grateful to all members who participated in the study, as well as all individuals who helped us successfully complete the research. Author’s information Zeyu Sun and Dan Zhou are co-senior authors. Authors and Affiliations School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, 388 Yuhangtang Road, Hangzhou, 310058, China Lingyun Sun, Guikang Wei, Yihong Ding, Jiayao Fan, Yuan Zhou, Zuyun Liu & Dan Zhou Department of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA Yuan Zhou Jinan Microecological Biomedicine Shandong Laboratory, Jinan, 250117, PR China. Zeyu Sun State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310003, P. R. China Zeyu Sun The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China Dan Zhou References Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. 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The lysosomal function of progranulin, a guardian against neurodegeneration. Acta Neuropathol. 2018;136:1–17. Hansson O, Kumar A, Janelidze S, Stomrud E, Insel PS, Blennow K, et al. The genetic regulation of protein expression in cerebrospinal fluid. EMBO Molecular Medicine. 2023;15:e16359. Hsiung G-YR, Fok A, Feldman HH, Rademakers R, Mackenzie IRA. rs5848 polymorphism and serum progranulin level. Journal of the Neurological Sciences. 2011;300:28–32. Additional Declarations No competing interests reported. Supplementary Files supplementtable0627.xlsx 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. 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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-4648743","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323499928,"identity":"a5766fbb-2ad6-4ff0-baeb-bbc6ab4d3469","order_by":0,"name":"Lingyun Sun","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Sun","suffix":""},{"id":323499929,"identity":"f09722c0-a2ae-44eb-b7f3-eadd325d64e4","order_by":1,"name":"Guikang Wei","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guikang","middleName":"","lastName":"Wei","suffix":""},{"id":323499930,"identity":"c9e88dfb-d283-47a4-926f-abaf7964f002","order_by":2,"name":"Feiyang Ji","email":"","orcid":"","institution":"Zhejiang University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Feiyang","middleName":"","lastName":"Ji","suffix":""},{"id":323499931,"identity":"330f20a4-f11b-4fcc-8bb1-aedc02404456","order_by":3,"name":"Yihong Ding","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yihong","middleName":"","lastName":"Ding","suffix":""},{"id":323499932,"identity":"c9d09867-a84c-4100-baf3-54208b3d6293","order_by":4,"name":"Jiayao Fan","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiayao","middleName":"","lastName":"Fan","suffix":""},{"id":323499933,"identity":"cf04bae5-61a1-4a7c-9e51-4e9bcb0d168e","order_by":5,"name":"Yue Xu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Xu","suffix":""},{"id":323499934,"identity":"73baa620-ef41-4eee-8b53-58a4d63cb1a9","order_by":6,"name":"Chunfeng He","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chunfeng","middleName":"","lastName":"He","suffix":""},{"id":323499935,"identity":"cd548199-867e-437d-95fe-6f48998caaee","order_by":7,"name":"Yuan Zhou","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhou","suffix":""},{"id":323499936,"identity":"1087df3a-e9a9-489c-9767-ca69d360a897","order_by":8,"name":"Zuyun Liu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zuyun","middleName":"","lastName":"Liu","suffix":""},{"id":323499937,"identity":"0227d6cb-9dbd-4f69-9e7e-df408f406293","order_by":9,"name":"Zeyu Sun","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Zeyu","middleName":"","lastName":"Sun","suffix":""},{"id":323499939,"identity":"15bea894-cd2e-4973-a6b0-9a45d43cdee4","order_by":10,"name":"Dan Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCCAwwMzEBKDsxiYCNBizHpWhIbwDxitPAdP3v4c0HF4fT5jWcMGD6UHWbgn92AX4vkmbwE4xlnDuc2NpwxYJxx7jCDxJ0D+LUYHMgxSOZtO5zbzHDGgBnIYDCQSCCg5fwbg8NAlelsIC1/idJyI8ewGaglgQekhZEYLZI33hgz85xJN5zBcKzgYM+5dB6JGwS08J3PMf7MU2EtLz/j8MYHP8qs5fhnENCCABIHwJHJQ6x6IOBvIEHxKBgFo2AUjCgAACoRRV+UU801AAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Dan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-06-27 13:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4648743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4648743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60851709,"identity":"d7700dab-321f-4d3b-9523-96f480359fd5","added_by":"auto","created_at":"2024-07-22 21:04:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":429391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the study.\u003c/strong\u003eFirst, we performed genetic-informed studies to identify AD-associated proteins (blue). We performed PWAS using AD GWAS data from European populations and UKB-PPP plasma proteomic data from European populations. Then, we used SMR and Bayesian colocalization analysis to probe AD-associated proteins identified by PWAS. Second, we performed cohort studies for AD and MCD using Cox regression analysis (yellow). In addition, sensitivity analysis was performed for AD. Third, we estimated the association between brain imaging features (including the volume of whole-hippocampus [WH] and white matter hyperintensities [WMH]) and abundance of plasma proteins (green). Finally, we highlighted several proteins with multiple lines of evidence and performed a protein-protein interaction analysis to provide more potential regulatory context between newly identified proteins and proteins that have been well studied for AD.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/7d569184e6635541c02ccb42.png"},{"id":60850901,"identity":"4cbf0577-8871-4c2e-a3c3-74f68bbbc27b","added_by":"auto","created_at":"2024-07-22 20:56:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100347,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan plot for AD PWAS. \u003c/strong\u003eManhattan plot for AD PWAS by integrating the AD GWAS (111,326 AD cases, 677,663 controls) with a plasma protein reference panel from the UKB-PPP. Each circle represents a protein. The statistical significance for the genetically determined protein abundance and AD risk is presented as −log10(\u003cem\u003eP\u003c/em\u003e) on the y-axis. The dash line denotes the significant threshold at\u003cem\u003e FDR\u003c/em\u003e \u0026lt; 0.05 (nominal \u003cem\u003eP\u003c/em\u003e= 1.13 E-03). PWAS identified 30 proteins whose cis-regulated plasma protein abundance were associated with AD at FDR \u0026lt; 0.05. The names of the 30 proteins are annotated in the Manhattan plot.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/9f0f3c1b6b050795dcd4ca33.png"},{"id":60851708,"identity":"2a1929e6-0d30-4c4a-bdd8-9cbac8e288ee","added_by":"auto","created_at":"2024-07-22 21:04:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":185588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression heatmap.\u003c/strong\u003e The heatmap shows the average gene expression levels of 30 genes (y axis) corresponding to the 30 AD-assocaited proteins identified by PWAS across 54 tissues (x axis, GTEx v8). The log-transformed TPM values were used to generate the heatmap. Color represents the relative abundance of the gene expression. Blue and red represent low and high expression, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/8e534477c6c499d2ab92f75c.png"},{"id":60850899,"identity":"e7f74f58-f188-4049-b086-f1d0f3299d6d","added_by":"auto","created_at":"2024-07-22 20:56:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA cohort study identified AD-associated proteins.\u003c/strong\u003e The volcano plot from the cohort study displays the effect size (hazard ratio, HR) and significance (-log(\u003cem\u003eP\u003c/em\u003e)) in panel A. Each circle denotes a protein. Proteins with an HR greater than 1 and a P value less than 0.05 are colored orange, while those with an HR less than 1 and a P value less than 0.05 are colored blue. Proteins with non-significant \u003cem\u003eP\u003c/em\u003e values (\u003cem\u003eP\u003c/em\u003e ≥ 0.05) are colored gray. Panel B shows the results from sensitivity analysis (Methods).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/6bccec0038943e450134253d.png"},{"id":60850904,"identity":"5d373e11-6c12-4421-b328-6e849fbe8935","added_by":"auto","created_at":"2024-07-22 20:56:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":530902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn plot of the results from multiple lines of cognitive disorders and related phenotypes. \u003c/strong\u003eThe Venn plot shows the result from (1) “AD PWAS”: a genetically informed PWAS for AD; (2) “AD Cox” a non-genetic observational cohort study for AD using Cox regression model; (3) “MCD” an observational cohort study for MCD; (4) “WH”: a cross-sectional study for the association between protein abundance and the volume of whole-hippocampus; (5) “WMH”: a cross-sectional study for the association between protein abundance and the total volume of white matter hyperintensities. Five proteins with multiple lines of evidence are highlighted. White boxes show evidence from the current study and light grey boxes display results from Bai et al. 2021. In white boxes, up and down arrows denote positive and negative associations between protein abundance and outcomes. In light grey boxes, up and down arrows denote higher and lower levels in AD patients compared to healthy controls.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/83396fb558061a6f366e876a.png"},{"id":60850905,"identity":"254dc7a4-196c-4a25-aec9-dbe9e776fd6d","added_by":"auto","created_at":"2024-07-22 20:56:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1166259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction of AD related proteins.\u003c/strong\u003e We set up the PPI with 20 pathological proteins (purple circle) of AD reported in the literatures and further extended the interaction network by including the 16 AD-associated proteins (blue and grey circle) newly identified by PWAS. Each circle represents a protein. Darker blue indicates a larger number of interactions between the protein and the remaining proteins.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/7758da99ea9b7205c66a6adc.png"},{"id":60862883,"identity":"a320e133-d22b-4c85-a68e-d74a4faa3311","added_by":"auto","created_at":"2024-07-23 02:22:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3124973,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/95b36696-bb74-4f83-9a80-3b9a9f57f6b8.pdf"},{"id":60850903,"identity":"d57f78d2-609f-44a6-ba2f-b1588dabe19c","added_by":"auto","created_at":"2024-07-22 20:56:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2597009,"visible":true,"origin":"","legend":"","description":"","filename":"supplementtable0627.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4648743/v1/7911cc510f6b524fb8bd7bf4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Proteome-wide association study identifies novel Alzheimer's disease- associated proteins","fulltext":[{"header":"Background","content":"\u003cp\u003eAlzheimer's disease is a neurodegenerative disorder characterized by cognitive decline and is the leading cause of dementia among the elderly population. It is estimated that there are now more than 50\u0026nbsp;million people with dementia worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and the prevalence of dementia is expected to double in Europe and triple globally by 2050[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AD is undoubtedly a public health problem in the world, which not only causes serious physical and psychological burdens on the patients but also imposes substantial challenges and burdens on families and society. Compared with the heavy burden of AD, the available drugs for AD treatment are extremely scarce. Over the past two decades, the FDA has fully approved only a few drugs for the early treatment of AD[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, there is an urgent need to identify potential causal proteins for AD which can be further investigated as candidate therapeutic targets for altering the clinical trajectory of AD.\u003c/p\u003e \u003cp\u003ePrevious studies have found some biomarkers of AD through cohort studies or case-control studies[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although, biomarkers identified by prospective cohort studies could predict the risk of diseases, they may not have a causal role on diseases[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A genetic-informed association study such as TWAS and PWAS studies can avoid reverse causation and are less susceptible to confounding factors. TWAS and PWAS can be regarded as \"natural gene or protein knockdown experiments at the population level\" where the risk of long-term events can be compared between groups with genetically regulated low expression and high expression of a particular molecule. Many studies have analyzed the association of AD from the aspects of transcriptome and proteome, and found some potential AD-associated molecules[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, purely genetic-informed approaches may result in false positive findings due to widespread pleiotropic effects of genetic variants in linkage disequilibrium (LD) with the variants in prediction models[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, genetic-informed association studies may complement cohort studies by leveraging their respective strengths.\u003c/p\u003e \u003cp\u003eHere, we sought to find potentially causal proteins in plasma for AD by combining the study designs of a genetically informed PWAS and a cohort study. Additionally, we considered early-stage mild cognitive disorder and brain imaging features that may occur in early stage of Alzheimer's disease as secondary outcomes to support the identification of AD-related proteins.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe UK Biobank\u003c/h2\u003e \u003cp\u003eThe UK Biobank comprises data from a population-based cohort study that recruited more than 500,000 participants aged from 40–69 who attended 1 of the 22 assessment centers across England, Scotland, and Wales for baseline assessment between 2006 and 2010[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The UKB study received ethical approval from the National Health and Social Care Information Management Board and the NHS North West Multi-Centre Research Ethics Board. Participants were given written informed consent to collect questionnaires and biological data. Clinical outcomes, including AD and mild cognitive disorder diagnosis, were accessed through the hospital admission records, death certificates, primary care records, and self-reports during the follow-up period from 2006 to 2023. Data acquisition and analyses in this study were conducted under UKB Application No.102158. In order to reduce the impact of population structure on genetic data analysis[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], only ancestrally European populations were included in this study.\u003c/p\u003e \u003cp\u003eThe UK Biobank Pharma Proteomics Project (UKB-PPP)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] characterized the plasma proteomics of 54,219 UKB participants at baseline. UKB-PPP used the antibody-based Olink Explore 3072 PEA, measuring 2,941 protein analytes and capturing 2,923 unique proteins in plasma samples collected from UK Biobank participants. The proteins were measured across eight protein panels: cardiometabolic, cardiometabolic II, inflammation, inflammation II, neurology, neurology II, oncology and oncology II. Details of the protein data processing and quality control are provided in the UKB protocol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/ukb/ukb/docs/Olink_1536_B0_to_B7_Normalization.pdf\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/ukb/ukb/docs/Olink_1536_B0_to_B7_Normalization.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We imputed the missing values using random forests method provided by the “missForest” R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/missForest/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/missForest/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After excluding non-European populations and proteins with a deletion rate greater than 50%[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we finally analyzed 2,817 proteins from 45,540 participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGWAS data source for AD\u003c/h2\u003e \u003cp\u003eWe adopted the latest AD summary statistics from a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Stage I samples (39,106 clinically diagnosed AD cases, 46,828 proxy-AD cases and 401,577 controls) came from EADB, GR@ACE, EADI, GERAD/PERADES, DemGene, Bonn, the Rotterdam study, the CCHS study, the NxC, and the UKB. Stage II samples (25,392 AD cases and 276,086 controls) are from the ADGC, CHARGE, and FinnGen consortia. They performed standard quality control on variants and samples from all datasets individually. Ethical approval and written informed consent to participate had been obtained in all studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAscertainment of AD and mild cognitive disorder\u003c/h2\u003e \u003cp\u003eCases of AD and mild cognitive disorder were identified through hospital admissions and death registry records of the participants. Inpatient admissions records were sourced from the Hospital Episode Statistics for England, the Scottish Morbidity Record for Scotland, and the Patient Episode Database for Wales. Death registry records were obtained from the NHS England for England and Wales, and from the Information and Statistics Division for Scotland. Both primary and secondary hospital diagnoses, as well as causes of death, were recorded using the International Classification of Diseases (ICD-10) coding system. The ICD codes were used to identify cases of AD and mild cognitive disorder (Supplementary Table\u0026nbsp;12). These cases were selected and validated by the UK Biobank outcome adjudication group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBrain magnetic resonance imaging\u003c/h2\u003e \u003cp\u003eBrain imaging data from magnetic resonance imaging (MRI) was acquired by standardized image acquisition protocols and analysis pipeline. Detailed information of the procedure can be found in the UKB protocol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/showcase/showcase/docs/brain_mri.pdf\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/brain_mri.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Participants underwent MRI scan from 2014 to 2023 at three imaging analysis centers that utilize similar scanners (3T Siemens Skyra with a 32-channel head coil). T1 structural is acquired using straight sagittal orientation (i.e., with the field-of-view aligned to the scanner axes). FSL and FreeSurfer were used to standard Siemens on-scanner conversion of complex multicoil data was carried out for the T1 data. Quality-controlled T1-weighted magnetic resonance imaging data were used for the analysis of the associations between proteins and hippocampus volume as well as the volume of white matter hyperintensities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eProteome-wide association study for AD\u003c/h2\u003e \u003cp\u003eTo identify proteins whose genetically regulated expression is associated with AD, we performed PWAS analyses by integrating GWAS summary statistics of AD from Céline et al[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. and plasma protein data from UK Biobank Pharma Proteomics Project using the PrediXcan framework[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PrediXcan is a gene-based association test that prioritizes genes that are likely to be causal for the phenotype. The whole working process of PrediXcan can be subdivided into three steps. Firstly, the model is trained by an elastic net regression model using the genetic variants and plasma protein expression data from a reference panel, which refers to 45,540 participants from UKB-PPP in our study. Second, the prediction model can be applied to a large GWAS data to predict gene-regulated protein expression levels. Finally, the associations between genetically regulated expression and trait of interests can be estimated. Here, we implemented the summary statistics-based PrediXcan framework to probe AD-associated plasma proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSMR/HEIDI and Bayesian colocalization analysis\u003c/h2\u003e \u003cp\u003eSMR[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was used to estimate the signal sharing between protein quantitative trait loci (pQTL) and GWAS for AD while taking into account the local linkage disequilibrium. We estimated SNP-protein abundance association and identified protein quantitative trait loci (pQTL) from the UKB-PPP dataset by linear regression. Next, we used the pQTL results and the AD GWAS summary statistics to perform SMR and HEIDI test. Since we considered the SMR test as complementary analysis for PWAS, we used nominally significant genes at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. \u003cem\u003eP\u003c/em\u003e ≥ 0.05 from HEIDI indicated “linkage” as suggested by the method paper[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo determine whether the same genetic variant is responsible for associations observed in GWAS for AD and in pQTL studies, Bayesian colocalization analysis was conducted by implementing the “coloc” R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cran.r-project.org/web/packages/coloc\u003c/span\u003e\u003cspan address=\"http://cran.r-project.org/web/packages/coloc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). COLOC provides the probability of five hypotheses: H0 refers to no pQTL and no GWAS association, H1 and H2 refer to association with pQTL but no GWAS association or association with GWAS but no pQTL association, H3 refers to pQTL and GWAS association but independent signals, and finally H4 refers to shared pQTL and GWAS association. P0, P1, P2, P3, and P4 are the corresponding probabilities for each configuration. We focused on posterior probability hypothesis 4 (PPH4), with strong evidence of colocalization at PPH4 ≥ 0.5[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePerspective cohort study\u003c/h2\u003e \u003cp\u003eTo assess the association between plasma protein levels and the risk of AD as well as the risk of MCD, we conducted cohort studies. The baseline plasma protein levels were considered as quantitative exposure. The demographic characteristics of the participants from the UK Biobank were considered as covariates in regression models. Participants were followed up, and the disease onset were recorded from baseline to the last AD or MCD patient diagnosis in 2023.\u003c/p\u003e \u003cp\u003eFor the cohort study of AD, participants of non-European descent and those with AD at baseline or within the first two years of follow-up were excluded. In total, 45,511 participants and 2,817 proteins were included in the analysis for AD. Cox regression analysis was performed, with follow-up time as the time variable and AD diagnosis status as the outcome variable, adjusting for age and sex. Hazard ratios (HR) and their 95% confidence intervals were estimated. \u003cem\u003eP\u003c/em\u003e-values were adjusted for multiple comparisons using the Benjamini-Hochberg FDR correction. In addition, we also conducted logistic regression to identify AD-associated proteins. To assess the robustness of our findings, we conducted the following sensitivity analysis: first, to investigate the potential impact of severe baseline diseases on the results, we excluded participants who had the following diseases at baseline: cancer, type 2 diabetes, stroke, depression, epilepsy, schizophrenia, and cardiovascular diseases (including hypertension, angina, and heart attack/myocardial infarction). After excluding these participants, we estimated the effect of each protein on the risk of AD using Cox regression by adjusting age, sex, education, and physical activity.\u003c/p\u003e \u003cp\u003eFor the cohort study of mild cognitive disorder, by excluding non-European participants and those with MCD at baseline, 45,532 participants and 2,817 proteins were included. Similarly, we performed Cox regression analysis for mild cognitive disorder by adjusting for age and sex and obtained the HR for each protein and the corresponding \u003cem\u003eP\u003c/em\u003e-value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAssociation study between brain imaging features and plasma proteins\u003c/h2\u003e \u003cp\u003eTo access the association between plasma protein levels and brain imaging features included hippocampus volume and the volume of white matter hyperintensities, we implemented linear regression models. After excluding participants with missing data on hippocampus volume and white matter hyperintensities volume, we conducted analysis using the hippocampus volume of 5,248 participants and the white matter hyperintensities volume of 5,139 participants. Firstly, log transformation was performed on the brain image information of the participants. After the transformation, the volume of hippocampus and the volume of white matter signal enhancement showed approximately normal distribution. We then performed linear regression using the converted data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein Interaction\u003c/h2\u003e \u003cp\u003eTo connect and embed the identified AD-associated proteins with well-established AD-related proteins[\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], we performed an analysis through protein-protein interactions (PPI) by implementing STRING[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. STRING is (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) a database systematically collects and integrates both physical interactions as well as functional associations protein–protein interactions. The data originates from a number of sources: automated text mining of the scientific literature, computational interaction predictions from co-expression, conserved genomic context, databases of interaction experiments and known complexes/pathways from curated sources. All these interactions are critically assessed, scored, and subsequently automatically transferred to less well-studied organisms using hierarchical orthology information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003eThe workflow of the study\u003c/h2\u003e\u003cp\u003eThe overall study workflow was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To identify potentially causal proteins, we systematically linked high-throughput proteomics in the blood to AD. On the one hand, we used plasma protein data obtained from the UKB-PPP and findings from the latest AD GWAS by Céline et al[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] (111,326 AD cases, 677,663 controls) to perform a PWAS analysis using a genetically informed framework. PWAS is designed to detect gene-phenotype associations mediated by changes in protein levels. PWAS integrates protein expression levels with genome-wide association studies to estimate the associations between genetically regulated protein levels and trait of interests. Then, we used the summary data-based Mendelian randomization followed by its accompanying heterogeneity in dependent instruments (SMR and HEIDI)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Bayesian colocalization analysis (COLOC)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] to provide additional support for PWAS-identified proteins. On the other hand, we performed a cohort study to probe AD-associated proteins based on the protein levels at baseline, followed by sensitivity analyses. In addition, we provided evidence between potentially causal proteins and early-stage phenotypes of AD including mild cognitive disorder and brain imaging features. Lastly, we embedded the newly identified proteins with well-studied AD-related proteins by a protein-protein interaction network.\u003c/p\u003e\u003ch2\u003ePWAS of Alzheimer’s disease\u003c/h2\u003e\u003cp\u003eTo identify genetically regulated proteins associated with AD, we performed a PWAS by integrating the AD GWAS results and human plasma proteomes profile. We utilized data from the latest large-scale AD GWAS, which included 111,326 AD cases and 677,663 controls of European descent. The UK Biobank Pharma Proteomics Project, encompassing 45,540 ancestrally European participants, served as the data source for establishing SNP-protein associations. A total of 2,817 proteins were included for analysis after quality control, among which 1,146 proteins were predictable (Pearson’s correlation r \u0026gt; 0.1) and their prediction models were generated by elastic net regression (Supplementary Table\u0026nbsp;1). The PWAS identified 30 proteins whose genetically predicted levels were found to be associated with AD (false discovery rate, \u003cem\u003eFDR\u003c/em\u003e \u0026lt; 0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1). Of the 30 AD risk proteins identified by PWAS, 14 (CR1, PILRA, ACE, EPHX2, CD2AP, TREM2, CD55, GRN, IL34, LPA, SIRPA, ACHE, TREML2, and C1R)[\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e–\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] have been reported to be associated with AD. The remaining 16 proteins (PILRB, FES, CR2, C1S, LRRC37A2, PRSS53, HAVCR2, TNFSF13B, HDGF, GC, LRP11, ITGAL, IDUA, DHRS4L2, SH3BP1, and BPIFB2) were newly discovered. Detailed information can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In summary, we confirmed the associations between 14 proteins and AD, and identified another 16 proteins whose genetically determined abundance in plasma were associated with the risk of AD.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePWAS-identified AD-associated proteins.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ score\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEffect size\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNew identified\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.407\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 1E-320\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePILRB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.529\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 1E-320\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePILRA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.373\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt; 1E-320\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.961\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78E-15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPHX2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.714\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22E-14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREM2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.518\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.444\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.57E-14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD2AP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.153\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.48E-13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.501\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.323\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.77E-08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.29E-08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.071\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.96E-07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.575\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.77E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAVCR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.422\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.238\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.76E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRSS53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.354\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1S\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.351\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRPA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.162\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.16E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREML2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.060\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.90E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF13B\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.141\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.21E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRRC37A2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.64E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACHE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.971\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.101\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.15E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDGF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.774\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDUA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.629\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.85E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1R\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.568\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.60E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRP11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.516\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.39E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHRS4L2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.468\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.24E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGAL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.441\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.235\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.80E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSH3BP1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.404\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.64E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZBTB16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.321\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.98E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFES\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eThis table gives the z-scores and effect size for the AD PWAS associations with their corresponding \u003cem\u003eP\u003c/em\u003e values for proteins passed the FDR correction.\u003c/p\u003e\u003cp\u003eAdditionally, to understand whether these 30 proteins identified by PWAS were specifically distributed in certain tissues, we visualized the gene expression profile across 54 tissues from the GTEx project by a heatmap. The average log-transformed transcript per million (TPM) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;13. The heatmap revealed genes with specific expression patterns including the ones highly expressed in brain tissues as well as other tissues. Notably, \u003cem\u003ePILRB\u003c/em\u003e and \u003cem\u003eHDGF\u003c/em\u003e exhibited ubiquitous expression across nearly all examined tissues. This observation implies that the regulatory mechanisms governing these genes expression are likely systemic, affecting a broad range of tissues, including brain tissues.\u003c/p\u003e\u003ch2\u003eMendelian randomization and colocalization analyses support the role of plasma protein on AD\u003c/h2\u003e\u003cp\u003ePWAS may result in false positive identification due to linkage or pleiotropy effect, to further priority the associations identified in PWAS, we performed SMR and HEIDI[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] analysis and Bayesian colocalization analysis[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] on the basis of different hypothesis for causality inference. We firstly used the genotype data and plasma protein data for pQTL analysis.\u003c/p\u003e\u003cp\u003eThen, we used the pQTL information and the same AD GWAS data for SMR and HEIDI analysis. Among the 30 proteins identified by PWAS, SMR analysis supported most of the findings (25 / 30, SMR \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;2) while the HEIDI test, a post filtering step in SMR using multiple SNPs in a cis-pQTL region to distinguish pleiotropy from linkage, suggested that the 8 of the associations may attribute to linkage (HEIDI \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;2). Token together, the SMR and HEIDI supported the role of 17 proteins (namely, DHRS4L2, ITGAL, C1R, CD2AP, HDGF, IL34, GRN, C1S, TNFSF13B, ZBTB16, TREM2, PILRB, GC, FES, ACHE, LRP11, and SIRPA), including eight novel ones, on the risk of AD.\u003c/p\u003e\u003cp\u003eFurthermore, colocalization analysis was performed by implementing a Bayesian colocalization approach COLOC. COLOC analysis showed a concordant signal distribution between pQTL and GWAS for TREM2, GRN, CIR, DHRS4L2, and SH3BP1.Notablly, four of them (namely, TREM2, GRN, C1R, and DHRS4L2) were also supported by the SMR and HEIDI analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eIn summary, 18 of the 30 proteins were supported by additional genetically informed approaches with more strict assumptions, showing the robustness of the identifications.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCOLOC and SMR analysis of the 30 significant proteins in AD PWAS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOLOC\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSMR\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePPH4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSMR \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHEIDI \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22E-26\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.88E-29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.81E-06\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePILRA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.45E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75E-15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29E-03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePILRB\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82E-10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70E-15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.98E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.25E-09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49E-11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.92E-02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPHX2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00E-23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.15E-03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREM2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00E + 00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05E-09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.89E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD2AP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.96E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.72E-13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.98E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRN\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.92E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.45E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.61E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.48E-08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.06E-03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.83E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.45E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.81E-02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAVCR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.79E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.56E-02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRSS53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.60E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.10E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64E-04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1S\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.02E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.66E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRPA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.57E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREML2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66E-08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59E-08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49E-22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.64E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06E-04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNFSF13B\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.93E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRRC37A2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.95E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.91E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACHE\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.74E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.84E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDGF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.87E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDUA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.44E-06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23E-07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.50E-04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.65E-03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.68E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.89E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1R\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.43E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14E-05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.79E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLRP11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.02E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.07E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDHRS4L2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.59E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.77E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGAL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.37E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSH3BP1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00E-01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.85E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23E-02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZBTB16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43E-04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.78E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFES\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.14E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68E-02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.63E-01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eFor the 30 proteins identified by AD PWAS, the result of Bayesian posterior probability of pQTL-GWAS colocalization is represented by the regional PPH4.The \u003cem\u003eP\u003c/em\u003e values for the SMR and HEIDI tests are presented. NA indicates not enough variants for a HEIDI test.\u003c/p\u003e\u003ch2\u003eProspective cohort study of AD proteomics\u003c/h2\u003e\u003cp\u003eWe performed a cohort study to identify AD-associated plasma proteins. At baseline, participants with no medical records or self-reported AD diagnosis were included in the study. Subjects diagnosed with AD within the first two years of follow-up were excluded. A total number of 45,511 participants with plasma protein measurement by Olink were finally included in the study. After 13.7 years follow up on average, we observed 449 new AD cases. Using a proportional Cox regression model, we tested the association between proteins and AD risk while adjusting for age and sex. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and Supplementary Table\u0026nbsp;4, 204 proteins were found to be associated with AD (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), leading by GFAP (HR = 1.959, 95%CI = 1.763–2.178, \u003cem\u003eP\u003c/em\u003e = 9.86E-36), APOE (HR = 0.533, 95%CI = 0.484–0.587, \u003cem\u003eP\u003c/em\u003e = 1.78E-37), and 23 proteins passed the multiple comparison correction line (\u003cem\u003eFDR\u003c/em\u003e \u0026lt; 0.05). Notably, among the 18 potentially causal proteins of AD identified by PWAS and SMR/COLOC (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), PILRB (HR = 1.123, 95% CI = 1.023–1.232, \u003cem\u003eP\u003c/em\u003e = 1.49E-02) and FES (HR = 1.138, 95% CI = 1.032–1.255, \u003cem\u003eP\u003c/em\u003e = 9.57E-03) were also evidenced by the cohort study with concordant sign of effects (Supplementary Tale 4 and 5). In addition, consistent results were observed using a Logistic regression model without taking into account the time of events.\u003c/p\u003e\u003cp\u003eBy excluding individuals with diseases (including cancer, type 2 diabetes, stroke, depression, epilepsy, schizophrenia, and cardiovascular-related diseases), that may influence plasma protein levels at baseline and adjusting for age, sex, education, and physical activity, we re-conducted proportional Cox regression and Logistic regression models to identify the association between proteins and AD. The results of sensitivity analysis were consistent with the primary findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Supplementary Tables\u0026nbsp;6 and 7). Similarly, GFAP (HR = 2.366, 95%CI = 1.954–2.865, \u003cem\u003eP\u003c/em\u003e = 1.12E-18) and APOE (HR = 0.519, 95%CI = 0.441–0.611, \u003cem\u003eP\u003c/em\u003e = 3.51E-15) showed the highest significant levels in the Cox regression model (Supplementary Table\u0026nbsp;6). The baseline abundance of PILRB in plasma was found to be positively associated with AD risk in both Cox and logistic regression (Cox: HR = 1.286, 95% CI = 1.078–1.535, \u003cem\u003eP\u003c/em\u003e = 5.31E-03; logistic: OR = 1.291, 95%CI = 1.078–1.546, \u003cem\u003eP\u003c/em\u003e = 5.40E-03). Detailed information can be found in Supplementary Tables\u0026nbsp;6 and 7.\u003c/p\u003e\u003ch2\u003eAssociation study between protein and early traits of AD\u003c/h2\u003e\u003cp\u003eIn order to find the proteins associated with the early trait of AD, we first conducted a Cox regression analysis for each protein using mild cognitive disorder as the outcome. Results showed that 409 proteins were associated with mild cognitive disorder with nominal significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Supplementary Table\u0026nbsp;8). Among the 409 proteins, the significance of 25 protein-AD associations passed the FDR correction (\u003cem\u003eFDR\u003c/em\u003e \u0026lt; 0.05). Two proteins (PILRB and GRN) with genetic-based evidence from PWAS and SMR/COLOC were also identified to be associated with mild cognitive disorder (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Taking PILRB as an example, a higher protein abundance of PILRB was found as a potential risk factor for mild cognitive disorder (HR = 1.178, \u003cem\u003eP\u003c/em\u003e = 2.80E-02) which was concordant with the observations that (1) a higher genetically determined abundance of PILRB was found to be associated with an increased risk of AD (PWAS: β = 0.081, \u003cem\u003eP\u003c/em\u003e \u0026lt; 1E-320), and (2) a positive association between PILRB and risk of AD by the cohort study (HR = 1.123, \u003cem\u003eP\u003c/em\u003e = 1.49E-02).\u003c/p\u003e\u003cp\u003eIn parallel, we associated the proteins with brain imaging features including the volume of the whole hippocampus and the total volume of white matter hyperintensities. Linear regression was performed on log-transformed brain imaging data. We found 233 proteins associated with the total hippocampal volume (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Supplementary Table\u0026nbsp;9, 10), of which two proteins (TREM2 and HDGF) were also probed by PWAS and SMR/COLOC for AD risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The levels of both TREM2 and HDGF were negatively associated with the hippocampus volume (TREM2: β = -0.0025, \u003cem\u003eP\u003c/em\u003e = 4.43E-02; HDGF: β = -0.0025, \u003cem\u003eP\u003c/em\u003e = 2.33E-02). In addition, 652 proteins were associated with the total volume of white matter hyperintensities (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Among these, TREM2 and GRN were positively associated with total volume of white matter hyperintensities (TREM2: β = 0.0483, \u003cem\u003eP\u003c/em\u003e = 8.31E-04; GRN: β = 0.0269, \u003cem\u003eP\u003c/em\u003e = 4.54E-02) and have evidence by SMR/COLOC to be potentially causal proteins for AD (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eAD-associated proteins substantiated by multiple lines of evidence\u003c/h2\u003e\u003cp\u003eBy taking PWAS as the primary analysis, we integrated evidence from multiple complementary tests including SMR/COLOC, the association study for AD, mild cognitive disorder, and brain imaging features. We highlighted five proteins underpinned by multiple lines of evidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe underscored PILRB which showed consistence evidence that a higher genetically determined abundance of PILRB was associated with an increased risk of developing AD. SMR/COLOC analysis further prioritized the association by incorporating pQTL data. In addition, the positive association was further confirmed by the cohort studies for both AD and mild cognitive disorder which took the measured protein level as exposure at baseline.\u003c/p\u003e\u003cp\u003eBoth genetically and non-genetically informed association test suggested FES as a risk factor for AD. The PWAS showed a positive association between genetically predicted abundance of FES and AD. The SMR/COLOC analysis excluded potential false positive findings due to inconsistent causal variant between pQTL and GWAS for AD. The cohort study for AD supported the role of FES using the UK Biobank subjects which is largely independent of the samples used in the PWAS association test.\u003c/p\u003e\u003cp\u003eGenetic-based PWAS revealed that a higher plasma abundance of HDGF were found to be associated with a higher risk of AD. The results from non-genetically informed cross-sectional study suggested that a higher plasma levels of HDGF were associated with a lower whole hippocampus volume, suggesting that a negative role of HDGF on memory functions. In addition, a meta-analysis conducted by Bai et al on seven AD-associated proteomic datasets from brain tissues and six AD-associated proteomic datasets from cerebrospinal fluid (CSF) revealed that the level of HDGF was higher in both brain tissues and CSF of AD patients compared to healthy controls[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These consistent results further supported the potentially essential role of HDGF on AD.\u003c/p\u003e\u003ch2\u003eProtein-protein interaction\u003c/h2\u003e\u003cp\u003eTo connect the newly identified AD-associated proteins with well-established AD-associated proteins and to embed the existing knowledge graph, we performed an analysis through protein-protein interactions (PPI). We performed a PPI analysis by implementing STRING[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] to embed the 16 newly identified proteins by PWAS with 20 well-studied AD-related proteins (namely, ABCA7, ABI3, ADAM10, APBB3, APOE, APP, BIN1, CASP7, CR1, PILRA, TREM2, EPHA1, MS4A6A, PICALM, PLCG2, PSEN1, PSEN2, RIN3, SORL1, and SPI1)[\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We found comprehensive interactions between TREM2, GRN, FES, and PILRB and these 20 AD pathologic proteins (Supplementary Table\u0026nbsp;11). TREM2, which interacted with 16 proteins (all are AD pathologic proteins reported in the literature) has the most interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Supplementary Table\u0026nbsp;11). PILRB was found to interact with ABCA7, a well-known AD-related protein involved in maintaining homeostasis of the immune system. ABCA7 dysfunction may influence the effect of microglia and increase amyloid deposition, which in turn leads to the development of AD. In brief, the PPI analysis bridged several newly identified proteins with AD-related proteins and suggested potential path through which these novel candidates may contribute to AD pathology.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo identify plasma proteins that contribute to the pathogenesis of AD, we performed genetic-informed proteome-wide association study for AD and cohort studies for AD and mild cognitive disorder. PWAS confirmed 14 protein-AD associations (including TREM2 and GRN) and identified 16 novel proteins for AD (including PILRB, FES, and HDGF). The nongenetic-based studies using measured protein abundance further supported the roles of PILRB, FES and HDGF.\u003c/p\u003e \u003cp\u003eConcordant evidence linking PILRB with AD and MCD was observed. Paired immunoglobulin-like type 2 receptor beta (PILRB) is an activated receptor interacted with DAP12 and involved in regulating immune system signal transduction and regulating the activation, proliferation, and function of immune cells[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Previous studies have found that the SNP of \u003cem\u003ePILRB\u003c/em\u003e (\u003cem\u003ers1476679\u003c/em\u003e) was associated with the susceptibility of AD and the risk allele (\u003cem\u003ers1476679\u003c/em\u003e\u003csup\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sup\u003e) was associated with increased \u003cem\u003ePILRB\u003c/em\u003e expression[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Compared with healthy controls and non-AD dementia, the level of PILRB in CSF was higher in AD patients [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. We also found that plasma PILRB level was positively associated with the risk of mild cognitive disorder which is generally considered to be symptom manifest in prodromal phases of AD[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In addition, Paired Immunoglobulin-like Type 2 Receptor Alpha (PILRA) and PILRB belong to the PILR family and are involved in the regulation of the immune system[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Several studies showed that \u003cem\u003ePILRA\u003c/em\u003e is AD risk gene[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Notably, PILRA knockdown microglia can increase the uptake of APOE, improve mitochondrial function, and increase lysosomal degradation. These findings suggest that PILRA inhibition could serve as a potential therapeutic approach[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Although the connection between plasma PILRB and the pathology of AD remains unclear, its ubiquitous expression across 54 tissues, including brain tissues, and its interactions with PILRA and ABCA7 suggested that PILRB may contribute to the development of AD by influencing microglial function.\u003c/p\u003e \u003cp\u003eFeline sarcoma oncogene (FES) is a non-receptor tyrosine kinase that plays a critical role in signal transduction pathways and is involved in various biological processes, including cell proliferation, differentiation, migration, and apoptosis[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The c-Fes tyrosine kinase cooperates with breakpoint cluster region protein (Bcr) to induce neurite outgrowth in a Rac and Cdc42-dependent manner, which may be the mechanism by which it promotes disease progression in brain disorders[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Our study found that FES interacts with BIN1, a protein known to influence tau pathology and regulate the inflammatory response of microglia[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Interestingly, clues also have been found for the role of FES on AD in the context of protein kinase activity[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Notably, tyrosine kinase inhibitor Fostamatinib which targeted to \u003cem\u003eFES\u003c/em\u003e has been approved by FDA for the treatment of Rheumatoid Arthritis and Immune Thrombocytopenic Purpura (ITP)[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], providing a candidate for drug repositioning. Although, we still know little about its involvement in AD pathogenesis, our study indicates that FES may be related to tau pathology and could potentially sever as a drug target. Further researches are needed to explain the role of FES in the occurrence and progress of AD.\u003c/p\u003e \u003cp\u003eBoth PWAS for AD and cross-sectional study for whole hippocampus volume consistently showed that HDGF may play an important role on the risk of AD. Heparin Binding Growth Factor (HDGF) is usually located in the nucleus and acts as a neurotrophic factor to prevent neuronal cell death and provide neuroprotection[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. However, a meta-analysis conducted by Bai et al. showed that the level of HDGF is higher in both brain tissues and CSF of AD patients compared to healthy controls[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Consistently, our study showed that a higher genetically predicted plasma level of HDGF was found to be associated with a higher risk of AD and a lower volume of hippocampus. Notably, the widespread expression of HDGF in nearly all tissues suggested its involvement in regulatory mechanisms that affect a broad range of tissues, including brain tissues, highlighting its roles in physiological and pathophysiological processes. Together, these results indicate the complex roles of HDGF, underscoring the need for further research to elucidate its role in AD.\u003c/p\u003e \u003cp\u003eFor TREM2, our study provides additional insights based on the population level associations. Triggering receptor expressed on myeloid cells 2 (TREM2) is an innate immune receptor in microglia of the nervous system that recognizes and binds phospholipids, apoptotic cells, and lipoproteins[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and mediates the uptake and degradation of amyloid-beta protein by microglia[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Many results from mouse models of AD suggest that TREM2 deficiency will increase Aβ accumulation[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], neuronal loss[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and accelerate tau hyperphosphorylation and aggregation[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Conversely TREM2-dependent microglial activation can delay the onset and/or progression of AD[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Results from a longitudinal cohort study suggest that lower CSF sTREM2 levels may accelerate AD progression due to decrease Aβ microglial clearance in the brain[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In addition, TREM2 is an established therapeutic target for AD. A phase 2 clinical trial of AL002, a monoclonal antibody that enhances microglial removal ability and regulates the inflammatory response by targeting TREM2, has been initiated[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. However, Sayed et al. observed that TREM2 defects prevented hippocampal atrophy in 9-month-old PS19 mice, while \u003cem\u003eTrem2\u003c/em\u003e mice showed worsening tau pathology[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The role of TREM2 in AD is still controversial, but our genetically informed study supports that a higher peripheral plasma TREM2 level tends to be a protective factor for AD.\u003c/p\u003e \u003cp\u003eOur findings suggest a potential protective role for the GRN in AD at population level, offering evidence that partially clarifies the previously contradictory perspectives on GRN's function. Granulin precursor (GRN/PGRN) is a secretory pleiotropic growth factor expressed in neurons or glial cells in the nervous system, which is involved in inflammation, wound healing, and cell proliferation and also acts as a key regulator of lysosomal function[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. The results from animal experiments show that GRN could reduce Aβ plaque load and protect against Aβ toxicity[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Sufficient GRN in microglia enhances the phagocytosis of Aβ deposition and overexpression of GRN can increase the survival rate of neurons[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], while insufficient GRN can lead to lysosomal dysfunction that may lead to neurodegenerative diseases[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Besides, both Mendelian randomization and cross-sectional studies showed that GRN levels in cerebrospinal fluid[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] and plasma[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] were negatively associated with the risk of AD. However, the results from Bai et al indicated that the level of GRN in both brain tissues and CSF is higher in AD patients compared to healthy controls[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which seemed to contradict with the previous knowledge of a protective role of GRN[\u003cspan additionalcitationids=\"CR74 CR75\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. However, genetic-based association analysis including PWAS, SMR, and COLOC in our study concordantly suggested a negative association between GRN and AD which is consistent with previous studies[\u003cspan additionalcitationids=\"CR74 CR75\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. These results further indicated that associations observed in pure cross-sectional or case-control studies may result in false positive identifications, underscoring the essential role of genetically informed association studies. Our study has several strengths. First, this study performed a well-powered PWAS of AD by leveraging the largest reference human plasma proteomes from UKB-PPP and summary statistics from the latest GWAS of AD. Second, we have taken the strengths of both genetic-informed study (PWAS and SMR/COLOC) and classical non-genetic-based cohort design while compensating for their respective potential shortcomings. Finally, this study also includes the early symptoms and characteristics of AD for analysis to reveal putative molecules for AD at preclinical stage. There are some limitations to our study. First, our proteins come from eight specific panels, including 2,903 proteins that are not the whole plasma proteome. Second, elucidations of GWAS-identified sites at the translation level may not be sufficient to describe the pathogenesis of AD. Finally, functional genomics and experimental validation are necessary to uncover the molecular mechanisms.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe identified 16 plasma proteins that may contribute to the risk of AD. We underscored the roles of PILRB, FES, and HDGF which showed multiple lines of evidence including mild cognitive disorder and brain imaging features. Our study provided additional insights into the development of AD from at population level. These proteins might be potential targets for AD treatment after foundational validations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD: Alzheimer\u0026apos;s disease\u003c/p\u003e\n\u003cp\u003eUKB-PPP: the UK Biobank Pharma Proteomics Project\u003c/p\u003e\n\u003cp\u003eGWAS: Genome-wide association study\u003c/p\u003e\n\u003cp\u003ePWAS: Proteome-wide association study\u003c/p\u003e\n\u003cp\u003eMCD: Mild cognitive disorder\u003c/p\u003e\n\u003cp\u003eCRP: C-reactive protein\u003c/p\u003e\n\u003cp\u003eCVDs: Cardiovascular diseases\u003c/p\u003e\n\u003cp\u003eLD: Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eSMR: Summary data-based Mendelian randomization\u003c/p\u003e\n\u003cp\u003eHEIDI: Heterogeneity in dependent instruments\u003c/p\u003e\n\u003cp\u003eCOLOC: Bayesian colocalization analysis\u003c/p\u003e\n\u003cp\u003eWH: Whole hippocampus\u003c/p\u003e\n\u003cp\u003eWMH: White matter hyperintensities\u003c/p\u003e\n\u003cp\u003eFDR: False discovery rate\u003c/p\u003e\n\u003cp\u003epQTL: Protein quantitative trait loci\u003c/p\u003e\n\u003cp\u003eUKB: the UK Biobank\u003c/p\u003e\n\u003cp\u003ePPI: Protein-protein interactions\u003c/p\u003e\n\u003cp\u003eCSF: Cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003eMRI: Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003ePPH4: Posterior probability hypothesis 4\u003c/p\u003e\n\u003cp\u003eITP: Immune Thrombocytopenic Purpura\u003c/p\u003e\n\u003cp\u003ePILRB: Paired immunoglobulin-like type 2 receptor beta\u003c/p\u003e\n\u003cp\u003eFES: Feline sarcoma oncogene\u003c/p\u003e\n\u003cp\u003eHDGF: Heparin Binding Growth Factor\u003c/p\u003e\n\u003cp\u003eTREM2: Triggering receptor expressed on myeloid cells 2\u003c/p\u003e\n\u003cp\u003eGRN: Granulin precursor\u003c/p\u003e\n\u003cp\u003eTPM: Transcript per million\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using UK Biobank resources under Application No.102158. All the participants in UKB provided written informed consent prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UKB-PPP Olink proteomics data used in this manuscript are available under dataset ( https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=1838 ). Additional information about registration for access to the data is available at ( http://www.ukbiobank.ac.uk/register-apply/ ). Baseline information, follow-up outcomes, brain imaging information, and genomic information of study subjects were obtained from the UK biobank ( https://www.ukbiobank.ac.uk/ ). The AD GWAS summary data used in this manuscript are available via the European Bioinformatics Institute GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ) under accession no. GCST90027158.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is supported by the National Natural Sciences Foundation of China 82204118 (D.Z.) and 82370612 (Z.S.), the Fundamental Research Funds from the Central Universities of Zhejiang University (D.Z.), and Shandong Provincial Laboratory Project SYS202202 (Z.S.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.Z. conceptualized and designed the study. L.S. conducted analyses and Y.Z. check the codes. L.S., G.W. and D.Z interpreted the data. L.S. and G.W. drafted of the manuscript. All authors critically reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the research participants and employees of the UKB, UKB-PPP and the AD GWAS summary data for their time and participation to make the work possible. We also are grateful to all members who participated in the study, as well as all individuals who helped us successfully complete the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZeyu Sun and Dan Zhou are co-senior authors.\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eSchool of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, 388 Yuhangtang Road, Hangzhou, 310058, China\u003c/p\u003e\n\u003cp\u003eLingyun Sun, Guikang Wei, Yihong Ding, Jiayao Fan, Yuan Zhou, Zuyun Liu \u0026amp; Dan Zhou\u003c/p\u003e\n\u003cp\u003eDepartment of Biostatistics and Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, USA\u003c/p\u003e\n\u003cp\u003eYuan Zhou\u003c/p\u003e\n\u003cp\u003eJinan Microecological Biomedicine Shandong Laboratory, Jinan, 250117, PR China.\u003c/p\u003e\n\u003cp\u003eZeyu Sun\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310003, P. R. China\u003c/p\u003e\n\u003cp\u003eZeyu Sun\u003c/p\u003e\n\u003cp\u003eThe Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China\u003c/p\u003e\n\u003cp\u003eDan Zhou\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. The Lancet Public Health. 2022;7:e105\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eScheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u0026eacute;telat G, Teunissen CE, et al. Alzheimer\u0026rsquo;s disease. The Lancet. 2021;397:1577\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eJaffe S. US FDA defends approval of Alzheimer\u0026rsquo;s disease drug. The Lancet. 2021;398:12. \u003c/li\u003e\n\u003cli\u003eWang Z-B, Wang Z-T, Sun Y, Tan L, Yu J-T. 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Journal of the Neurological Sciences. 2011;300:28\u0026ndash;32. \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":"Alzheimer’s disease, PWAS, plasma protein, mild cognitive disorder, brain imaging","lastPublishedDoi":"10.21203/rs.3.rs-4648743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4648743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlzheimer's disease (AD) is a progressive neurodegenerative disease, with a critical shortage of effective prevention and treatment options. Here, we aimed to identify proteins whose genetically regulated plasma levels were associated with AD and its related phenotypes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn integrative proteome-wide search using Olink-based plasma proteomes (N\u0026thinsp;=\u0026thinsp;45,540) from the UK Biobank Pharma Proteomics Project (UKB-PPP) and a large-scale genome-wide association study (GWAS) for AD (N case\u0026thinsp;=\u0026thinsp;111,326, N control\u0026thinsp;=\u0026thinsp;677,663) was employed to identify AD-associated proteins. Cohort studies for AD or mild cognitive disorder (MCD) with average follow-ups of 13.7 years, alongside cross-sectional studies for the volume of whole hippocampus (WH) and white matter hyperintensities (WMH) were performed to provide additional supports.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe identified 30 AD-associated proteins through a genetic-informed proteome-wide association study (PWAS). Among these, 14 proteins (including TREM2 and GRN) have been previously reported to be associated with AD. No clear evidence has linked the remaining 16 proteins (including PILRB, FES, and HDGF) with AD. PILRB and FES were further supported by cohort studies for AD and/or MCD. A higher plasma abundance of HDGF was found to be associated with a lower volume of whole-hippocampus and an increased risk of AD, consistent with a previous study which showed a potentially risk role of HDGF for AD in both brain tissues and cerebrospinal fluid. The protein-protein interaction analysis linked PILRB with ABCA7, an AD-related protein involved in the immune system.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe integrative genetic-informed proteome-wide scan provides promising AD-associated proteins for further mechanistic studies.\u003c/p\u003e","manuscriptTitle":"Proteome-wide association study identifies novel Alzheimer's disease- associated proteins","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 20:56:21","doi":"10.21203/rs.3.rs-4648743/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":"e9eba695-cf08-4f6c-a804-489f7285ab95","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T08:54:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 20:56:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4648743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4648743","identity":"rs-4648743","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}