Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques

Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques | 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 Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques View ORCID Profile Emma Pruin , Meike Bartels , Ernest Diez Benavente , Noortje A.M. van den Dungen , Joost K.R. Hoekstra , Dominique D.P. de Kleijn , Lennart P.L. Landsmeer , Michal Mokry , Gerard Pasterkamp , Brenda W.J.H. Penninx , Wouter J. Peyrot , View ORCID Profile Hester M. den Ruijter , View ORCID Profile Sander W. van der Laan , Yuri Milaneschi doi: https://doi.org/10.1101/2025.07.11.25331336 Emma Pruin 1 Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands 2 Amsterdam Public Health, Mental Health program , Amsterdam, The Netherlands 3 Amsterdam Public Health, Methodology program , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emma Pruin For correspondence: e.pruin{at}amsterdamumc.nl y.milaneschi{at}amsterdamumc.nl s.w.vanderlaan-2{at}umcutrecht.nl Meike Bartels 2 Amsterdam Public Health, Mental Health program , Amsterdam, The Netherlands 10 Amsterdam Public Health, Personalized Medicine program , Amsterdam, The Netherlands 11 Department of Biological Psychology, Vrije Universiteit Amsterdam . The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ernest Diez Benavente 9 Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Noortje A.M. van den Dungen 6 Central Diagnostic Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Joost K.R. Hoekstra 7 Department of Vascular Surgery, Division of Surgical Specialties, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands 12 Department of Nephrology and Hypertension, Division of Internal Medicine and Dermatology, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands 13 Regenerative Medicine Center Utrecht, University Medical Center Utrecht, University of Utrecht , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dominique D.P. de Kleijn 7 Department of Vascular Surgery, Division of Surgical Specialties, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lennart P.L. Landsmeer 6 Central Diagnostic Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michal Mokry 6 Central Diagnostic Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gerard Pasterkamp 6 Central Diagnostic Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brenda W.J.H. Penninx Find this author on Google Scholar Find this author on PubMed Search for this author on this site Wouter J. Peyrot 1 Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands 2 Amsterdam Public Health, Mental Health program , Amsterdam, The Netherlands 3 Amsterdam Public Health, Methodology program , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Hester M. den Ruijter 6 Central Diagnostic Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands 9 Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, Utrecht University , Utrecht, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hester M. den Ruijter Sander W. van der Laan 6 Central Diagnostic Laboratory, Division Laboratory, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht , Utrecht, The Netherlands 8 Department of Genome Sciences, University of Virginia , Charlottesville, VA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sander W. van der Laan For correspondence: e.pruin{at}amsterdamumc.nl y.milaneschi{at}amsterdamumc.nl s.w.vanderlaan-2{at}umcutrecht.nl Yuri Milaneschi 1 Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands 2 Amsterdam Public Health, Mental Health program , Amsterdam, The Netherlands 4 Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program , Amsterdam, The Netherlands 5 Amsterdam Neuroscience, Complex Trait Genetics program , Amsterdam, The Netherlands PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: e.pruin{at}amsterdamumc.nl y.milaneschi{at}amsterdamumc.nl s.w.vanderlaan-2{at}umcutrecht.nl Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background The increased risk of atherosclerotic diseases (stroke, coronary artery disease [CAD]) observed in depression may stem from shared pathophysiology. We examined whether: 1) major depression (MD) and atherosclerotic traits share genetic risk, and 2) altered gene expression in various tissues linked to shared genetics has a potential causal role in depression etiology. Methods Data from the largest genome-wide association studies of MD (N=3,887,532) and 8 atherosclerotic traits (N=26,909-1,308,460) were used in Mendelian randomization and colocalization to detect cross-trait causal associations and genomic loci containing shared causal variants. In shared loci, summary data-based Mendelian randomization estimated the effects of gene expression on MD etiology using expression quantitative trait loci datasets from whole blood, brain and heart tissues and atherosclerotic plaques from the Athero-Express Biobank Study. Results MD genetic liability increased risk of any stroke (OR=1.15, p=9.47×10 -8 ), ischemic stroke (OR=1.16, p=1.52×10 -7 ), small vessel disease (OR=1.34, p=4.76×10 -5 ) and CAD (OR=1.2, 95%CIs=1.13-1.26, p=3.76×10 -22 ). Eight genomic regions harbored potentially shared causal variants, including one on chromosome 7 linking MD with any stroke, ischemic stroke and CAD. Altered expression of 16 genes in blood, 10 in brain, and 6 in heart was found causal for MD etiology. In atherosclerotic plaques, one gene was linked to MD at nominal significance only. Conclusion Major depression and atherosclerotic diseases share genetic risk potentially acting in depression pathophysiology through expression of genes in blood, brain and heart tissues. An involvement of atherosclerotic plaques in depression etiology was not supported. Identified pathways could guide the development of new treatments to prevent depression-heightened atherosclerotic risk. Introduction Depression is a highly prevalent disorder – about 1 in 6 people are affected at some point of their lives 1 – and ranks amongst the top contributors to disability worldwide 2 . Its health burden is increased by high comorbidity with somatic disease 3 , such as an almost two-fold increase in risk of atherosclerotic diseases 4 . The comorbidity of depression and atherosclerotic disease may be promoted and maintained by numerous factors. Besides shared sociodemographic, behavioral and health-related (e.g. medication effects) determinants, biological pathways including inflammation and metabolic alterations could play an important role 5 . Aspects of this shared biology may represent promising targets for treatments to reduce the health impact of depression. Consistent with the hypothesis of shared biological pathways, the comorbidity of depression and atherosclerotic disease has a substantial genetic basis 6 . The two conditions also show overlap in the genetic factors that influence them, with significant genetic correlations found for major depression (MD) with coronary artery disease (CAD), as well as stroke 7 . In addition, there is evidence for a potential causal effect of depression genetic liability on atherosclerotic diseases (not vice versa) from Mend elian Randomization (MR) studies 8 , 9 . We can leverage such information on the shared genetic architecture to shed light on downstream biological pathways active in different tissues. Gene expression, the transcription of DNA to RNA, leads to cell differentiation and in consequence differs between tissues in health and disease. Tissue-specific effects of genetic loci can be assessed by analyzing their effects on gene expression; such genetic variants are known as expression quantitative loci (eQTL). For instance, risk loci for MD have been linked to expression in certain brain tissues, such as the prefrontal cortex, anterior cingulate cortex and hippocampus, as well as in blood and adrenal glands 10 . As an intermediary step in the link between genes and traits 11 , gene expression data provide the opportunity to link risk variants for the comorbidity of depression and atherosclerotic disease to the pathology of culprit tissues. Considering the emerging role of inflammation and metabolic alterations in depression, atherosclerotic plaque emerges as a tissue that could be proposed as a substantial source of biological dysregulations in depression pathophysiology. Atherosclerotic plaques consist of lipid deposits accompanied by an inflammatory response within the vascular wall 12 . Excess presence of atherosclerotic plaque is the hallmark of diseases like CAD and cerebrovascular disease, which can lead to stroke. Previous studies have not investigated whether altered gene expression in atherosclerotic plaques might be contributing to depression. In the present study, we maximized GWAS sample size by using summary statistics of the most recent meta-analyses to date for MD 10 , CAD 13 , and stroke (subtypes) 14 . We combined complementary evidence from the novel approaches of two-sample Mendelian Randomization and colocalization to reliably identify regions likely to contain shared causal variants. Furthermore, we tested whether the expression in various relevant tissues (brain regions, parts of the heart, and whole blood) of genes localized in shared loci was causally involved in depression etiology. Finally, leveraging unique eQTL data of atherosclerotic plaques from the Athero-Express Biobank Study 15 - we investigated the potential causal links between gene expression in atherosclerotic plaques and depression risk. Methods and Materials GWAS summary statistics We identified the largest meta-analysis of GWAS available to date of MD, a broad operationalization of depression combining different measures from clinical/interview, electronic health records, questionnaires, or self-report of diagnosis (N = 3,887,532), as well as eight atherosclerotic traits (stroke, ischemic stroke, small vessel disease, cardioembolic stroke, CAD, coronary artery calcification, carotid intima media thickness; N = 26,909 - 1,308,460), and obtained the respective summary statistics for European ancestry individuals ( Supplemental Table 1 ). To globally quantify the degree of genetic overlap between MD and the atherosclerotic traits we estimated single-nucleotide polymorphism (SNP) based genetic correlations using the related GWAS summary statistics and the high-definition likelihood (HDL) method 16 . Study design Figure 1 illustrates the study design based on a sequence of interdependent analyses, with results from Mendelian Randomization and colocalization informing the subsequent analysis of various eQTL datasets. All analyses were based on the summary statistics of the selected GWAS. Download figure Open in new tab Figure 1. Flow of analyses. SMR-HEIDI : summary data–based Mendelian randomization and heterogeneity in dependent instruments method, MD : major depression, eQTLGen/BrainMeta/GTEx/Athero-Express : studies with eQTL data used here. Causal inference through Mendelian Randomization We conducted Mendelian Randomization analyses using the R package TwoSampleMR (version 0.6.4) 17 . The instrumental variable consisted of several independent SNPs, selected from the exposure data by filtering and clumping (see Supplementary Methods ). Causal estimates were obtained using the inverse variance weighted (IVW) method, which combines the ratio estimates of individual SNPs whilst inversely weighting them by their standard error 18 . Estimates were deemed significant when p < 0.1 after FDR correction for multiple testing (Benjamini-Hochberg procedure) 19 . Standard MR sensitivity analyses, described in the Supplementary Methods, were conducted for those trait pairs with significant causal estimates. Only traits with statistically significant MR estimates were selected for the next analysis steps (see Figure 1 ). Colocalization Regions containing causal variants shared between MD and all atherosclerotic traits with a significant MR estimate were identified using Bayesian testing, as implemented by the coloc method 20 . First, regions of 1 megabase around the lead SNPs, selected for being significantly associated with MD 10 , were extracted from each of the GWAS datasets. The resulting pairs of summary statistics segments were analyzed using the R package coloc (version 5.2.3) 20 . Prior probabilities for SNP-trait associations were set to default (association with trait 1: p 1 = 1×10 -4 , association with trait 2: p 2 = 1×10 -4 , association with both traits: p 12 = 1×10 -5 ). Coloc compares the following five hypotheses: The selected region contains no causal variant (H0), a causal variant for one of the traits (H1 and H2), non-shared causal variants for both traits (H3), or one shared causal variant for both traits (H4). We considered a posterior probability for H4 > 0.8 indicative of colocalization. SNP-Lookup in gene-expression databases For each lead SNPs of a colocalizing region, we checked whether it was significantly associated with cross-tissue average expression of certain genes in the Genotype-Tissue Expression portal ( https://www.gtexportal.org ). We examined the eQTL’s tissue specificity based on its m-value 21 , a priori deeming whole blood, brain, heart, artery, and adipose tissue relevant for depression or atherosclerotic disease. Additionally, we investigated the cellular expression of the genes previously identified as eQTL of the selected genetic variant in atherosclerotic disease. To this end, we used PlaqView ( https://www.plaqview.com ) 22 which contains data from carotid atherosclerotic plaques from the Athero-Express Biobank Study (the Slenders et al. dataset 23 ). SMR-HEIDI for effect on MD The potential causal link of shared genomic regions with MD via gene expression was tested using summary data-based Mendelian randomization (SMR) and heterogeneity in dependent instruments (HEIDI) analysis 24 . SMR, a version of MR that was specifically developed to test the involvement of gene expression in disease mechanisms using eQTL data, is complemented by a HEIDI test, which rejects the hypothesis of shared causality or pleiotropy where linkage is a more likely explanation of an association. Here, we focused exclusively on regions of high likelihood for a shared causal variant between MD and atherosclerotic traits, as identified using colocalization analysis. We performed two main sets of SMR-HEIDI analyses across different tissues and eQTL datasets. 1. Blood, brain and heart eQTL in publicly available datasets First, we tested potential causal effects of gene expression in 20 different tissues on MD, by integrating the MD GWAS with publicly available eQTL data sets. These included eQTLGen (N = 31,684) for whole blood 25 , BrainMeta (N = 2,865) for brain tissue 26 , and GTEx for specific brain regions (N = 139 to 255), coronary artery (N = 240), aorta (N = 432), atrial appendage (N = 429) and left ventricle (N = 432) 27 , further details in Supplemental Table 2 . In the SMR-Portal 28 , p-value thresholds were set at 5.0×10 -8 for the lead eQTL SNP and 1.57×10 -3 for SNPs to be included in the HEIDI test. Estimates were deemed significant when p < 0.1 after FDR correction for multiple testing across eQTL-tissue pairs (Benjamini-Hochberg procedure) 19 . 2. Atherosclerotic plaques in the Athero-Express Biobank study The Athero-Express Biobank Study (AE) is an ongoing longitudinal biobank study including patients that undergo arterial endarterectomy in two Dutch tertiary referral centers since 2002 15 . For the present study, subsequent patients were included who underwent an endarterectomy of either the carotid or the femoral and iliac arteries and of which genotyping and transcriptomic data were available. Clinical data were extracted from patient medical files and standardized questionnaires. This study complies with the Declaration of Helsinki, and all participants provided informed consent. The medical ethical committees of the respective hospitals approved this study which was registered under number 22/018. Detailed description of the study procedures and generation of the eQTL datasets are provided in the Supplemental Methods . After quality control, eQTL data from 626 plaque samples were included. Analysis of the plaque tissue differed from the analysis of the other tissues in being tailored to a dataset with overall weak associations. For this, we used the downloaded SMR software (version 1.3.1) 24 , allowing instruments with eQTL p-value < 0.1 after FDR correction for SNPs across pre-selected regions. The same procedure was repeated using CAD, stroke, and CIMT as outcomes, to better understand the role of eQTL in plaque pathology and disease risk. Results We included GWAS summary statistics for MD and eight atherosclerotic traits ( Supplemental Table 1 ). To explore whether these traits share common genetic pathways, suggestive of a potential causal relation, we calculated genetic correlations. Small genetic correlations ( Supplemental Table 3 ) were detected of MD with any type of stroke (r=0.19, se=0.03, p=1.2×10 -12 ), ischemic stroke (r=0.2, se=0.03, p=4.71×10 -13 ), small vessel disease (r=0.24, se=0.05, p=7.4×10 -6 ) and CAD (r=0.23, se=0.02, p=6.0×10 -32 ). Genetic liability for major depression causally affects (ischemic) stroke and CAD Next, we tested for a potential causal effect of MD liability on atherosclerotic traits, and vice versa. The strength of the MR instrumental variable, i.e. the effect of selected SNPs on the exposure, was found to be of adequate strength across traits (F > 10, see Supplemental Table 5 ). Figure 2 shows that the genetic liability of MD significantly associated with an increased risk of any type of stroke (OR=1.15, 95%CIs=1.09-1.21, p=9.47×10 -8 ), ischemic stroke (OR=1.16, 95%CIs=1.10-1.22, p=1.52×10 -7 ), small vessel disease (OR=1.34, 95%CIs=1.16-1.55, p=4.76×10 -5 ), and CAD (OR=1.2, 95%CIs=1.13-1.26, p=3.76×10 -22 ), indicating a causal effect of MD on these diseases. The effects of MD genetic liability on cardioembolic stroke, large artery stroke, coronary artery calcification, and carotid intima media thickness, were not statistically significant. Causal estimates were robust, with the weighted median and MR Egger estimators showing the same direction and magnitude of effect as the IVW method. Download figure Open in new tab Figure 2. MR Analysis of Causal Effects between MD and Atherosclerosis. Forest plot displaying causal estimates (betas) from the IVW method. Error bars represent standard errors. * Indicates p-value significant at False Discovery Rate < 0.1. N SNPs : number of SNPs used as instruments for the exposure. OR : Odds ratio per doubling (2-fold increase) in the prevalence of the exposure (only for dichotomous outcomes). ALLSTROKE : any stroke, IS : ischemic stroke, CES : cardioembolic strokes, LAS : large artery stroke, SVD : small vessel disease, CAD : coronary artery disease, CAC : coronary artery calcification, CIMT : carotid intima-media thickness, DEP : major depression. n . a .: not applicable. Note that SVD was not included as an exposure, as no valid instruments were identified (N SNPs = 0). For full results refer to Supplemental Table 4 and 9. For the results with significant effects – outcomes of stroke, ischemic stroke, small vessel disease, CAD – several sensitivity analyses were considered to assess the extent to which MR assumptions may have been violated ( Supplemental Tables 6-8 ). Across traits, the Egger intercept did not indicate horizontal pleiotropy (not significantly different from 0), but Cochran’s Q statistic suggested instruments’ effects were heterogeneous (Q > 0, p < 0.05). MR PRESSO, which investigates pleiotropy by correcting for outlier SNPs, indicated horizontal pleiotropy for CAD (effect no longer significant after outlier detection and removal), but not for stroke and its subtypes. In reversed MR analyses, the genetic liability of atherosclerotic diseases, as well as greater genetically predicted CAC and CIMT, were not significantly associated with increased risk of MD (see Figure 2 ), thus providing no evidence for a causal effect of atherosclerotic disease on depression. Major depression and atherosclerotic diseases share causal genetic loci Given the expectation that traits with causal relationships found in MR are more likely to share causal genes, (ischemic) stroke, small vessel disease, and CAD were submitted to colocalization analysis with MD. A high probability of colocalization, with PP.H4 > 0.8, was found for MD and stroke at 2 lead SNPs, ischemic stroke at 1 lead SNPs and CAD at 7 lead SNPs (see Supplemental Table 10 and Supplemental Figures 1-10 ). At an intron variant of the gene TMEM106B on chromoso me 7, rs10950392, there was a signal for colocalization of MD and all three atherosclerotic traits, see Figure 3 . In addition, the region around this SNP showed the overall greatest probability of colocalization (PP.H4 = 99.82% for MD and CAD). No shared loci for MD and small vessel disease were found. Overall, these results provide evidence that regions surrounding eight unique SNPs are linked to potentially mechanistic pathways shared between depression and atherosclerotic disease. Download figure Open in new tab Figure 3. Mirror plots for top results of colocalization analysis. Three mirror plots of the region around lead SNP rs10950392. Panels from top to bottom: SNP associations with MD, SNP associations with the respective atherosclerotic disease, basic gene annotation. Plots were generated using the R package RACER ( https://github.com/oliviasabik/RACER ) 47 . ALLSTROKE: any stroke ; IS : ischemic stroke; CAD : coronary artery disease; PP . H4 : posterior probability of coloc hypothesis for, i.e. the region containing a variant causal for both traits; LD_BIN : levels of linkage disequilibrium with rs10950392. Shared causal loci are associated with gene expression GTEx lookup of lead SNPs found associations with the expression of twelve genes (see Supplemental Figures 11-22 ). For instance, the recurring lead SNP rs10950392, was found to be associated with expression TMEM106B across tiss ues, with highly tissue-specific expression relevant to depression and/or atherosclerosis, for instance in whole blood, left ventricle, atrial appendage and multiple brain regions. Shared causal loci act on major depression via tissue-specific gene expression To investigate whether gene expression is involved in the pathway between shared genetic regions and MD, we conducted SMR analyses of eQTL in various tissues. The SMR analysis identified potential causal effects of gene expression on MD in all tested tissues, except for coronary artery and substantia nigra, see Figure 4 . The greatest number of significant estimates was found for expression in blood, with seven genes ( ZFAND2A, TTYH3, PIP5K1C, GNA12, C7orf50, ARRDC5, ARL4A ) also passing the HEIDI test for linkage. In meta-analyzed data from all brain regions, expression of five genes ( RASGRP1, HDGFL2, GPR85, CHST12, AMZ1 ) showed significant causal effects on MD. The expression of one gene, MRM2 , showed significant causal effects on MD in all included GTEx tissues, with most of them also passing the HEIDI test. Notably, expression of ZFAND2A and GNA12 in both blood and the heart (atrial appendage and left ventricle) had a causal effect on MD. Similarly, the effect of PIP5K1C was relevant across tissue domains, in whole blood, cerebellum and aorta. Besides, there was a positive relationship between number of significant estimates and sample size of eQTL datasets per tissue ( Supplemental Table 2 ). Download figure Open in new tab Figure 4. Significant causal estimates of gene expression in various tissues affecting MD. Plot mapping genes with significant causal estimates at FDR 0.05, signifying that null hypothesis of a shared causal variant was not rejected. FDR : False discovery rate. Gene expression in atherosclerotic plaques was not linked to major depression Finally, we investigated whether gene expression specific to atherosclerotic plaque tissue is causally linked to MD, by conducting SMR on eQTL data from 626 plaque samples. Samples stem from persons undergoing carotid endarterectomy, a cohort of older adults with high levels of disease and corresponding risk factors such as high levels of smoking (see Table 1 ) that is representative of the Athero-Express. View this table: View inline View popup Download powerpoint Table 1. Baseline characteristics of the Athero-Express patients included in the eQTL analyses. Characteristics are given as n (%) or mean (standard deviation); eGFR : estimated glomerular filtration based on Modification of Diet in Renal Disease (MDRD) formula 48 . KDOQI : the Kidney Disease Outcomes Quality Initiative (KDOQI) of chronic kidney disease (CKD) classification based on estimated glomerular filtration rate using the MDRD formula 49 ; BMI: body mass index in kg/m 3 ; BMI ( WHO ): BMI categories according to the World Health Organization 50 ; Diabetes : diabetes status based on questionnaire, diagnosis by a medical doctor, or based on diabetic medication; Hypertension : hypertension status based on questionnaire, diagnosis by a medical doctor, or based on hypertensive medication ( antihypertensives ); Antiplatelets : usage of aspirin, dipyridamole and/or ADP inhibitor; Lipid-lowering drugs include statins, fibrates and other lipid-lowering medication; Symptoms prior to surgery; Restenosis indicates whether the CEA was de novo, or after a restenosis. SMR analyses in the plaque were hindered by weak SNP-to-gene associations, leading to inclusion of only 15 genes (see Figure 5 ), with the majority not having enough SNPs available to conduct HEIDI. For these 15 genes, no significant SMR estimates were found at FDR < 0.1 (see Supplemental Table 11 ). Figure 5 shows one nominally significant estimate (beta=0.056, SE=0.024, p=0.013) for the gene ZFAND2A , which passes the HEIDI test (15 SNPs, p=0.87), indicating that the association is unlikely to be explained by linkage. Download figure Open in new tab Figure 5. Causal estimates of gene expression in atherosclerotic plaques affecting MD. SMR results for all genes included in the analysis of atherosclerotic plaques. Z-score : SMR beta divided by standard error; no adjustment for multiple testing. For full results refer to Supplemental Table 11. Given weak eQTL for the atherosclerotic plaque, we conducted SMR analyses of the effect of gene expression on CIMT, stroke and CAD to provide a positive control. This analysis yielded no nominally significant estimates (see Supplemental Tables 12-14 ), indicating that plaque eQTL data may not reflect genetic propensity for atherosclerosis, thus reducing the chance of finding significant effects on MD. Discussion We examined which genetic risk factors are shared by major depression and atherosclerotic disease, and whether these genetic risk factors link to altered gene expression in various tissues, including atherosclerotic plaques. We found a causal connection between genetic liability for major depression and (ischemic) stroke, small vessel disease, and coronary artery disease, traceable to eight specific genomic regions. Altered expression of shared genetic factors was found to link to major depression predominantly in whole blood, brain, and heart, with limited evidence in atherosclerotic plaques. First, using results from the latest GWAS, we showed that increased MD genetic liability was linked to a higher risk of developing (ischemic) stroke, small vessel disease, and CAD, but not vice versa. This causal direction, from depression to atherosclerotic disease, is in line with previous MR research 8 , 9 . Earlier studies in smaller GWAS (<500,000 for atherosclerotic diseases), found significant effects of depression genetic liability on CAD and small vessel disease, a pattern that our results from larger GWAS extend to (ischemic) stroke in general. MR estimates suggest causal effects only in the absence of horizontal pleiotropy – SNPs having independent effects on two traits rather than affecting the outcome via the exposure. Here, follow-up analyses did not show evidence for horizontal pleiotropy for stroke – in contrast to CAD. This can likely be explained by the fact that CAD is a particularly broad and polygenic trait connected to multiple causal pathways. An effect of depression on CAD remains plausible in the context of results from other study designs, for example increased risk of CAD following depression has also been indicated by numerous prospective cohort studies 29 . Next, we investigated causal relationships on the level of genomic regions. A high probability for containing a shared causal variant for MD and (ischemic) stroke or CAD was found for eight regions, with many of them containing protein-coding genes with known functions potentially relevant to etiology and pathogenesis of depression and/or atherosclerosis. For example, the gene TMEM106B , located in a region on chromosome that stood out for linking MD with multiple atherosclerotic traits, has been linked to central nervous system inflammation 30 . Accordingly, its association with neurodegenerative diseases may arise via regulation of microglia 31 . On the cellular level, TMEM106B is critical for the functioning of lysosomes 32 , a key player in plaque formation 33 . Whether lysosome activity may also be linked to depression is an open question, and the possibility of pleiotropy in the pathways remains. Besides providing some fine-mapping information, colocalization was used to prioritize shared genes for depression and atherosclerotic disease that were promising candidates for downstream gene expression analyses. Based on this, we examined whether shared genetic vulnerability was related to gene expression in various tissues potentially influencing MD etiology. Expression – in blood, brain, heart, and assorted other tissues – of 28 genes from the shared causal regions with atherosclerosis, was indeed found to be potentially causal for depression. SMR analysis identified more than 20 links of gene expression with MD, spread across all investigated tissues (blood, brain, heart, artery, adipose). Some of these genes are known to have functions related to plausible shared mechanisms of MD and atherosclerotic diseases, such as immunometabolic processes like inflammation, lipid metabolism, and mitochondrial function: For instance, ZFAND2A – with expression in atherosclerotic plaque, as well as whole blood, adipose tissue, atrial appendage, and left ventricle implicated here – is a zinc finger gene, a family of genes that has been found associated with metabolic dysregulation, in particular with glucose metabolism 34 . Whilst we might speculate about a link to inflammatory traits such as immunometabolic depression and atherosclerosis, further research would be required to elucidate the role of ZFAND2A in human health. A gene more directly linked to immune processes is RASGRP2 , whose expression in brain tissue we found to be potentially causal for depression. RASGRP2 is highly expressed in immune cells 35 – specifically T cells 36 – and linked to diabetes 37 . Relatedly, GNA12 , here relevant to depression when expressed in whole blood or heart tissue, appears to be involved in regulation of pro-inflammatory cytokines IL-6 and IL-8 38 . Lipid metabolism in particular is relevant to both depression and atherosclerotic diseases. In relation to this, PLIN3 has been investigated for its role in macrophage lipid accumulation 33 , a contributor to atherosclerosis. Generally, PLIN3 is involved in formation and function of lipid droplets 33 , organelles found abundantly in adipose tissues. Thus, the effect of PLIN3 expression in visceral adipose tissues on depression found here may hint that the precise processes of lipid storage in adipose tissue should be considered relevant to the comorbidity. Alterations in GPR146 expression, coding for G-protein coupled receptor 146, are linked to cholesterol levels. Specifically, deficiency of GPR146 leads to reduced LDL cholesterol and protection against hypercholesterolemia and atherosclerosis 39 . However, the current study shows an effect of GPR146 in the cerebellum, a connection that has not been investigated before. The gene MRM2 , which in the present results stands out for significant causal estimates of its expression in most tissues, is connected to mitochondrial function 40 and specifically altered in atherosclerosis 41 . Mitochondrial dysfunction seems to play a role in psychiatric disorders 41 , 42 , although details remain to be clarified. To summarize, specific clues about the shared biology of depression and atherosclerosis can be derived from our results. Given the indirect nature of our genomics-based search, these functional descriptions must be interpreted as preliminary directions for future research rather than definite mechanistic explanations. Finally, we were specifically interested in whether the shared genetic vulnerability acts through gene expression in the atherosclerotic plaque. However, we did not find strong evidence for alteration in gene expression in atherosclerotic plaque as a potential causal mechanism in depression. In the analysis of atherosclerotic plaque eQTL we found little evidence for a causal effect on MD, with nominally significant (p < 0.05) estimates for expression of only one gene, ZFAND2A , and no significant estimates after adjusting for multiple testing. While evidence for involvement of ZFAND2A converges with our results from other tissues, in particular whole blood and heart, replication of this result is needed to reduce the likelihood of a false positive. To further characterize the plaque eQTL, we tested their effect on traits known to be based on the occurrence of atherosclerotic plaque: CIMT, stroke, CAD. The results of this analysis were negative, indicating no causal effect of plaque eQTL on atherosclerotic disease. However, eQTL analysis provides an incomplete picture of the role of plaques in disease and comorbidity. GWAS-eQTL links are generally less abundant than expected 43 , and heterogeneity between the GWAS and plaque eQTL samples, in terms of age, health, and other covariates, may reduce a possible association. Further insights could be gained by analyzing additional omics layers, for instance proteomics. Alternatively, the sparse findings may be due to the analysis being underpowered for the plaque samples. Whilst a comparison to the sample sizes for tissues with significant causal estimates does not suggest a substantial contrast, power may nevertheless be lower if effect sizes in the eQTL are smaller in plaque tissue. Similarly, standard errors in plaque eQTL can be relatively large, given that despite the homogenous cohort – patients with clinical symptoms at an advanced stage of disease – plaque samples are heterogenous in nature. The current study benefits from several factors. In selecting GWAS we relied on the largest available meta-analyses for all traits, yielding the most robust results. Additionally, we were, for the first time, able to investigate eQTL in the culprit tissue for atherosclerotic diseases with regard to depression. MR and colocalization complement each other – whilst relying on distinct methodologies, both examine the shared causal basis of two traits – thereby providing a layer of triangulation that enhances robustness of the study 44 . Nevertheless, the current study is subject to several limitations. All selected GWAS meta-analysis samples were limited to European ancestry, reducing generalizability to other ancestries. Additionally, heterogeneity in the GWAS phenotypes may have diluted relevant signals, especially for MD. The MD GWAS meta-analysis is based on a broad phenotype ranging from clinical diagnosis to simple self-report questions 10 , likely capturing less specific genetic architecture 45 . Relatedly, the clinical heterogeneity of major depressive disorder, also reflected at the genetic level 46 , may obscure the applicability of findings that are potentially driven by a biologically distinct subgroup of patients exhibiting immuno-metabolic alterations. Conclusion This study indicates that part of the connection between depression and atherosclerotic disease stems from a shared genetic root. The genetic liability for major depression encompasses mechanisms likely causal for the development of coronary artery disease and stroke. The study pinpointed eight regions in the genome harboring potentially causal variants shared by depression with coronary artery disease and stroke. In these shared genomic regions, pathways acting through the expression of 28 genes involved in immuno-metabolic pathways in blood, brain and heart are likely involved in the pathophysiology of depression. Less consistent evidence was found for similar mechanisms acting in atherosclerotic plaques. Overall, the present study identified specific shared biological pathways potentially underlying the established risk of atherosclerotic disease in depression. For clinical practice, evidence of shared biological mechanisms suggests the relevance of carefully monitoring cardiometabolic health and atherosclerotic risk in subjects with depression. Furthermore, pathways identified in the present study could guide the development of new treatments to prevent depression and its heightened atherosclerotic risk. Data Availability The data, analytic methods, and study materials are available to other researchers. The Athero-Express Biobank Study data are available through DataverseNL; the other datasets are available through the respective sources as reported in Supplemental Table 1 and 2. The main scripts used for the quality control and the (meta-)analysis of the data are available through GitHub. https://doi.org/10.34894/4IKE3T https://github.com/CirculatoryHealth/MR_CVD_MDD Disclosures Funding sources EP, BWJHP, and MB are supported by Stress in Action (NWO gravitation grant number 024.005.010). BWJH is also supported by the EU H2020 TO_AITION (grant number: 848146). MB is also supported by an NWO VICI grant (grant number VI.C.211.0540). SWvdL is funded through EU H2020 TO_AITION (grant number: 848146), EU HORIZON NextGen (grant number: 101136962), EU HORIZON MIRACLE (grant number: 101115381), and Health∼Holland PPP Allowance ‘Getting the Perfect Image’. YM is supported by EU H2020 TO_AITION (grant number: 848146), Amsterdam UMC (Starter Grant Ronde 2), Amsterdam Neuroscience (PoC funding 2024-2026) and the ImmunoMIND consortium, funded by UK Research & Innovation as part of the UK national Mental Health Platform. Conflict of interest SWvdL and GP have received Roche funding for unrelated work. Roche had no part in this study, neither in the conception, design and execution of this study, nor in the preparation and contents of this manuscript. Data and code availability The data, analytic methods, and study materials are available to other researchers. The Athero-Express Biobank Study data are available through DataverseNL ( https://doi.org/10.34894/4IKE3T ); the other datasets are available through the respective sources as reported in Supplemental Table 1 and 2 . The main scripts used for the quality control and the (meta-)analysis of the data are available through GitHub ( https://github.com/CirculatoryHealth/MR_CVD_MDD ). Acknowledgements We are thankful for the support of the Leducq Foundation ‘PlaqOmics’ and ‘AtheroGen’, and the Chan Zuckerberg Initiative ‘MetaPlaq’. The collaborative project ‘Getting the Perfect Image’ was co-financed through use of PPP Allowance awarded by Health∼Holland, Top Sector Life Sciences & Health, to stimulate public-private partnerships. Claudia Tersteeg, Krista den Ouden, Mirjam B. Smeets, and Loes B. Collé are graciously acknowledged for their work on the DNA extraction. Astrid E.M.W. Willems, Evelyn Velema, Kristy M. J. Vons, Sara Bregman, Timo R. ten Brinke, Sara van Laar, Louise M. Catanzariti, Joyce E.P. Vrijenhoek, Sander M. van de Weg, Arjan H. Schoneveld, Arnold Koekman, Arjan Boltjes, Petra H. Homoed-van der Kraak, and Aryan Vink are graciously acknowledged for their past and continuing work on the Athero-Express Biobank Study. We would also like to thank all the (former) employees involved in the Athero-Express Biobank Study of the Departments of Surgery of the St. Antonius Hospital Nieuwegein and University Medical Center Utrecht for their continuing work. Lastly, we would like to thank all participants of the Athero-Express Biobank Study; without you these kinds of studies would not be possible. We would also like to thank research participants and employees of 23andMe, Inc. for making this work possible. Footnotes Author list and author afficilations updated; Adjusted subject area of the submission (from Psychiatry to Genetic Medicine) References 1. ↵ Marx , W. et al. Major depressive disorder . Nat. Rev. Dis. Primers 9 , 44 ( 2023 ). OpenUrl PubMed 2. ↵ GBD 2021 Diseases and Injuries Collaborators . Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021 . Lancet 403 , 2133 – 2161 ( 2024 ). OpenUrl CrossRef PubMed 3. ↵ Berk , M. et al. Comorbidity between major depressive disorder and physical diseases: a comprehensive review of epidemiology, mechanisms and management . World Psychiatry 22 , 366 – 387 ( 2023 ). OpenUrl CrossRef PubMed 4. ↵ Wium-Andersen , M. K. et al. An attempt to explain the bidirectional association between ischaemic heart disease, stroke and depression: a cohort and meta-analytic approach . Br. J. Psychiatry 217 , 434 – 441 ( 2020 ). OpenUrl CrossRef PubMed 5. ↵ Gold , S. M. et al. Comorbid depression in medical diseases . Nat. Rev. Dis. Primers 6 , 69 ( 2020 ). OpenUrl PubMed 6. ↵ Meijsen , J. et al. Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians . Nat. Commun . 15 , 5064 ( 2024 ). OpenUrl CrossRef PubMed 7. ↵ Bergstedt , J. et al. Distinct biological signature and modifiable risk factors underlie the comorbidity between major depressive disorder and cardiovascular disease . Nat. Cardiovasc. Res . 3 , 754 – 769 ( 2024 ). OpenUrl PubMed 8. ↵ Li , G. H.-Y. et al. Evaluation of bi-directional causal association between depression and cardiovascular diseases: a Mendelian randomization study . Psychol. Med . 52 , 1765 – 1776 ( 2022 ). OpenUrl CrossRef PubMed 9. ↵ Lu , Y. , Wang , Z. , Georgakis , M. K. , Lin , H. & Zheng , L. Genetic liability to depression and risk of coronary artery disease, myocardial infarction, and other cardiovascular outcomes . J. Am. Heart Assoc . 10 , e017986 ( 2021 ). OpenUrl CrossRef PubMed 10. ↵ Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium . Electronic address: andrew.mcintosh{at}ed.ac.uk & Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies . Cell 188 , 640 - 652 .e9 ( 2025 ). OpenUrl CrossRef PubMed 11. ↵ Porcu , E. et al. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits . Nat. Commun . 10 , 3300 ( 2019 ). OpenUrl CrossRef PubMed 12. ↵ Lusis , A. J. Atherosclerosis . Nature 407 , 233 – 241 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 13. ↵ Aragam , K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants . Nat. Genet . 54 , 1803 – 1815 ( 2022 ). OpenUrl CrossRef PubMed 14. ↵ Mishra , A. et al. Stroke genetics informs drug discovery and risk prediction across ancestries . Nature 611 , 115 – 123 ( 2022 ). OpenUrl CrossRef PubMed 15. ↵ Verhoeven , B. A. N. et al. Athero-express: Differential atherosclerotic plaque expression of mRNA and protein in relation to cardiovascular events and patient characteristics. Rationale and design . Eur. J. Epidemiol . 19 , 1127 – 1133 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 16. ↵ Ning , Z. , Pawitan , Y. & Shen , X. High-definition likelihood inference of genetic correlations across human complex traits . Nat. Genet . ( 2020 ) doi: 10.1038/s41588-020-0653-y . OpenUrl CrossRef PubMed 17. ↵ Hemani , G. et al. The MR-Base platform supports systematic causal inference across the human phenome . Elife 7 , ( 2018 ). 18. ↵ Burgess , S. , Butterworth , A. & Thompson , S. G. Mendelian randomization analysis with multiple genetic variants using summarized data . Genet. Epidemiol . 37 , 658 – 665 ( 2013 ). OpenUrl CrossRef PubMed 19. ↵ van den Oord , E.J.C.G. & Sullivan , P. F. False discoveries and models for gene discovery ., Trends Genet . 19 , 537 – 542 ( 2003 ). OpenUrl CrossRef PubMed Web of Science 20. ↵ Giambartolomei , C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics . PLoS Genet . 10 , e1004383 ( 2014 ). OpenUrl CrossRef PubMed 21. ↵ GTEx Consortium et al. Genetic effects on gene expression across human tissues . Nature 550 , 204 – 213 ( 2017 ). OpenUrl CrossRef PubMed Web of Science 22. ↵ Ma , W. F. et al. PlaqView 2.0: A comprehensive web portal for cardiovascular single-cell genomics . Front. Cardiovasc. Med . 9 , 969421 ( 2022 ). OpenUrl PubMed 23. ↵ Depuydt , M. A. C. et al. Microanatomy of the Human Atherosclerotic Plaque by Single-Cell Transcriptomics . Circ. Res . 127 , 1437 – 1455 ( 2020 ). OpenUrl CrossRef PubMed 24. ↵ Zhu , Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets . Nat. Genet . 48 , 481 – 487 ( 2016 ). OpenUrl CrossRef PubMed 25. ↵ Võsa , U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression . Nat. Genet . 53 , 1300 – 1310 ( 2021 ). OpenUrl CrossRef PubMed 26. ↵ Qi , T. et al. Genetic control of RNA splicing and its distinct role in complex trait variation . Nat. Genet . 54 , 1355 – 1363 ( 2022 ). OpenUrl CrossRef PubMed 27. ↵ GTEx Consortium . The GTEx Consortium atlas of genetic regulatory effects across human tissues . Science 369 , 1318 – 1330 ( 2020 ). OpenUrl Abstract / FREE Full Text 28. ↵ Guo , Y. et al. SMR-Portal: an online platform for integrative analysis of GWAS and xQTL data to identify complex trait genes . Nat. Methods 22 , 220 – 222 ( 2025 ). OpenUrl PubMed 29. ↵ Gan , Y. et al. Depression and the risk of coronary heart disease: a meta-analysis of prospective cohort studies . BMC Psychiatry 14 , 371 ( 2014 ). OpenUrl CrossRef PubMed 30. ↵ Milind , N. et al. Transcriptomic stratification of late-onset Alzheimer’s cases reveals novel genetic modifiers of disease pathology . PLoS Genet . 16 , e1008775 ( 2020 ). OpenUrl CrossRef PubMed 31. ↵ Gilchrist , L. et al. Depression symptom-specific genetic associations in clinically diagnosed and proxy case Alzheimer’s disease . Nat. Ment. Health 3 , 212 – 228 ( 2025 ). OpenUrl 32. ↵ Brady , O. A. , Zheng , Y. , Murphy , K. , Huang , M. & Hu , F. The frontotemporal lobar degeneration risk factor, TMEM106B, regulates lysosomal morphology and function . Hum. Mol. Genet . 22 , 685 – 695 ( 2013 ). OpenUrl CrossRef PubMed 33. ↵ Marques , A. R. A. , Ramos , C. , Machado-Oliveira , G. & Vieira , O. V. Lysosome (dys)function in atherosclerosis-A big weight on the shoulders of a small organelle . Front. Cell Dev. Biol . 9 , 658995 ( 2021 ). OpenUrl PubMed 34. ↵ Buchner , D. A. et al. Zinc finger protein 407 (ZFP407) regulates insulin-stimulated glucose uptake and glucose transporter 4 (Glut4) mRNA . J. Biol. Chem . 290 , 6376 – 6386 ( 2015 ). OpenUrl Abstract / FREE Full Text 35. ↵ Wang , C. et al. RasGRP1 promotes the acute inflammatory response and restricts inflammation-associated cancer cell growth . Nat. Commun . 13 , 7001 ( 2022 ). OpenUrl CrossRef PubMed 36. ↵ Priatel , J. J. , Teh , S.-J. , Dower , N. A. , Stone , J. C. & Teh , H.-S. RasGRP1 transduces low-grade TCR signals which are critical for T cell development, homeostasis, and differentiation . Immunity 17 , 617 – 627 ( 2002 ). OpenUrl CrossRef PubMed Web of Science 37. ↵ Taneera , J. et al. A systems genetics approach identifies genes and pathways for type 2 diabetes in human islets . Cell Metab . 16 , 122 – 134 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 38. ↵ Jian , S.-L. et al. Gα 12 drives invasion of oral squamous cell carcinoma through up-regulation of proinflammatory cytokines . PLoS One 8 , e66133 ( 2013 ). OpenUrl CrossRef PubMed 39. ↵ Yu , H. et al. GPR146 deficiency protects against hypercholesterolemia and atherosclerosis . Cell 179 , 1276 - 1288 .e14 ( 2019 ). OpenUrl CrossRef PubMed 40. ↵ Rebelo-Guiomar , P. et al. A late-stage assembly checkpoint of the human mitochondrial ribosome large subunit . Nat. Commun . 13 , 929 ( 2022 ). OpenUrl CrossRef PubMed 41. ↵ Qu , K. et al. Mitochondrial dysfunction in vascular endothelial cells and its role in atherosclerosis . Front. Physiol . 13 , 1084604 ( 2022 ). OpenUrl PubMed 42. ↵ Morris , G. & Berk , M. The many roads to mitochondrial dysfunction in neuroimmune and neuropsychiatric disorders . BMC Med . 13 , 68 ( 2015 ). OpenUrl CrossRef PubMed 43. ↵ Connally , N. J. et al. The missing link between genetic association and regulatory function . Elife 11 , ( 2022 ). 44. ↵ Seldenrijk , A. et al. Depressive and anxiety disorders and risk of subclinical atherosclerosis Findings from the Netherlands Study of Depression and Anxiety (NESDA) . J. Psychosom. Res . 69 , 203 – 210 ( 2010 ). OpenUrl CrossRef PubMed 45. ↵ Cai , N. et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression . Nat. Genet . 52 , 437 – 447 ( 2020 ). OpenUrl CrossRef PubMed 46. ↵ Milaneschi , Y. et al. Genetic association of major depression with atypical features and obesity-related immunometabolic dysregulations . JAMA Psychiatry 74 , 1214 – 1225 ( 2017 ). OpenUrl PubMed 47. ↵ Sabik , O. L. & Farber , C. R. RACER: A data visualization strategy for exploring multiple genetic associations . bioRxiv 495366 ( 2018 ) doi: 10.1101/495366 . OpenUrl Abstract / FREE Full Text 48. ↵ Tarwater , K. Estimated glomerular filtration rate explained . Mo. Med . 108 , 29 – 32 ( 2011 ). OpenUrl PubMed 49. ↵ Hallan , S. I. & Orth , S. R. The KDOQI 2002 classification of chronic kidney disease: for whom the bell tolls . Nephrol. Dial. Transplant 25 , 2832 – 2836 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 50. ↵ Weir , C. B. & Jan , A. BMI classification percentile and Cut Off points . in StatPearls ( StatPearls Publishing , Treasure Island (FL ), 2025 ). View the discussion thread. Back to top Previous Next Posted December 01, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques Emma Pruin , Meike Bartels , Ernest Diez Benavente , Noortje A.M. van den Dungen , Joost K.R. Hoekstra , Dominique D.P. de Kleijn , Lennart P.L. Landsmeer , Michal Mokry , Gerard Pasterkamp , Brenda W.J.H. Penninx , Wouter J. Peyrot , Hester M. den Ruijter , Sander W. van der Laan , Yuri Milaneschi medRxiv 2025.07.11.25331336; doi: https://doi.org/10.1101/2025.07.11.25331336 Share This Article: Copy Citation Tools Major depression and atherosclerotic disease: Linking shared genetics to pathways in blood, brain, heart, and atherosclerotic plaques Emma Pruin , Meike Bartels , Ernest Diez Benavente , Noortje A.M. van den Dungen , Joost K.R. Hoekstra , Dominique D.P. de Kleijn , Lennart P.L. Landsmeer , Michal Mokry , Gerard Pasterkamp , Brenda W.J.H. Penninx , Wouter J. Peyrot , Hester M. den Ruijter , Sander W. van der Laan , Yuri Milaneschi medRxiv 2025.07.11.25331336; doi: https://doi.org/10.1101/2025.07.11.25331336 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4411) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6574) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1263) Infectious Diseases (except HIV/AIDS) (15903) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (665) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (967) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5421) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe91db53d7406d3',t:'MTc3OTI1Njc3Mw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();