{"paper_id":"02f556ea-c7a9-445d-aa13-169ea8d515db","body_text":"Genetic and Phenotypic Architecture of Brain Glymphatic System | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return\"[object Function]\"==o.call(a)}function e(a){return\"string\"==typeof a}function f(){}function g(a){return!a||\"loaded\"==a||\"complete\"==a||\"uninitialized\"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){(\"c\"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){\"img\"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),\"object\"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height=\"0\",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),\"img\"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||\"j\",e(a)?i(\"c\"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName(\"script\")[0],o={}.toString,p=[],q=0,r=\"MozAppearance\"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&\"[object Opera]\"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?\"object\":l?\"script\":\"img\",v=l?\"script\":u,w=Array.isArray||function(a){return\"[object Array]\"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split(\"!\"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split(\"=\"),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(\".\").pop().split(\"?\").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split(\"/\").pop().split(\"?\")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&\"css\"==i.url.split(\".\").pop().split(\"?\").shift()?\"c\":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Genetic and Phenotypic Architecture of Brain Glymphatic System Changhe Shi , Dongrui Ma , Shuangjie Li , Chunyan Zuo , Zhiyun Wang , Yuemeng Sun , Shasha Qi , Yuanyuan Liang , Chenwei Hao , Yanmei Feng , Zhengwei Hu , Xiaoyan Hao , Mengjie Li , Ruwei Yang , Song Tan , Chengyuan Mao , Ying Jing , Yuming Xu , Yunpeng Wang , Shilei Sun , Ole A. Andreassen doi: https://doi.org/10.1101/2025.03.23.25323721 Changhe Shi 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 2 Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou University , Zhengzhou, Henan, China 3 NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 4 Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: shichanghe{at}gmail.com Dongrui Ma 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shuangjie Li 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chunyan Zuo 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhiyun Wang 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuemeng Sun 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shasha Qi 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuanyuan Liang 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chenwei Hao 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yanmei Feng 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhengwei Hu 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaoyan Hao 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mengjie Li 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ruwei Yang 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Song Tan 5 Sichuan Provincial Key Laboratory for Human Disease Gene Study and Rare Disease Medical Centre and Department of Neurology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China , Chengdu, Sichuan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chengyuan Mao 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 3 NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 4 Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ying Jing 6 School of Basic Medical Sciences, Zhengzhou University , Zhengzhou, Henan, 450001, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yuming Xu 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 3 NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 4 Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yunpeng Wang 7 NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo , Oslo, Norway 8 Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shilei Sun 1 Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 3 NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China 4 Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University , Zhengzhou, 450000, Henan, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ole A. Andreassen 7 NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo , Oslo, Norway Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Background The glymphatic system plays a crucial role in clearing metabolic waste from the brain, facilitating waste exchange between cerebrospinal fluid and interstitial fluid, and supporting brain homeostasis. However, quantifying glymphatic function has been challenging. The Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS) method offers a non-invasive approach to assess glymphatic function by calculating an index that reflects fluid mobility within the brain. This study aimed to identify genetic variants associated with the ALPS index and explore its relationships with metabolic, immune, cognitive, and health-related phenotypes. Methods Data from 43,823 participants in the UK Biobank were analyzed. After rigorous quality control, 36,997 individuals with valid bilateral ALPS indices were included. A genome-wide association study (GWAS) was conducted to identify genetic loci linked to the ALPS index. The study also explored correlations between the ALPS index and various non-imaging traits, including cognitive performance, blood pressure, and lifestyle factors. Statistical analyses included GWAS, gene enrichment analysis, polygenic risk score validation, Cox regression, and Mendelian randomization. Results The GWAS identified 14 independent loci, encompassing 3,814 single-nucleotide polymorphisms, associated with white matter integrity, brain volume, fiber tract connectivity, inflammation, and metabolism. Key candidate genes, such as GNA12 , SERPIND1 , and MAPT, were linked to vascular function and neurodegenerative diseases. Enrichment analysis revealed significant roles for neuronal development, signal transduction, and metabolic pathways. The ALPS index showed significant associations with non-imaging phenotypes: higher indices correlated with better physical exercise, cognitive performance, and lower metabolic risks, while negative associations were found with smoking and excessive computer use. Polygenic risk scores confirmed these associations. Further analyses suggested that higher ALPS indices may protect against Alzheimer’s disease and multiple sclerosis. Conclusions This study represents the largest genome-wide analysis of the ALPS index to date, revealing key genetic variants that influence glymphatic function and their potential role in neurological health. The ALPS index may serve as a promising biomarker for neurodegenerative disease risk and offers new avenues for therapeutic interventions aimed at improving glymphatic clearance. Background The glymphatic system is a vital pathway for the clearance of metabolic waste from the brain, operating through a combination of convective bulk flow and diffusion along concentration gradients[ 1 ]. Cerebrospinal fluid (CSF) infiltrates the brain parenchyma via perivascular spaces, facilitating the exchange of waste products with interstitial fluid[ 2 ]. This dynamic mechanism ensures efficient waste clearance but also regulates ion and neurotransmitter balance, supports nutrient transport, and modulates neuronal activity[ 3 ]. Despite the inherent challenges in quantitatively assessing glymphatic system function, the Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS) method provides a non-invasive approach for its evaluation[ 4 ]. DTI-ALPS calculates an index reflecting brain fluid mobility by analyzing the diffusivity of water molecules along the anterior-posterior axis, particularly in projection and association fibers adjacent to the lateral ventricles, thereby serving as an indicator of glymphatic clearance capacity[ 5 ]. Current research highlights that glymphatic system function is intricately linked to various genes involved in regulating vascular permeability, CSF circulation, and the transport of metabolic waste within the brain[ 6 ]. However, large-scale genome-wide association studies (GWAS) specifically addressing the genetic basis of the glymphatic system remain unexplored. In this study, we employed an automated and unified algorithm to calculate the ALPS index in a substantial cohort of over 36,000 participants from the UK Biobank (UKBB). Through GWAS, genetic variants significantly associated with the ALPS index were identified, offering insights into their potential biological mechanisms. Alongside phenotype association analyses, the study further explored the ALPS index’s potential roles in metabolism, immunity, cognition, and other health-related traits. This research elucidates the genetic variants and phenotypes linked to glymphatic system function, underlining its possible implications in disease and providing valuable insights for future therapeutic interventions. Materials and methods Ethics This study was conducted under approved project 221671 by the UK Biobank, following protocols sanctioned by the National Research Ethics Service Committee ( http://www.ukbiobank.ac.uk/ethics/ ). All participants provided written informed consent. MRI Data Acquisition and Participant Information Brain imaging data were collected using a Siemens Skyra 3T scanner with a 32-channel RF head coil at the UK Biobank imaging centers in Cheadle, Manchester, Newcastle, and Reading. The diffusion-weighted imaging (DWI) protocol followed standards set by the UK Biobank Imaging Working Group ( http://www.ukbiobank.ac.uk/expert-working-groups ). Image processing involved registration, eddy correction, and diffusion tensor imaging (DTI) tensor fitting using the Oxford FSL and mrtrix3 pipelines.[ 7 ] Data from 43,823 participants undergoing their first imaging session were analyzed. ALPS Index calculation The ALPS index was calculated using a shared bash script ( https://github.com/gbarisano/alps ).[ 8 ] Diffusion tensor metrics (Dxx, Dyy, Dzz) were extracted from co-registered FA maps and diffusivity maps. Four 5 mm diameter spherical regions of interest (ROIs) were placed in projection and association fibers near the lateral ventricles. The ALPS index was calculated as the ratio of mean diffusivity along the x-axis to the mean diffusivity along the y- and z-axes for both hemispheres, resulting in left, right, and bilateral ALPS indices. Genome-Wide Association A GWAS was performed on the mean ALPS index for both hemispheres using linear regression across 7,604,629 SNPs. Regenie (v3.4.1) [ 9 ] was used with nested ridge regression and LOCO to mitigate confounding. Quality-controlled imputed data from the Wellcome Trust Centre included variants with minor allele frequency (MAF) >1%, missingness <10%, HWE p > 1×10□¹□, and mach-r² >0.8. Covariates included age, sex, genotype array, scanner site, intracranial volume, and the top 10 genetic principal components. Phenotypes were rank-transformed, and genetic inflation and heritability were estimated using Linkage Disequilibrium SCore regression (LDSC).[ 10 ] Post-GWAS, the LD structure, genomic risk loci, candidate single nucleotide variants (SNVs), lead SNVs, and independent significant SNVs for the ALPS index were identified using the SNV2GENE pipeline in FUMA (v1.5.2).[ 11 ] Variants were annotated for pathogenicity and regulatory effects, and cross-referenced with the GWAS Catalog to identify relevant associations. Conditional and Joint Association Analysis Using Conditional and joint multiple-SNP analysis using Genome-wide Complex Trait Analysis (GCTA)-COJO,[ 12 ] conditional and joint analyses were conducted to identify independent signals within significant loci. GWAS summary statistics and LD structure from the UK Biobank reference panel were used, applying a MAF threshold of 1% and a 10 Mb LD window. Independent SNPs were identified with an r² < 0.6, and lead SNPs with r² < 0.1. Candidate Gene Identification and Annotation FUMA (v1.5.2)[ 11 ] was employed for positional mapping, gene-based association using Multi-marker Analysis of GenoMic Annotation (MAGMA),[ 13 ] and variant annotation with ANNOVAR.[ 14 ] Genomic risk loci were defined based on independent signals and proximity. Candidate genes were cross-referenced with the GWAS Catalog and annotated for pathogenicity and regulatory effects. Genes supported by multiple lines of evidence were prioritized. Transcriptome-Wide Association We employed FUSION software ( http://gusevlab.org/projects/fusion/ ) to construct a gene expression prediction model based on GWAS data[ 15 ]. This approach integrated gene expression data with reference panel weights for conducting a transcriptome-wide association study (TWAS). We analyzed expression weights derived from the Genotype-Tissue Expression project version 8 (GTEx v8) across 22 tissues relevant to the glymphatic system, including the central nervous system, blood vessels, blood, and peripheral nerve tissues. Using LD reference data from the 1000 Genomes Project Phase 3, we assessed the association between gene expression and the ALPS Index. To address the issue of multiple testing, Bonferroni correction (P < 0.05 / [number of genes * number of tissues]) was applied to identify significantly associated genes and their expression quantitative trait loci (eQTLs). Colocalization Analysis COLOC (v5.1.0)[ 16 ] was utilized within FUSION to evaluate the co-localization of GWAS SNPs with eQTL/ splicing quantitative trait loci (sQTL) signals in a 1.5 Mb window around significant loci. A posterior probability (PP4) >80% indicated co-localization, suggesting shared causal variants. Functional Annotation of Susceptible Genes We further sought to identify candidate genes influencing ALPS Index phenotypic variation using an integrative approach supported by multiple lines of evidence. Genes annotated by more than three methods were classified as candidate genes for ALPS Index. Gene enrichment analysis was performed using DAVID ( https://david.ncifcrf.gov/ ),[ 17 ] focusing on specific Gene Ontology (GO) categories with a false discovery rate (FDR) < 0.05 after Benjamini-Hochberg correction. Cell-Specific Susceptibility Analysis The scDRS algorithm[ 18 ] linked single-cell RNA sequencing data to polygenic risk for the ALPS index. Gene sets associated with the ALPS index were constructed, and susceptibility scores were calculated and normalized for each cell, with p-values derived from comparison to control scores. Disease Association Analysis Enrichr ( https://maayanlab.cloud/Enrichr/ )[ 19 ] was used to assess associations between candidate genes and known diseases using the GWAS Catalog 2023. Enrichment significance was determined with FDR < 0.05. Enrichment of Drug Target Genes We sourced druggable genes mapped to Entrez IDs from the DGIdb database,[ 20 ] extracting 5,012 potential targets (Supplementary Table 1). Additionally, we utilized Finan et al.,[ 21 ] identifying 4,463 druggable genes (Supplementary Table 2) linked to GWAS loci of complex diseases. To ensure reliability, we further screened 2,587 genes validated by both sources and assigned official names by the Human Genome Nomenclature Committee (HGNC). DrugBank[ 22 ] and ClinicalTrials ( https://www.clinicaltrials.gov ) databases were consulted to evaluate drug development status.[ 22 ] Association Analysis Between ALPS Index and Five Major Categories of Non-Imaging Phenotypes We used the ALPS index, rank-transformed from 36,997 participants, to perform phenotype association analysis with 2,121 UK Biobank risk factors across five categories: Biomarkers, Medical Conditions, Cognitive Health, Lifestyle, and Sex-Specific Factors. Variables were sourced from Additional Exposures, Assessment Centre, Biological Samples, Health-Related Outcomes, and Online Follow-Up. PheWAS was conducted using the PHESANT R package,[ 23 ] which classified variables as continuous, ordered/unordered categorical, or binary. In the “ALPS Index-Association Analysis” step, the rank-transformed ALPS index was the independent variable, with factors as dependent variables, adjusting for age, sex, and assessment center. We reported standardized regression coefficients (β) for continuous outcomes and log odds ratios (OR) for binary outcomes. All analyses were two-sided and corrected for multiple testing using FDR. Associations with pFDR < 0.05 were selected for further validation. Polygenic Risk Score Validation of ALPS Index Associations Across Five Phenotype Categories In the validation process, we calculated the polygenic risk score (PRS) for the ALPS index in 370,920 UKBB participants who did not have a directly measured ALPS index. After excluding SNPs with a call rate below 95% and MAF below 0.1%, and selecting individuals of recent British ancestry without close relatives, we used PRS-CS[ 24 ] with GWAS data from 36,111 White British individuals to derive the PRS. In the “ALPS Index-Association Analysis” step, the ALPS index-PRS was the independent variable, and factors with pFDR < 0.05 were the dependent variables. We adjusted for age, sex, genotyping array, the top 10 genetic principal components, and assessment center. This “ALPS Index-PRS-PheWAS” was conducted using the PHESANT package in R, employing standardized β for linear models and log-transformed OR for binary outcomes. All 181 association results were subjected to FDR correction. Validation with Neurological Diseases Using Mendelian Randomization and Cox To validate the significant negative associations, we used multivariable Cox proportional hazards models to assess the relationship between the ALPS Index and the incidence of neurological diseases (Alzheimer’s disease [AD], Multiple sclerosis [MS], cerebral infarction, stroke). Follow-up started at the second recruitment visit and continued until diagnosis, death, loss to follow-up, or last hospital admission. Models were adjusted for age, sex, Townsend deprivation index, and BMI, with the proportional hazards assumption tested using Schoenfeld residuals. Participants with prior neurological diseases were excluded. Additionally, two-sample Mendelian Randomization (MR)[ 25 ] was conducted using inverse-variance weighted (IVW) and other robust methods to explore causal relationships between the ALPS Index and neurological diseases, employing independent genetic instruments and performing sensitivity analyses for heterogeneity and pleiotropy. Detailed methods are provided in Supplementary Methods. Results ALPS Index in 36,997 UK Biobank Participants Fig. 1 illustrates the overarching framework of our study. Among the 43,823 UKBB participants with post-processed DTI data, the average diffusivity along the x-axis in the projection and association fibers (Dxproj and Dxassoc), as well as the average diffusivity along the y-axis (Dyproj) and z-axis (Dzassoc), were extracted. This allowed for the computation of the left, right, and bilateral mean ALPS indices (see Supplementary Fig. 1 and Methods section). Following stringent quality control and genetic analyses, a total of 36,997 UKBB participants with bilateral mean ALPS indices were retained for further examination. To achieve normal distribution of the ALPS index, a rank-based inverse normal transformation was applied prior to analysis. Download figure Open in new tab Figure 1. Study overview. The top section outlines the process of calculating the ALPS Index. The bottom-left section presents the GWAS analysis and subsequent analyses such as transcriptome-wide association studies (TWAS) and biological function analysis of the identified genes. The bottom-right section focuses on exploring the phenome-wide associations of the identified genes. Ideogram of associated genomic loci was generated using PhenoGram ( http://visualization.ritchielab.org/phenograms/plot ). Variants Associated with ALPS index To ensure the integrity of our cohort, participants with neurological or psychiatric disorders (ICD-10 codes F or G) were excluded from the analysis. Consequently, the study encompassed ALPS index data from 36,111 white British participants for GWAS analysis. The mean age of this sample was 64 years (SD: 7.69; range: 45–83 years), with females constituting 52.73% of the cohort. Using the FUMA platform, 14 independent genome-wide significant loci were identified ( Table 1 ), associated with white matter integrity, fiber tract connectivity, brain volume, inflammation, and metabolism (Supplementary Table 3). These loci encompassed 3,814 significant SNPs linked to the ALPS index ( Fig. 2 , Table 1 , Supplementary Fig. 2, and Supplementary Table 4). No genomic inflation was detected (Supplementary Fig. 3). Most significant SNPs were intronic or intergenic (Supplementary Table 4), with only 51 exonic variants. Additionally, 108 SNPs had deleterious Combined Annotation Dependent Depletion (CADD) scores (> 12.37) and 63 were in open chromatin regions (commonChrState of 1 or 2). LDSC analysis showed a genomic inflation factor (λ GC ) of 1.16 and heritability of 0.26. The LDSC intercept was 1.01 with an attenuation ratio of 0.07, indicating inflation was due to polygenicity rather than population stratification, ensuring robust findings. Download figure Open in new tab Figure 2. Manhattan plot showing the GWAS results. Manhattan plots showing the GWAS results derived from a two-step linear mixed model using the regenie software, with ridge regression in step one and linear regression in step two. The horizontal axis indicates chromosomal position. The vertical axis indicates −log10(P value) of the association. The dotted lines indicate the genome-wide-significance threshold of P=□5□×□10 −8 . Gene, associated gene in the identified genomic locus, the nearest Gene of the SNP based on ANNOVAR annotations. AV-ALPS, average of the left and right ALPS index. P-values are two-sided and unadjusted for multiple testing. View this table: View inline View popup Table 1. 14 genomic risk loci identified with ALPS Index from 36,111 samples. GCTA-COJO identified 15 independent genome-wide significant SNPs (MAF□≥□0.01, P□<□5×10□□). Fourteen of these SNPs were within the FUMA-identified loci, and one was outside (Supplementary Tables 3 and 5). Identification and Functional Exploration of Candidate Genes Related to ALPS Index By analyzing GWAS summary statistics, we identified genes using four methods: positional mapping identified 46 genes within ±10 kb of lead variants (Supplementary Table 6), MAGMA gene analysis revealed 45 significant genes (mean χ² statistic, P < 0.05/19141 = 2.61 × 10□□) (Supplementary Table 7), TWAS uncovered 56 genes ( Fig. 3 and Supplementary Table 8), and Bayesian colocalization integrated eQTL and sQTL data to identify 143 genes ( Fig. 3 and Supplementary Table 9). Combining these methods yielded 206 candidate genes, with 11 genes consistently identified by all four methods, 14 by three methods, and 48 by two methods ( Fig. 4a and Supplementary Table 10). Download figure Open in new tab Figure 3. Transcriptome-wide significant genes with ALPS Index. We used precomputed functional weights from 22 publicly available gene expression reference panels from GTEx v8. Transcriptome-wide significant genes and the corresponding eQTLs were determined using Bonferroni correction based on the number of features tested in different tissues: Artery_Aorta (7,817), Artery_Coronary (3,898), Artery_Tibial (9,561), Brain_Amygdala (2,604), Brain_Anterior_cingulate_cortex_BA24 (3,451), Brain_Caudate_basal_ganglia (5,036), Brain_Cerebellar_Hemisphere (6,091), Brain_Cerebellum (7,271), Brain_Cortex (5,608), Brain_Frontal_Cortex_BA9 (4,517), Brain_Hippocampus (3,547), Brain_Hypothalamus (3,543), Brain_Nucleus_accumbens_basal_ganglia (4,988), Brain_Putamen_basal_ganglia (4,283), Brain_Spinal_cord_cervical_c-1 (3,112), Brain_Substantia_nigra (2,257), Heart_Atrial_Appendage (6,740), Heart_Left_Ventricle (5,886), Muscle_Skeletal (8,515), Nerve_Tibial (11,274), Pituitary (6,177), Whole_Blood (7,980). *Significant result in the TWAS. **significant result in the TWAS and conditional analyses, and with a COLOC PP4□>□0.8. Download figure Open in new tab Figure 4. Post-GWAS analysis results. a: Annotation of candidate genes by using four different approaches. The Venn diagram illustrates the overlap of candidate genes for the ALPS index identified by four methods (Colocalization, TWAS, posMap, MAGMA). The bar chart in the lower left corner represents the proportion of annotated genes at the end of systole using these four methods: red indicates genes annotated by all four methods, purple indicates genes annotated by three methods, green indicates genes annotated by two methods, and yellow indicates genes annotated by only one method. TWAS, transcriptome-wide association study; MAGMA, multi-marker genome-wide annotation analysis. b: Gene set enrichment analysis of significant genes identified in the GWAS, using Gene Ontology and KEGG Ontology database. The p-values reported are two-sided and unadjusted. c: UMAP visualization displays clusters of human brain vascular cells, labeled according to cell types annotated with marker genes from previously published studies. d: Subpopulations of human brain vascular cells associated with ALPS Index. Cells with significant associations are marked in red, representing scDRS disease scores; other cells are shown in blue. Several of these genes are closely linked to vascular function and CSF flow. For instance, GNA12 regulates smooth muscle contraction and supports endothelial integrity[ 26 ], while SERPIND1 is involved in blood coagulation and inflammation[ 27 ]. Genes such as DPYSL5 and MAPT play crucial roles in nervous system development and are associated with neurodegenerative diseases like AD, potentially affecting CSF dynamics and glymphatic clearance[ 28 , 29 ]. GCAT participates in amino acid metabolism[ 30 ], while KHK plays a key role in fructose metabolism[ 31 ], and GNA12 regulates cellular metabolism via the TOR signaling pathway[ 32 ]. such as TRIOBP and SNAP29 , likely influence CSF flow and waste clearance. Additionally, some genes, including C16orf95 and ABHD1 , may regulate molecular exchange between CSF and the lymphatic system, warranting further investigation. Gene enrichment analysis using the DAVID platform revealed that the candidate genes are significantly enriched in pathways related to neuronal development, cell structure organization, signal transduction, enzyme activity, and metabolic processes ( Fig. 4b and Supplementary Table 11). The single-cell disease relevance score (scDRS) algorithm showed significant enrichment of these genes in astrocytes (r = 0.11, p < 0.001) and oligodendrocytes (r = 0.12, p < 0.001) ( Fig. 4c-d ). Furthermore, the GWAS Catalog associated these genes with various brain imaging metrics, psychological and behavioral traits, neurological diseases such as Parkinson’s and Alzheimer’s, metabolic and blood indicators, lifestyle factors, and molecular markers, highlighting their potential roles in neural health and related biological processes (Supplementary Table 12). Exploratory analyses identified enrichment of ALPS index-related genes in validated drug targets for other indications. For example, CRHR1 targets include Flortaucipir F-18 and PBT-1033 used in diagnosing Cushing’s syndrome and psychiatric disorders, respectively[ 33 , 34 ]. MAPT targets like Paclitaxel and Docetaxel are utilized in cancer treatment and Alzheimer’s research[ 35 , 36 ]. LPAR1 -driven drug BMS-986020 is currently in clinical trials [ 37 ], while SERPIND1 and TNIK -driven drugs such as Sulodexide, Bemiparin, and Fostamatinib are used for treating venous thrombosis, rheumatoid arthritis, and immune thrombocytopenia[ 38 , 39 ] (Supplementary Table 13). These findings suggest that the identified candidate genes not only play crucial roles in the biological mechanisms underlying the ALPS index but also represent potential targets for therapeutic interventions. Association Analysis Between ALPS Index and Five Major Categories of Non-Imaging Phenotypes Previous small-scale studies have linked the ALPS index to diseases such as dementia, cerebrovascular disease, Parkinson’s, sleep disorders, type 2 diabetes, migraines, epilepsy, schizophrenia, multiple sclerosis, neuromyelitis optica, and glioma. To systematically investigate these associations, we analyzed the rank-inverse normal transformed ALPS index from 36,997 participants against 2,121 non-imaging phenotypes from the UK Biobank, categorized into Biomarkers and Physical Measurements, Medical Conditions and Treatments, Cognitive and Mental Health, Lifestyle and Social Factors, and Sex-specific Factors (Supplementary Table 14). After adjusting for age, sex, and assessment center, 181 phenotypes showed significant associations (β range: −0.675 to 0.435; p FDR : 1.44□×□10□²□ to 4.99□×□10□²) ( Fig. 5 and Supplementary Table 15). Complete results for all 2,121 phenotypes are detailed in Supplementary Table 16. Download figure Open in new tab Figure 5. Manhattan plot showing associations of phenotypes with ALPS Index, grouped by categories. The x-axis represents phenotypes, and the y-axis represents the −log10 of uncorrected p values of two-sided test for linear regression between each phenotype and the ALPS Index (see Supplementary Table 14 for detailed results). Each dot represents one phenotype, and the colours indicate their according categories. The size of the dots corresponds to the magnitude (absolute value) of the effect between the phenotype and ALPS Index. The solid dots represent phenotypes that exhibited significant associations with the ALPS Index. The dashed lines indicate the threshold to survive Benjamini-Hochberg procedure (FDR correction). Significant associations were observed in key categories, particularly blood cells, blood pressure, biochemistry, and body measurements. Blood cell-related phenotypes, including red and white blood cell counts, reticulocyte count, etc., displayed negative associations with the ALPS index (β = −0.042 to −0.014, p FDR = 1.09 × 10□³ to 2.64 × 10□²), suggesting that alterations in blood composition may affect cerebrovascular health and brain fluid circulation. Blood pressure indicators (diastolic and systolic) also exhibited negative associations (β = −0.051 to −0.033, p FDR = 5.14 × 10□¹□ to 1.41 × 10□□), indicating that hypertension may impede cerebrovascular function and glymphatic clearance. Body measurements, including weight, BMI, and waist circumference, were significantly negatively correlated with the ALPS index (β = −0.047 to −0.015, p FDR = 1.09 × 10□¹□ to 6.67 × 10□²□), while height showed a positive association (β = 0.013 to 0.016, p FDR = 1.01 × 10□² to 6.64 × 10□□), suggesting that changes in body fat distribution may decrease glymphatic function, whereas healthier body composition may enhance brain fluid circulation. Additionally, fat mass indicators (whole-body, trunk, and leg fat mass) exhibited negative associations with the ALPS index (β = −0.039 to −0.015, p FDR = 1.09 × 10□¹□ to 1.34 × 10□¹□), implying that excessive fat may inhibit fluid flow. Lean mass in the arms and legs was also negatively correlated (β = −0.019 to −0.017, p FDR = 1.67 × 10□□ to 3.75 × 10□□), suggesting that muscle loss may disrupt fluid balance. Biochemical markers such as uric acid, glycated haemoglobin, and C-reactive protein displayed significant negative associations with the ALPS index (β = −0.056 to −0.029, p FDR = 1.24 × 10□□ to 4.18 × 10□□), indicating that metabolic abnormalities may impair brain fluid balance and glymphatic function. Finally, additional indicators from pulmonary function, ocular measurements, and impedance measures were assessed. Pulmonary function metrics (Forced Vital Capacity [FVC] and Forced Expiratory Volume in 1 second [FEV□]) positively correlated with the ALPS index, suggesting respiratory health supports brain fluid balance. Additionally, eye surgeries and impedance measures may reflect changes in brain fluid dynamics. Diabetes, including non-insulin-dependent and insulin-dependent types, obesity, and dyslipidemia were significantly negatively correlated with the ALPS index (β = −0.286 to −0.058, p FDR = 1.48 × 10□□ to 2.65 × 10□□), suggesting that metabolic abnormalities may disrupt brain fluid dynamics via vascular and metabolic pathways. Cardiovascular and cerebrovascular diseases, such as hypertension, angina, and cerebral infarction, also showed negative associations (β = −0.240 to −0.101, p FDR = 1.26 × 10□³ to 2.89 × 10□□), indicating impaired cerebrovascular function and glymphatic clearance. Medical interventions, including antihypertensive medications and aspirin, also correlated negatively with the ALPS index (β = −0.122 to −0.088, p FDR = 9.46 × 10□□ to 1.30 × 10□³), potentially affecting glymphatic clearance through metabolic and vascular regulation. Neurological disorders, including MS, AD, and dementia, exhibited significant negative associations (β = −0.592 to −0.506, p FDR = 1.27 × 10□² to 2.36 × 10□²), suggesting impaired neuronal function and waste clearance. Kidney diseases (acute and chronic renal failure) and respiratory diseases (asthma and Chronic Obstructive Pulmonary Disease) were negatively correlated with the ALPS index, suggesting that impaired renal and lung function may disrupt brain fluid balance, whereas bilateral vasectomy showed a positive association, potentially related to metabolic or vascular changes. In the cognitive and mental health domain, the ALPS index was positively correlated with the number of attempts and correct matches in symbol-matching tasks and negatively correlated with task completion and identification times (β = −0.039 to 0.050, p FDR = 2.02□×□10□² to 2.85□×□10□¹□), indicating that efficient brain fluid circulation is associated with enhanced cognitive performance. In health behaviours and lifestyle factors, physical exercise indicators positively correlated with the ALPS index (β = 0.033 to 0.067, p FDR = 1.91□×□10□□ to 4.01□×□10□²), suggesting enhanced brain fluid circulation, whereas smoking, increased computer use, video gaming, and circadian rhythm disturbances negatively correlated (β = −0.131 to −0.036, p FDR = 1.95□×□10□² to 3.41□×□10□³), indicating impaired glymphatic clearance. Additionally, socioeconomic factors and birth weight showed positive associations (β = 0.040 to 0.051, p FDR = 2.05□×□10□³ to 4.50□×□10□□), reflecting better health management, while sex-specific factors such as number of children and age at first use of oral contraceptives were also associated with the ALPS index, suggesting potential sex-specific influences on brain fluid dynamics. Polygenic Risk Score Validation of ALPS Index Associations Across Five Phenotype Categories In the validation process, to evaluate the predictive efficacy of the polygenic risk score (PRS)[ 24 ] for the ALPS Index, we calculated the PRS in 370,920 UKBB participants without directly measured ALPS Index data, using PRS-CS and GWAS summary statistics from 36,111 White British participants. The ALPS index-PRS was set as the independent variable, with age, sex, genotyping array, top 10 genetic principal components, and assessment centre as covariates. A positive association was observed, indicating strong predictive capacity (Supplementary Fig. 4). PRS-PheWAS analysis assessed 181 phenotypes, revealing significant associations with 42 phenotypes, with consistent effect directions for 29 phenotypes (Supplementary Tables 15 and 16). Significant associations were found for 7.5% of biomarkers and physical measurements, 0.8% of lifestyle factors, and 0.2% of medical conditions. Negative associations with the ALPS Index were observed for diastolic and systolic blood pressure, haematological markers (red blood cell distribution width and reticulocyte count), the date of first AD diagnosis, time spent playing computer games, and dietary diversity. In contrast, positive associations were identified for lung function indicators (FVC and FEV□), body measurements (height, Sex Hormone-Binding Globulin, vitamin D levels), birth weight, walking speed, preference for green olives, and the age at first use of glasses or contact lenses. Validation of ALPS Index Associations with Neurological Diseases Using Mendelian Randomization and Cox Regression In the association analysis, significant negative associations were identified between the ALPS Index and neurological diseases, including AD, unspecified dementia, MS, cerebral infarction, stroke (unspecified as haemorrhage or infarction), and other cerebrovascular diseases (β = −0.592 to −0.206, p FDR = 4.50□×□10□ 8 to 4.77□×□10 −2 ). To further validate these associations, Cox proportional hazards models were applied to evaluate time-to-event data, providing insights into the influence of the ALPS Index on disease risk or progression over time. The ALPS Index was divided into three categories based on tertiles: low, medium, and high. Compared to the low ALPS Index category, stronger protective effects were observed, particularly for AD (HR = 0.645, p = 6.09 × 10□□) and MS (HR = 0.655, p = 1.34 × 10□□), indicating a significant reduction in disease risk ( Fig. 6 and Supplementary Table 17). Following this, Mendelian randomization (MR)[ 25 ] was employed to assess potential causal relationships between the ALPS Index and AD and ischemic stroke, showing a protective effect (OR = 0.889 for AD, p = 2.630□×□10 −2 ; OR = 0.867 for ischemic stroke, p = 2.590□×□10 −2 , Supplementary Table 18). MR-Egger intercept tests indicated no horizontal pleiotropy and heterogeneity tests confirmed these associations (Supplementary Table 19). Collectively, these findings indicate that a higher ALPS Index is associated with a reduced risk of AD and MS, as shown by association and Cox regression analyses, as well as a reduced risk of AD and ischemic stroke, as supported by MR analysis. Download figure Open in new tab Figure 6. Cox Proportional Hazards Analysis of ALPS Index and Neurological Diseases. This figure shows the Cox proportional hazards model results for the association between the ALPS Index and four common neurological diseases: AD, MS, cerebral infarction (ischemic stroke, IS), and stroke (unspecified as hemorrhage or infarction). The curves represent the time-to-event analysis, with the x-axis indicating the follow-up time in years and the y-axis showing the survival probability for the respective neurological diseases. Three categories of the ALPS Index were analyzed: continuous, medium, and high. The hazard ratios (HR) for each disease and category are provided along with their corresponding 95% confidence intervals (L95, U95) and p-values, as adjusted for age, sex, genotyping array, Townsend deprivation index, and body mass index (BMI). Participants with pre-existing neurological conditions were excluded from the analysis. Discussion To our knowledge, this study represents the largest individual-level GWAS to date investigating the genetic architecture of the ALPS index, a key indicator of glymphatic system function within the brain. Utilizing data from 36,997 participants in the UKBB, we employed a novel analytical framework to calculate the ALPS index from DTI data and conducted comprehensive genetic analyses. Our GWAS identified 14 independent genome-wide significant loci associated with the ALPS index, encompassing 3,814 significant single nucleotide polymorphisms (SNPs). The candidate genes implicated by these loci are involved in critical biological pathways related to neuronal development, vascular integrity, and metabolic processes. Furthermore, our extensive association analysis revealed significant associations between the ALPS index and a diverse range of non-imaging phenotypes across five major categories: biomarkers and physical measurements, medical conditions and treatments, cognitive and mental health, lifestyle and social factors, and sex-specific factors. These findings underscore the genetic basis of glymphatic function and highlight its potential role in the risk stratification and early detection of neurodegenerative diseases. Collectively, our results provide valuable insights into the genetic determinants of brain fluid dynamics and their implications for neural health. A total of 3,814 variants were identified across 14 genome-wide significant loci. Analysis of these loci associated with the ALPS index highlighted their enrichment in intronic and intergenic regions, indicating a potential regulatory role in glymphatic function. Notably, only 1.34% of the variants were exonic, suggesting that non-coding regions may significantly influence glymphatic activity and brain health. This conclusion is further supported by the presence of variants in regions of active chromatin, including open chromatin areas, suggesting involvement in the transcriptional regulation of genes related to glymphatic efficiency. These risk variants also showed enrichment in genetic regions linked to traits such as white matter integrity, fibre tract connectivity, brain structure volume, inflammation, and metabolism, highlighting the interplay between glymphatic function and broader neurobiological processes. Furthermore, results from LDSC regression indicated that the observed genomic inflation was primarily due to polygenicity rather than population stratification, supporting the robustness of the findings. Additional analyses using GCTA-COJO identified 15 independent SNPs within the 14 significant loci identified by FUMA, further validating these associations. Collectively, these findings suggest that genetic variants influencing the ALPS index predominantly exert regulatory effects, with broader implications for understanding the genetic basis of glymphatic function and its link to neurological health and disease risk. The identified variations associated with the ALPS Index were primarily located in gene regions implicated in regulating white matter integrity, fibre tract connectivity, and metabolic function. These aspects are closely linked to the mechanisms underlying brain waste clearance[ 6 ]. Functional annotation of the ALPS Index-associated loci revealed key genes related to cerebrovascular function, including GNA12 , SERPIND1 , and LPAR1 , which may enhance the efficiency of the glymphatic system by regulating brain fluid flow[ 40 ]. Notably, GNA12 modulates smooth muscle contraction through the PI3K/ROCK signalling pathway and protects endothelial cells by maintaining miR-155 levels, underscoring its pivotal role in vascular function regulation[ 41 ]. GNA12 was consistently identified in both GWAS and functional annotation analyses as a gene linked to the regulation of brain fluid dynamics, particularly in regions associated with smooth muscle and cerebrovascular function. This suggests that its variants may impact glymphatic waste clearance by altering cerebral hemodynamics[ 42 ]. Additionally, our study revealed associations between the ALPS Index and genes implicated in neurodevelopment and neurodegenerative diseases. Notably, MAPT was frequently identified in analyses related to the ALPS Index[ 43 ]. MAPT encodes the Tau protein, whose abnormal accumulation is a hallmark of AD[ 44 ]. Our findings suggest that variants in MAPT may influence CSF clearance by promoting Tau protein accumulation, potentially accelerating the progression of neurodegenerative diseases. Furthermore, MAPT -targeted therapies, such as paclitaxel and docetaxel—commonly used in cancer treatment—underscore MAPT ’s role in cytoskeletal dynamics and protein metabolism[ 45 ]. This indicates that Tau accumulation, in addition to its connection with neuronal damage, may also impair glymphatic clearance, thereby increasing the risk of neurodegenerative diseases. Among the candidate genes, LPAR1 emerged as particularly significant. LPAR1 is integral to cell migration and morphogenesis and plays a critical role in myelination and functional connectivity within the brain[ 46 ]. Our findings indicate that LPAR1 is closely linked to the regulation of glymphatic function, particularly in relation to brain waste clearance.[ 47 ]. Moreover, drugs targeting LPAR1 , such as BMS-986020, which are currently undergoing clinical trials, present promising avenues for future research into therapies aimed at addressing glymphatic system dysfunction[ 37 ]. Another significant gene, DPYSL5 , was repeatedly associated with glymphatic system function, particularly in neuronal migration and axon guidance[ 28 ]. DPYSL5 plays a crucial role in regulating brain fluid dynamics and waste clearance, with mutations potentially impacting the ALPS Index and, consequently, neurodevelopment and cognitive function. In our exome-wide analysis, the synonymous variant (p.Gly558Gly) of DPYSL5 was significantly associated with the ALPS Index, further supporting its involvement in glymphatic regulation. Given its critical function during neurodevelopment, variations in DPYSL5 may indirectly influence glymphatic waste clearance by modulating neuronal migration and synaptic transmission[ 48 ]. SERPIND1 was identified in multiple analyses related to the ALPS Index as a gene closely tied to CSF clearance. It primarily regulates blood coagulation and inflammatory responses, thereby influencing vascular function and brain fluid dynamics[ 49 ]. Drugs targeting SERPIND1 , like sulodexide and heparin, which are already used for venous thrombosis prevention and treatment, support its potential as a therapeutic target for regulating glymphatic function[ 38 ]. Additionally, TNIK modulates neurodevelopment and synaptic transmission, indirectly impacting CSF flow and metabolic waste clearance[ 50 ]. Current drugs targeting TNIK , such as fostamatinib, used for rheumatoid arthritis and immune thrombocytopenia, suggest that this gene could serve as a novel target for future therapies addressing glymphatic system dysfunction[ 51 ]. The gene enrichment analyses revealed that candidate genes associated with the ALPS index are involved in critical neuronal and cellular processes, such as neuronal development and metabolic pathways. This aligns with existing research on the genetic underpinnings of neurodevelopmental and neurodegenerative disorders. For example, genes related to neuronal structure and signal transduction pathways have been previously implicated in neurodegenerative conditions like AD and PD[ 52 ]. The scDRS algorithm’s identification of significant gene enrichment in astrocytes and oligodendrocytes underscores their role in neural homeostasis and disease progression, consistent with studies highlighting astrocytes’ involvement in neuroinflammation and myelination processes, which are crucial for maintaining brain health[ 53 ]. Furthermore, the significant associations between candidate genes and brain imaging metrics, cognitive traits, and metabolic indicators point to a complex genetic architecture underlying cognitive and psychological traits, reinforcing the genetic overlap between neurological and metabolic conditions[ 54 ]. Additionally, drug target enrichment analyses suggest a potential for repurposing existing drugs that target ALPS-associated genes, such as CRHR1 - and MAPT -driven therapies, for neurodegenerative conditions. These findings highlight the translational potential of ALPS-related genes in guiding therapeutic strategies for both neurological and non-neurological conditions. Our study found significant negative associations between the ALPS index and various haematological traits, including red blood cell count, white blood cell count, and reticulocyte count. This suggests that alterations in immune function and blood composition may profoundly affect brain health by disrupting fluid balance and waste clearance[ 55 ]. Furthermore, the negative associations with diastolic and systolic blood pressure reinforce the detrimental effects of hypertension on cerebrovascular health, indicating that elevated blood pressure may hinder CSF flow and reduce waste clearance efficiency. Previous studies have linked hypertension to white matter lesions and disturbances in CSF circulation, supporting these findings[ 56 ]. Anthropometric measures such as weight, BMI, and waist circumference showed negative associations with the ALPS Index, while height correlated positively, suggesting that increased adiposity may impair glymphatic function by disrupting brain fluid dynamics[ 57 , 58 ]. Negative associations with biochemical markers like uric acid, glycated haemoglobin, and C-reactive protein further link metabolic abnormalities and chronic inflammation to impaired brain waste clearance[ 59 ]. Medical conditions related to metabolic dysfunction—including diabetes and its subtypes, obesity, and dysregulated lipid metabolism—were also negatively associated with the ALPS Index. These findings indicate that metabolic dysregulation, especially chronic fluctuations in blood glucose and increased adiposity, may impair glymphatic clearance by affecting the cerebrovascular system and altering CSF composition and flow[ 60 ]. In the cardiovascular domain, significant negative associations were observed between hypertension, angina, and the ALPS Index, reinforcing the known link between hypertension and cerebrovascular dysfunction. Elevated blood pressure may compromise vascular structure and elasticity, impeding normal CSF flow and reducing waste clearance efficiency[ 56 ]. Chronic hypertension has been shown to contribute to white matter lesions and abnormal CSF circulation, adversely affecting brain health[ 61 ]. Additionally, ischemic stroke and cerebrovascular diseases, such as cerebral infarction, were significantly correlated with lower ALPS Index values, indicating that disruptions in cerebral blood supply during stroke can severely impair CSF flow and glymphatic clearance[ 62 ], potentially exacerbating neuronal damage and neurodegeneration during recovery[ 63 ]. Neurological diseases, including AD, MS, and unspecified dementia, showed significant negative associations with the ALPS Index, implying that these conditions may hinder glymphatic clearance by disrupting immune and metabolic functions in the brain. Patients with these conditions frequently exhibit restricted CSF flow and accumulated metabolic waste, both of which are closely linked to impaired glymphatic function[ 64 ]. Moreover, the inflammatory responses and immune dysregulation associated with neurodegenerative diseases, particularly in MS, may further exacerbate these impairments, highlighting the intricate relationship between glymphatic efficiency and neurological health[ 65 ]. In addition, validation through Cox regression confirmed a protective role of a higher ALPS Index in reducing the risk of AD and MS, while MR supported its protective effect on AD and ischemic stroke (cerebral infarction). These findings further reinforce the hypothesis that improved glymphatic function, as reflected by a higher ALPS Index, is associated with a lower risk of neurodegenerative and cerebrovascular conditions. This underscores the potential of the ALPS Index as a valuable marker for assessing both glymphatic function and neurological disease risk. Although this study demonstrates a strong capacity for discovery, several limitations should be noted. This study was primarily conducted in populations of European ancestry, validation in other ethnic groups is a crucial step to ensure the generalizability of the findings. Future research should also explore therapeutic interventions targeting the glymphatic system, especially in individuals carrying rare loss-of-function variants. Moreover, integrating multi-omics data (e.g., proteomics, metabolomics) may help uncover the molecular mechanisms underlying glymphatic system function. Conclusion In conclusion, this study systematically elucidates the genetic architecture of the ALPS Index, identifying key loci and pathways associated with glymphatic system function and its broad impact on brain health. These findings provide new insights into the role of the glymphatic system in neurodegenerative diseases and offer a foundation for the development of therapeutic strategies targeting glymphatic dysfunction. Declarations Ethics approval and consent to participate This study was conducted under approved project 221671 by the UK Biobank, following protocols sanctioned by the National Research Ethics Service Committee ( http://www.ukbiobank.ac.uk/ethics/ ). All participants provided written informed consent. Consent for publication All authors have read and agreed to the published version of the manuscript. Availability of data and materials Data from the UK Biobank Diffusion-Weighted Imaging (DWI) scans were collected according to the published protocol ( https://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=2367 ). Permission to use the UK Biobank Resource was obtained via material transfer agreement as part of Data Access Application 221671. All imaging data, phenotypes and genetics data are made available by UK Biobank via their standard data access procedure (see http://www.ukbiobank.ac.uk/register-apply ). All other data supporting the findings of this study are available within the article, the supplementary information or the supplementary data files. Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China to C.S. [grant number 82371433, 82171247], the Scientific Research and Innovation Team of the First Affiliated Hospital of Zhengzhou University to C.S. [grant number ZYCXTD2023011], and the National Natural Science Foundation of China to C.M. [grant number 82271277]. Authors’ contributions C.S. conceived and designed this study. D.M. drafted the manuscript. C.S., S.T., C.M., Y.J., Y.X., Y.W., S.S., and O.A.A. revised the manuscript. D.M., S.L., C.Z., Z.W., Y.S., S.Q., Y.L., C.H., Y.F., X.H., M.L., and R.Y. performed statistical analysis. C.S. supervised the project. C.S. and C.M. obtained funding. All authors have read and approved the final version of the manuscript. Acknowledgements This research was conducted using the UKBB Resource under application number 221671. We are grateful to UK Biobank for making the data available and to all UK Biobank study participants who generously donated their time to make this resource possible. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Abbreviations AD Alzheimer’s disease CADD Combined Annotation Dependent Depletion CSF Cerebrospinal fluid DTI Diffusion tensor imaging DTI-ALPS Diffusion Tensor Imaging Along the Perivascular Space DWI Diffusion-weighted imaging eQTL Expression Quantitative Trait Loci FDR False discovery rate FEV□ Forced Expiratory Volume in 1 second FVC Forced Vital Capacity GCTA-COJO Conditional and joint multiple-SNP analysis using Genome-wide Complex Trait Analysis GO Gene Ontology GTEx v8 Genotype-Tissue Expression project version 8 GWAS Genome-wide association study IVW Inverse-variance weighted LDSC Linkage Disequilibrium SCore regression MAF Minor Allele Frequency MAGMA Multi-marker Analysis of GenoMic Annotation MR Mendelian Randomization MS Multiple sclerosis OR Odds ratio PRS Polygenic risk score ROIs Regions of interest scDRS Single-cell disease relevance score SNP Single nucleotide polymorphism SNVs Single nucleotide variants sQTL Splicing Quantitative Trait Loci TWAS Transcriptome-wide association study UKBB UK Biobank References 1. ↵ Lohela TJ , Lilius TO , Nedergaard M : The glymphatic system: implications for drugs for central nervous system diseases . 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Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Genetic and Phenotypic Architecture of Brain Glymphatic System Changhe Shi , Dongrui Ma , Shuangjie Li , Chunyan Zuo , Zhiyun Wang , Yuemeng Sun , Shasha Qi , Yuanyuan Liang , Chenwei Hao , Yanmei Feng , Zhengwei Hu , Xiaoyan Hao , Mengjie Li , Ruwei Yang , Song Tan , Chengyuan Mao , Ying Jing , Yuming Xu , Yunpeng Wang , Shilei Sun , Ole A. Andreassen medRxiv 2025.03.23.25323721; doi: https://doi.org/10.1101/2025.03.23.25323721 Share This Article: Copy Citation Tools Genetic and Phenotypic Architecture of Brain Glymphatic System Changhe Shi , Dongrui Ma , Shuangjie Li , Chunyan Zuo , Zhiyun Wang , Yuemeng Sun , Shasha Qi , Yuanyuan Liang , Chenwei Hao , Yanmei Feng , Zhengwei Hu , Xiaoyan Hao , Mengjie Li , Ruwei Yang , Song Tan , Chengyuan Mao , Ying Jing , Yuming Xu , Yunpeng Wang , Shilei Sun , Ole A. 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