Genetic analysis of retinal cell types reveals synaptic pathology in schizophrenia

Genetic analysis of retinal cell types reveals synaptic pathology in schizophrenia | bioRxiv /* */ /* */ <!-- <!-- /*! * 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-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Genetic analysis of retinal cell types reveals synaptic pathology in schizophrenia View ORCID Profile Emanuel Boudriot , View ORCID Profile Marius Stephan , View ORCID Profile Finn Rabe , View ORCID Profile Lukasz Smigielski , View ORCID Profile Andrea Schmitt , View ORCID Profile Peter Falkai , Michael J. Ziller , Moritz J. Rossner , View ORCID Profile Philipp Homan , View ORCID Profile Sergi Papiol , View ORCID Profile Florian J. Raabe doi: https://doi.org/10.1101/2024.08.09.607343 Emanuel Boudriot 1 Max Planck Institute of Psychiatry , 80804 Munich, Germany 2 Department of Psychiatry and Psychotherapy, LMU University Hospital , LMU Munich, 80336 Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Emanuel Boudriot Marius Stephan 2 Department of Psychiatry and Psychotherapy, LMU University Hospital , LMU Munich, 80336 Munich, Germany 3 Systasy Bioscience GmbH , 81669 Munich, Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marius Stephan Finn Rabe 4 Department of Adult Psychiatry and Psychotherapy, University of Zurich , 8032 Zurich, Switzerland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Finn Rabe Lukasz Smigielski 5 Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich , 8032 Zurich, Switzerland PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lukasz Smigielski Andrea Schmitt 1 Max Planck Institute of Psychiatry , 80804 Munich, Germany 2 Department of Psychiatry and Psychotherapy, LMU University Hospital , LMU Munich, 80336 Munich, Germany 6 6German Center for Mental Health (DZPG), partner site Munich-Augsburg 7 Laboratory of Neurosciences (LIM-27), Institute of Psychiatry, University of São Paulo (USP) , São Paulo-SP 05403-903, Brazil MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrea Schmitt Peter Falkai 1 Max Planck Institute of Psychiatry , 80804 Munich, Germany 2 Department of Psychiatry and Psychotherapy, LMU University Hospital , LMU Munich, 80336 Munich, Germany 6 6German Center for Mental Health (DZPG), partner site Munich-Augsburg MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Peter Falkai Michael J. Ziller 1 Max Planck Institute of Psychiatry , 80804 Munich, Germany 8 Department of Psychiatry, University of Münster , 48149 Münster, Germany 9 Center for Soft Nanoscience, University of Münster , 48149 Münster, Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Moritz J. Rossner 2 Department of Psychiatry and Psychotherapy, LMU University Hospital , LMU Munich, 80336 Munich, Germany 3 Systasy Bioscience GmbH , 81669 Munich, Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Philipp Homan 4 Department of Adult Psychiatry and Psychotherapy, University of Zurich , 8032 Zurich, Switzerland 10 Neuroscience Center Zurich, University of Zurich and ETH Zurich , 8057 Zurich, Switzerland MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philipp Homan Sergi Papiol 1 Max Planck Institute of Psychiatry , 80804 Munich, Germany 11 Institute of Psychiatric Phenomics and Genomics (IPPG) , LMU Munich, 80336 Munich, Germany PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sergi Papiol Florian J. Raabe 1 Max Planck Institute of Psychiatry , 80804 Munich, Germany 2 Department of Psychiatry and Psychotherapy, LMU University Hospital , LMU Munich, 80336 Munich, Germany MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Florian J. Raabe For correspondence: florian_raabe{at}psych.mpg.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Importance As an accessible part of the central nervous system, the retina provides a unique window to study pathophysiological mechanisms of brain disorders in humans. Imaging and electrophysiological studies have revealed retinal alterations across several neuropsychiatric and neurological disorders. However, it remains largely unclear whether primary disease mechanisms within the retina contribute to the observed retinal alterations and which specific retinal cell types and biological mechanisms are involved. Objective To determine whether specific retinal cell types are affected by genomic risk for neuropsychiatric and neurological disorders and to explore the mechanisms through which genomic risk converges in these cell types. Design, Setting, and Participants In this study, we combined findings from genome-wide association studies in schizophrenia, bipolar disorder, major depressive disorder, multiple sclerosis, Parkinson disease, Alzheimer disease, and stroke with retinal single-cell transcriptomic data sets from humans, macaques, and mice. To identify susceptible cell types, we applied MAGMA cell type enrichment analyses and performed subsequent pathway analyses. Furthermore, we translated the cellular top hit to the structural level by using retinal optical coherence tomography and genotyping data in the large population-based UK Biobank cohort ( n = 36,349). Main Outcomes and Measures Cell type-specific enrichment of genetic risk loading for neuropsychiatric and neurological disorder traits in the gene expression profiles of retinal cells. Results Amacrine cells (interneurons within the retina) were robustly enriched in schizophrenia genetic risk across mammalian species and in different developmental stages. This enrichment was primarily driven by genes involved in synapse biology. On the structural level, higher polygenic risk for schizophrenia was associated with thinning of the ganglion cell–inner plexiform layer, which contains dendrites and synaptic connections of amacrine cells. Moreover, retinal immune cell populations were enriched in multiple sclerosis genetic risk. No consistent cell type associations were found for bipolar disorder, major depressive disorder, Parkinson and Alzheimer disease, or stroke. Conclusions and Relevance This study provides novel insights into the cellular underpinnings of retinal alterations in neuropsychiatric and neurological disorders and highlights the retina as a potential proxy to study synaptic pathology in schizophrenia. Introduction One major challenge in neuropsychiatric research is the difficulty of accessing human brain tissue in vivo with sufficient resolution. Because of its embryological origin, the retina is part of the central nervous system (CNS) and shares many anatomical, developmental, and functional features with the brain, making it a potential proxy for studying CNS disorders in living patients 1 . The retina is highly structured and organized into distinct layers containing cell bodies and neuropil 2 . It can be examined non-invasively with much higher resolution than the brain by using methods such as optical coherence tomography 3 and electroretinography. Retinal investigations across major neuropsychiatric and neurological disorders have identified microstructural and electrophysiological alterations in schizophrenia (SCZ), bipolar disorder (BD), major depressive disorder (MDD), 4 – 11 multiple sclerosis (MS), 12 Alzheimer disease (AD), 13 Parkinson disease (PD), 14 and stroke 15 – 18 . For example, meta-analyses show reduced thicknesses of the retinal nerve fiber layer (RNFL) and the ganglion cell–inner plexiform layer (GCIPL) 4 , 19 , 20 in individuals with SCZ compared with healthy controls. In MS, where RNFL and GCIPL thinning is also observed, retinal imaging is already used in clinical practice to support diagnosis and disease monitoring 12 , 21 . However, the mechanistic underpinnings of retinal alterations in CNS disorders are poorly understood. Hypothetically, shared genetically driven mechanisms could directly affect retinal structures in brain diseases 1 , 7 , 22 – 29 in addition to secondary mechanisms such as retrograde degeneration in MS 30 and stroke 15 or the effect of comorbidities in psychiatric disorders 31 . Previous studies have identified genetic overlap between retinal structures and CNS disease traits 25 , 29 and have shown an association between polygenic disease risk and retinal alterations in SCZ, 7 , 26 , 32 AD, 27 and PD 27 . However, the cellular and molecular mechanisms that drive the link between the retina and CNS traits remain unknown. The integration of summary statistics of genome-wide association studies (GWASs) and single cell transcriptomic data via gene set analysis tools such as MAGMA has been shown to be a powerful approach to elucidate pathophysiological mechanisms and identify relevant cell types in polygenic complex traits 33 – 35 . In this study, we mapped the polygenic risk of major neuropsychiatric and neurological disorders known to be associated with retinal alterations to retinal single cell gene expression data. Our aim was to identify which retinal cell types are associated with common variant genetic risk findings in major psychiatric and neurological disorders. Moreover, we explored which genetically driven pathophysiological mechanisms converge in retinal cells, and whether the high risk loading for SCZ found in amacrine cells (ACs) could be translated back to human retinal structure in a large population-based cohort. Methods Identification of cell type associations By considering single cell and single nucleus retinal transcriptomic data sets from humans, 36 – 38 non-human primates (NHPs), 39 and mice, 40 we calculated the specificity of each gene to different retinal cell types on the basis of the gene’s expression levels in those cell types ( eMethods in Supplement 1 ). In addition, we assigned single nucleotide polymorphisms from GWAS summary statistics for stroke, 41 AD, 42 PD, 43 MS, 44 MDD, 45 BD, 46 and SCZ 47 to nearby genes ( eMethods in Supplement 1 ). Then, we performed a MAGMA cell type enrichment analysis 33 , 34 ( Figure 1a ; eMethods in Supplement 1 ) to evaluate whether genetic risk is preferentially enriched in distinct cell types, as indicated by stronger genome-wide association signals in more cell type-specific genes. Download figure Open in new tab Figure 1: Gene set analysis reveals enrichment of genetic risk for major neurological and neuropsychiatric disorders in retinal cells. a, Schematic overview of the analytical approach. Single nucleotide polymorphisms from genome-wide association studies (GWASs) for several neurological and neuropsychiatric disorders were assigned to their nearby genes. In addition, specificity for each gene to retinal cell types was calculated from scRNA-seq expression data. Then, a MAGMA gene set analysis was performed to calculate the enrichment between genetic risk and the top 10% of genes that had the highest cell type specificity for each cell type. b, Dotplots illustrating the associations between retinal cell types and GWAS traits; dotplots were based on human scRNA-seq data 36 for the fovea (left) and the peripheral retina (right). P values were adjusted for the false discovery rate within each trait. Inner black dots indicate statistical significance after additional Bonferroni correction for the seven investigated traits. The size of the outer dots represents the false discovery rate adjusted MAGMA P value. The color scale illustrates the MAGMA Z-score. Abbreviations: AC, amacrine cell; AD, Alzheimer disease; BC, bipolar cell; BD, bipolar disorder; FDR, false discovery rate-adjusted P value; GWAS, genome-wide association study; HC, horizontal cell; MDD, major depressive disorder; MG, Müller glia; MS; multiple sclerosis; PD, Parkinson disease; RGC, retinal ganglion cell; RPE, retinal pigment epithelium; scRNA-seq, single-cell RNA sequencing; SCZ, schizophrenia; snRNA-seq, single-nucleus RNA sequencing. Gene Ontology analyses To gain insights into the neurobiological mechanisms through which polygenic risk acts in the revealed cell types we analyzed the enrichment of biologically relevant gene sets in the lists of genes that are associated with traits of interest and specifically expressed in trait-relevant cell types. To this end, we performed gene set enrichment analyses (GSEAs) with Enrichr 48 – 50 and preranked GSEA 51 ( eMethods in Supplement 1 ). Validation and translation in a population-based cohort We used population data from the UK Biobank (UKBB; www.ukbiobank.ac.uk ) to validate and translate our top cellular findings. The population data included Caucasian British and Irish individuals without a diagnosis of SCZ or schizotypal or delusional disorder ( eFigure 7 in Supplement 1 ). We computed a polygenic risk score (PRS) for SCZ (SCZ-PRS) from the latest release of genome-wide association data 47 . By using robust regression, we tested whether genetic susceptibility to SCZ is associated with alterations of the RNFL, GCIPL, and inner nuclear layer (INL) thickness ( eMethods in Supplement 1 ). Results Cell type-specific enrichment of GWAS traits in retinal cells Our analyses were based on the assumption that the polygenic risk burden for a complex brain disorder accumulates not only in the brain cell types that are mainly relevant for the clinical phenotype 34 , 35 but also in tissues and cell types affected by the same genetic architecture and, presumably, by the same mechanisms that are relevant for the said clinical phenotype. A disease-relevant cell type will show an accumulation of expressed genes with high genetic risk loading, and if the functions of said cell type are relevant for pathobiology, the signal will increase with the specificity of expression. Cell types in other tissues and contexts that express the same genes may be affected by the same genetic risk and produce a different or similar pathophysiological state. To investigate such associations of major polygenic brain disorders in retinal cell types, we performed a MAGMA cell type enrichment analysis by integrating GWAS summary statistics with single-cell RNA sequencing (scRNA-seq) data from light-responsive human post mortem retinas 36 ( Figure 1b ; eTable 1 and 2 in Supplement 2 ). The analysis revealed that the expression profile of ACs was significantly enriched for genetic risk of SCZ in both the fovea and the peripheral retina. Less consistently, SCZ also mapped to other retinal neurons in humans, i.e., to horizontal cells (HCs), but only in the foveal region, and to retinal ganglion cells (RGCs), but only in the peripheral retina. After controlling the expression specificity of each gene for ACs in MAGMA, the associations with HCs and RGCs with SCZ were still significant. This finding indicates that the observed inconsistent enrichment of HCs and RGCs cannot be solely explained by genetic overlap between retinal neurons ( eTable 3 and 4 in Supplement 2 ). In MDD, we found a link to peripheral retinal ganglion cells (RGCs), albeit with a weaker effect than for SCZ, and in MS, we identified a significant enrichment with foveal and peripheral retinal immune cell populations. In stroke, AD, PD, and BD, analyses showed no significant cell type associations with genetic risk. To exclude confounding by data set-specific batch effects, we validated our findings in an independent human single-nucleus RNA sequencing (snRNA-seq) data set 38 . This analysis confirmed significant enrichment for SCZ risk in retinal ACs and for MS risk in retinal microglia ( eFigure 1a in Supplement 1 ; eTable 5 in Supplement 2 ). ACs represent a highly diverse population of interneurons that, with a few exceptions, 52 release inhibitory neurotransmitters 53 . To investigate whether the enrichment between SCZ risk and ACs was driven by a particular subtype, we performed additional cell type enrichment analyses within the comprehensively annotated AC snRNA-seq subset from the Human Retinal Cell Atlas 38 . This analysis showed no particular association with a specific subtype; this was the case when we considered the major AC subpopulations classified by the inhibitory neurotransmitters they express (GABAergic, glycinergic, both GABAergic and glycinergic, or non-GABAergic non-glycinergic; eFigure 1b in Supplement 1 ; eTable 6 in Supplement 2 ) and when we considered the detailed resolution of all 73 AC subtypes ( eFigure 1c in Supplement 1 ; eTable 7 in Supplement 2 ). Expression of SCZ risk genes in the developing human retina Previous studies implicate prenatal neurodevelopment in SCZ, 54 , 55 so we explored whether the enrichment of GWAS traits exhibits different patterns in snRNA-seq data from fetal retinal cells 37 . In this evaluation, we found a robust enrichment for SCZ risk in fetal ACs ( eFigure 2 in Supplement 1 ; eTable 8 in Supplement 2 ). Fetal retinal cell types showed no association with any other disorder. Download figure Open in new tab Figure 2: Associations between traits and retinal cell types are robust across mammalian species. Bar plots illustrating the log-transformed MAGMA P values (false discovery rate-corrected across each trait) for the association between retinal cells and schizophrenia, bipolar disorder, major depressive disorder, Parkinson disease, Alzheimer disease, multiple sclerosis, and stroke in the non-human primate macaca fascicularis 39 and in mice 40 . The dashed lines represent the Bonferroni-corrected significance level of 0.05/7. Note that vascular cells, retinal pigment epithelium, pericytes, endothelial cells, and astrocytes were not similarly annotated in the two species. Abbreviations: AC, amacrine cell; AD, Alzheimer disease; BC, bipolar cell; BD, bipolar disorder; FDR, false discovery rate-adjusted P value; HC, horizontal cell; MDD, major depressive disorder; MG, Müller glia; MS; multiple sclerosis; NHP, non-human primate; PD, Parkinson disease; RGC, retinal ganglion cell; RPE, retinal pigment epithelium; SCZ, schizophrenia. Validation of retinal enrichment across species To validate the associations identified in human transcriptomes and look for conservation of these findings in mammalian model organisms, we repeated the cell type association analysis with scRNA-seq data from non-human primates (NHPs) 39 and mice 40 ( Figure 2 ; eTable 9 – 11 in Supplement 2 ). These analyses confirmed the robust association of SCZ with ACs and revealed significant enrichment in foveal ACs of NHPs and in mice ACs, but not in peripheral ACs of NHPs. Moreover, we found inconsistent enrichment of the SCZ trait in the transcriptome of other retinal neurons of both species, namely bipolar cells (BCs) and RGCs of mice and foveal HCs of NHPs. For BD, the results showed a significant enrichment in HCs in both species. MDD was inconsistently enriched in ACs, HCs, and RGCs. In line with the findings in the human retina, genetic risk for MS was enriched in microglia from both species. Notably, in stroke the enrichment of the genome-wide association signal in peripheral retinal vascular cells also reached statistical significance in NHPs. In summary, in analyses of transcriptomic data from different species and developmental stages we found that SCZ was robustly enriched in retinal ACs and that MS robustly mapped to immune cell populations but not to neural cells. Enriched genes converge on distinct gene sets To reveal the biological mechanisms behind the identified trait–cell type associations, we used Enrichr 48 – 50 to perform GSEAs with those cell type-specific genes that were significantly associated in the human transcriptome with the trait of interest ( Figure 3 ; eFigure 3 and 4 in Supplement 1 ). Download figure Open in new tab Figure 3: Enrichment analysis implicates disrupted retinal synapses in schizophrenia. a–c, Bar plots displaying the top 10 gene sets from Gene Ontology (GO) biological process ( a ), GO cellular component ( b ), and GO molecular function ( c ) that were overrepresented among those genes that were specifically expressed in amacrine cells and significantly associated with schizophrenia (SCZ), as obtained with Enrichr. Specifically, the enrichment analysis was based on the overlap of 226 genes that were in the top specificity decile for both foveal and peripheral amacrine cells and had an unadjusted MAGMA gene-level P value of less than 0.05. For additional analyses for each relevant cell type and region separately, see e Figure 4 . d, Sunburst plot from SynGO based on 84/226 genes that were significantly associated with SCZ on the gene level, had a cellular component annotation in SynGO, and were specifically expressed in both foveal and peripheral amacrine cells in the Cowan et al. data set 36 . Colors indicate gene count per term, including child terms. Fields with 10 or more genes mapping to them or their child terms are annotated. e–h, Bar plots illustrating the 10 genes with the highest MAGMA Z-scores for SCZ within the top 10% cell type-specific genes of each cell type that was significantly associated with genetic risk for SCZ in the human data set of Cowan et al. 36 Abbreviations: AC, amacrine cell; HC, horizontal cell; RGC, retinal ganglion cell. Download figure Open in new tab Figure 4: Ganglion cell–inner plexiform layer thickness is associated with polygenic risk for schizophrenia in a population-based cohort. a, Schematic illustration of the retinal layers (retinal nerve fiber layer, ganglion cell–inner plexiform layer [GCIPL], inner nuclear layer) that were investigated within the population-based cohort and of the anatomical location of the amacrine cells. b, Partial robust regression plot between GCIPL thickness (averaged across both eyes) and polygenic risk scores for schizophrenia in the UK Biobank cohort ( n = 36,349). Solid line represents the regression line, and the complementary shaded area corresponds to the 95% CI. Abbreviations: AC, amacrine cell; GCIPL, ganglion cell–inner plexiform layer; INL, inner nuclear layer; RNFL, retinal nerve fiber layer. In SCZ, we found broad involvement of gene sets related to synapse biology, including gene sets involved in synaptic plasticity and in calcium and glutamatergic signaling in ACs ( Figure 3a – c ). According to SynGO 56 annotation, the SCZ risk genes with high specificity for ACs were relevant for both pre and postsynaptic structures, such as the presynaptic membrane and postsynaptic density membrane, respectively ( Figure 3d ). To validate our findings with another method, we performed a preranked GSEA 51 ; the results of this analysis also pointed to neuronal and synaptic function, and in addition, to cell adhesion ( eFigure 3 ). Among the genes specifically expressed in ACs, HCs, and RGCs with the strongest association to SCZ were CACNA1I, which encodes the calcium voltage-gated channel subunit alpha1 I; DOC2A , which is relevant for Ca 2+ -dependent neurotransmitter release 57 ; the dopamine receptor D2 ( DRD2 ), which is the common target of antipsychotics 58 ; and SLC32A1 , which codes for a protein involved in GABA and glycine uptake into synaptic vesicles 59 ( Figure 3e ; eTable 12–15 in Supplement 3 ). In MDD, the GSEAs yielded gene sets involved in neuronal biology, but with less involvement in synapse biology ( eFigure 5 ; eTable 16 in Supplement 3 ). In MS, the enrichment in retinal immune cells converged onto immunological gene sets, especially those involved in cell-mediated immunity ( eFigure 6 in Supplement 1 ), with interleukin 2 receptor subunit alpha ( IL2RA ) as the most strongly associated gene ( eTable 17 and 18 in Supplement 3 ). Schizophrenia polygenic risk and retinal imaging To translate the most consistent genetic association of the SCZ trait with ACs to the human retinal microstructure in vivo, we explored the association between SCZ-PRSs and retinal layer thicknesses in 36,349 individuals of the UKBB cohort ( eFigure 7 in Supplement 1 ; Figure 4 ). Robust linear regression revealed a significant association of higher SCZ-PRS with reduced GCIPL thickness ( b = −0.09; 95% CI [−0.16, −0.03]; P = 0.007; Figure 4b ) but no significant associations with RNFL ( P = 0.481) and INL ( P = 0.865) thickness ( eTable 19 in Supplement 4 ). Discussion By integrating GWAS summary statistics and single cell transcriptomics, we showed that the polygenic risk of different CNS disorders converges on distinct cell types and biological functions in the retina. Most notably, we found robust associations between SCZ risk and ACs and between MS risk and retinal immune cells, which were conserved across different mammalian species. Interestingly, the enrichment of SCZ in ACs was also detectable in fetal retinal tissue, underlining that the retina reflects the neurodevelopmental component in SCZ pathogenesis 54 . Less consistently, we detected associations of HCs, RGCs, and BCs with SCZ in several human retinal data sets from different anatomical regions and in transcriptomic data from mice and NHPs. Some inconsistencies between species might be explained by the fact that although most cell types are conserved, marked anatomical differences also exist, not least the absence of a fovea in mice 2 , 60 . Moreover, technical variations in tissue preparation and scRNA-seq processing may also have contributed to these inconsistencies. ACs are mostly GABAergic or glycinergic interneurons with diverse functions in visual signal processing 53 . They provide feedback on BCs, whose response to light has been found to be reduced in SCZ, 7 , 9 and synapse with RGCs and with each other within the inner plexiform layer (IPL) 53 , 61 , 62 . Previous brain-based post-GWAS analyses highlighted a disturbed synapse biology in SCZ that also affects GABAergic interneurons 47 , 63 . Here, we found that the polygenic disease risk also converges on gene sets that are highly relevant for synaptic function in ACs. In particular, the functions in which these gene sets are involved include synaptic plasticity and neurotransmitter biology, which have been identified to be dysregulated in SCZ 64 , 65 . We translated these genetically driven cellular findings to the microstructural level by demonstrating an association of higher SCZ-PRS with GCIPL thinning in a non-schizophrenic population-based cohort, the UKBB. This finding, which was based on the latest SCZ GWAS 47 , aligns with a very recent analysis of UKBB data 32 using a different GWAS discovery sample 66 . The GCIPL comprises the ganglion cell layer, which contains RGC bodies, and the complex neuropil of the IPL 67 . Given that our study found no association for the RNFL, the axonal layer of RGCs, it is less likely that the revealed association of GCIPL thickness with SCZ-PRS is driven by alterations of RGCs. Instead, it might reflect synaptic impairments within the IPL in SCZ. The somas of ACs, BCs, and HCs in the INL appeared not to be affected. This finding aligns with a recent study from our group which highlighted pronounced thinning of the IPL 7 in SCZ spectrum disorders. The present study indicates that synapse biology in SCZ, which is disturbed in the brain 46 , is also altered in the retina and thereby might contribute to the microstructural alterations in the complex neuropil of the IPL 67 in patients with SCZ. Given the difficulties in investigating synaptic pathology in the brain itself in vivo in humans, 63 , 68 our findings highlight the retina as a unique opportunity to directly image microstructures that could reflect synaptic pathology in the CNS. In addition, our study yielded inconsistent evidence of an association between several types of retinal neurons and affective disorders. Considering the robust association of retinal neurons with genetic SCZ risk, the less consistent enrichment of BD and MDD in retinal cells might reflect their genetic overlap with SCZ 69 within the spectrum of neuropsychiatric disorders 70 . With respect to neurological disorders, only MS risk was robustly enriched in a distinct cell population. The link between MS and retinal immune cells and microglia is consistent with the known enrichment of MS risk genes in brain microglia 44 and could suggest a role of resident immune cells in retinal thinning in MS 71 . In addition, a recent study demonstrated genetic pleiotropy of retinal structures with AD and PD 29 . An increased AD-PRS has been linked to retinal changes such as a thicker IPL and INL, and an increased PD-PRS, to a thinner outer plexiform layer 27 . However, other studies failed to find relevant genetic associations between AD and the thicknesses of the RNFL or GCIPL 72 or between PRSs for retinal layer thicknesses and incident or prevalent AD or PD 73 . On a cellular level, postmortem findings point to a reduction of dopaminergic ACs in PD 74 . Conversely, AD, which is genetically associated with brain microglia, 42 , 75 has been linked to dysfunction of retinal glia 76 – 79 . Despite these previously described associations of retinal cell populations with AD and PD, our study found no significant enrichment of AD or PD with specific cell types. Of note, the negative finding regarding a genetic link between retinal microglia and AD is consistent with previous work 22 . In summary, among several neurological and psychiatric diseases that were previously linked to retinal alterations, only the polygenic architecture of SCZ and MS robustly mapped to specific and distinct retinal cell populations, indicating distinct disease- and cell type-specific mechanisms underlying the reported retinal alterations in SCZ and MS. Limitations Our approach has several limitations. Because we focused on highly cell type-specific genes, effects of genes with broader, tissue-wide, or pan-neuronal expression patterns are underrepresented. Nevertheless, this approach enables better interpretability because by minimizing overlap between cell types, it yields fewer significant results 80 . Second, technical limitations of scRNA-seq and snRNA-seq may lead to underrepresentation of transcripts exported to distal cellular compartments 34 , 81 . Moreover, expression patterns may change with age 35 . The donor age range of the Cowan et al. retina atlas was between 50 and 80 years, 36 which is far beyond the typical age of onset of SCZ 82 . However, we applied our analyses to retinas from two developmental stages in humans (adults and fetuses), and the finding for ACs in SCZ was robust across both these developmental stages and across transcriptomic data sets from different species. Conclusions This study demonstrates that the polygenic risk for SCZ and MS converges on specific cell populations within the retina, and it provides evidence that to a certain degree, the retina mirrors genetically driven pathophysiological mechanisms in SCZ and MS. The distinctly layered microstructure of the retina could serve as a window to the brain and enable the evaluation of cellular and synaptic pathology in vivo in affected patients, e.g., by assessing structural changes of the GCIPL in SCZ. In sum, this work highlights the retina’s potential to serve as an accessible proxy for studying the neurobiology of CNS disorders. Data availability Summary statistics for AD, BD, MDD, and SCZ were obtained from https://pgc.unc.edu/for-researchers/download-results/ . Summary statistics for PD and stroke were downloaded from the NHGRI-EBI catalogue under accession codes GCST90275127 and GCST90104534 ( https://www.ebi.ac.uk/gwas/studies/ ). MS summary statistics were sourced from the Data Access Committee of the International Multiple Sclerosis Genetics Consortium (IMSGC) via https://imsgc.net/?page_id=31 . Chan Zuckerberg CELLxGENE Discover was used to download retinal scRNA-seq data of Cowan et al. ( https://cellxgene.cziscience.com/collections/2f4c738f-e2f3-4553-9db2-0582a38ea4dc ), the MRCA ( https://cellxgene.cziscience.com/collections/a0c84e3f-a5ca-4481-b3a5-ccfda0a81ecc ), and the HRCA ( https://cellxgene.cziscience.com/collections/4c6eaf5c-6d57-4c76-b1e9-60df8c655f1e ). The NHP expression data of Peng et al. were downloaded from the Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell/study/SCP212/ ). HomoloGene is available at https://www.ncbi.nlm.nih.gov/datasets/gene/ . g:Profiler is available at https://biit.cs.ut.ee/gprofiler/gost ). MSigDB is available at https://www.gsea-msigdb.org/gsea/msigdb/ . Information on how to access data from the UK Biobank is available at https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access . Code availability SynGO is available at https://www.syngoportal.org/ . Enrichr is available at https://maayanlab.cloud/Enrichr/ . The MAGMA.Celltyping R package is available at https://github.com/neurogenomics/MAGMA_Celltyping . Author contributions Dr Rabe had full access to the UK Biobank data used in the study. Mr Boudriot had full access to all other data in the study except the UK Biobank data. Both authors take responsibility for the integrity of the respective data and the accuracy of the data analysis. Concept and design: Boudriot, Raabe, Stephan. Acquisition, analysis, or interpretation of data: Boudriot, Papiol, Raabe, Rabe, Smigielski, Stephan. Drafting of the manuscript: Boudriot, Papiol, Raabe, Rabe, Smigielski. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Boudriot, Papiol, Raabe, Rabe, Smigielski, Stephan. Obtained funding: Boudriot, Falkai, Homan, Raabe, Rossner, Schmitt. Administrative, technical, or material support: Falkai, Homan. Supervision: Raabe. Conflict of Interest Disclosures Prof Rossner is shareholder of, and Dr Stephan is employed by Systasy Bioscience GmbH, Munich, Germany. Prof Falkai received speaking fees from Boehringer-Ingelheim, Janssen, Otsuka, Lundbeck, Recordati, and Richter and was a member of the advisory boards of these companies and Rovi. Prof Homan has received grants and honoraria from Novartis, Lundbeck, Mepha, Janssen, Boehringer Ingelheim, and Neurolite outside of this work. No other disclosures were reported. Funding/Support Mr Boudriot received funding from the Pesl Alzheimer Foundation (Pesl-Alzheimer-Stiftung; 2024– 2025) and the Foundations of the Medical Faculty of the LMU Munich (Stiftungen zugunsten der Medizinischen Fakultät – Cluster 2; 2024–2025). Prof Falkai was supported by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung [BMBF]) within the initial phase of the German Center for Mental Health (DZPG; grant FKZ 01EE2303A and 01EE2303F) and within the Era-Net Neuron project GDNF_UpReg (FKZ 01EW2206). Prof Homan is supported by a NARSAD grant from the Brain & Behavior Research Foundation (28445), by a Research Grant from the Novartis Foundation (20A058), and by an ERC Synergy Grant (101118756). Dr Papiol was supported by European Union’s Horizon Europe research and innovation program under grant agreement No 101057454 (Psych-STRATA). Dr Raabe was supported by the Munich Clinician Scientist Program (MCSP) of the Medical Faculty, LMU Munich (FöFoLe 009/2019 and Advanced Track 01/2021), the Lisa Oehler Foundation (Dr. Lisa Oehler-Stiftung; 2022–2024), and the Pesl Alzheimer Foundation (Pesl-Alzheimer-Stiftung; 2024–2025). Role of the Funder/Sponsor The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Supplementary information Supplement 1: eMethods and eFigure 1–7. Supplement 2: eTable 1–11. Supplement 3: eTable 12–18. Supplement 4: eTable 19. Acknowledgments The authors thank Jacquie Klesing, BMedSci (Hons), Board-certified Editor in the Life Sciences (ELS), for editing assistance with the manuscript. Figure 1a and Figure 4a were created with Biorender.com. References 1. ↵ London A , Benhar I , Schwartz M . The retina as a window to the brain-from eye research to CNS disorders . Nat Rev Neurol . Jan 2013 ; 9 ( 1 ): 44 – 53 . doi: 10.1038/nrneurol.2012.227 OpenUrl CrossRef PubMed 2. ↵ Grünert U , Martin PR . Cell types and cell circuits in human and non-human primate retina . Prog Retin Eye Res . Feb 5 2020 : 100844 . doi: 10.1016/j.preteyeres.2020.100844 OpenUrl CrossRef 3. ↵ Huang D , Swanson EA , Lin CP , et al. Optical Coherence Tomography . Science . 1991 ; 254 ( 5035 ): 1178 – 1181 . doi : doi: 10.1126/science.1957169 OpenUrl Abstract / FREE Full Text 4. ↵ Komatsu H , Onoguchi G , Silverstein SM , et al. Retina as a potential biomarker in schizophrenia spectrum disorders: a systematic review and meta-analysis of optical coherence tomography and electroretinography . Mol Psychiatry . Dec 11 2023 ; doi: 10.1038/s41380-023-02340-4 OpenUrl CrossRef 5. Prasannakumar A , Kumar V , Mailankody P , et al. A systematic review and meta-analysis of optical coherence tomography studies in schizophrenia, bipolar disorder and major depressive disorder . World J Biol Psychiatry . Oct 2023 ; 24 ( 8 ): 707 – 720 . doi: 10.1080/15622975.2023.2203231 OpenUrl CrossRef 6. Wagner SK , Cortina-Borja M , Silverstein SM , et al. Association Between Retinal Features From Multimodal Imaging and Schizophrenia . JAMA Psychiatry . May 1 2023 ; 80 ( 5 ): 478 – 487 . doi: 10.1001/jamapsychiatry.2023.0171 OpenUrl CrossRef 7. ↵ Boudriot E , Gabriel V , Popovic D , et al. Signature of altered retinal microstructures and electrophysiology in schizophrenia spectrum disorders is associated with disease severity and polygenic risk . Biological Psychiatry . 2024 ; doi: 10.1016/j.biopsych.2024.04.014 OpenUrl CrossRef 8. Boudriot E , Schworm B , Slapakova L , et al. Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders . Eur Arch Psychiatry Clin Neurosci . Apr 2023 ; 273 ( 3 ): 575 – 588 . doi: 10.1007/s00406-022-01455-z OpenUrl CrossRef 9. ↵ Hébert M , Mérette C , Gagné AM , et al. The Electroretinogram May Differentiate Schizophrenia From Bipolar Disorder . Biol Psychiatry . Feb 1 2020 ; 87 ( 3 ): 263 – 270 . doi: 10.1016/j.biopsych.2019.06.014 OpenUrl CrossRef 10. Bubl E , Kern E , Ebert D , Bach M , Tebartz van Elst L . Seeing gray when feeling blue? Depression can be measured in the eye of the diseased. Biol Psychiatry . Jul 15 2010 ; 68 ( 2 ): 205 – 8 . doi: 10.1016/j.biopsych.2010.02.009 OpenUrl CrossRef PubMed Web of Science 11. ↵ Georgiadis F , Kallen N , Cecere G , Barthelmes D , Kirschner M , Homan P . Detecting transdiagnostic retinal deviations in mental disorders through normative modeling . medRxiv . 2024 :2024.06.11.24308654. doi: 10.1101/2024.06.11.24308654 OpenUrl Abstract / FREE Full Text 12. ↵ Petzold A , Balcer LJ , Calabresi PA , et al. Retinal layer segmentation in multiple sclerosis: a systematic review and meta-analysis . Lancet Neurol . Oct 2017 ; 16 ( 10 ): 797 – 812 . doi: 10.1016/s1474-4422(17)30278-8 OpenUrl CrossRef PubMed 13. ↵ den Haan J , Verbraak FD , Visser PJ , Bouwman FH . Retinal thickness in Alzheimer’s disease: A systematic review and meta-analysis . Alzheimers Dement (Amst) . 2017 ; 6 : 162 – 170 . doi: 10.1016/j.dadm.2016.12.014 OpenUrl CrossRef 14. ↵ Lee JY , Martin-Bastida A , Murueta-Goyena A , et al. Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease . Nat Rev Neurol . Apr 2022 ; 18 ( 4 ): 203 – 220 . doi: 10.1038/s41582-022-00618-9 OpenUrl CrossRef 15. ↵ Park HY , Park YG , Cho AH , Park CK . Transneuronal retrograde degeneration of the retinal ganglion cells in patients with cerebral infarction . Ophthalmology . Jun 2013 ; 120 ( 6 ): 1292 – 9 . doi: 10.1016/j.ophtha.2012.11.021 OpenUrl CrossRef PubMed Web of Science 16. Anjos R , Vieira L , Costa L , et al. Macular Ganglion Cell Layer and Peripapillary Retinal Nerve Fibre Layer Thickness in Patients with Unilateral Posterior Cerebral Artery Ischaemic Lesion: An Optical Coherence Tomography Study . Neuroophthalmology . Feb 2016 ; 40 ( 1 ): 8 – 15 . doi: 10.3109/01658107.2015.1122814 OpenUrl CrossRef PubMed 17. Ye C , Kwapong WR , Tao W , et al. Alterations of optic tract and retinal structure in patients after thalamic stroke . Front Aging Neurosci . 2022 ; 14 : 942438 . doi: 10.3389/fnagi.2022.942438 OpenUrl CrossRef 18. ↵ Wang D , Li Y , Wang C , et al. Localized retinal nerve fiber layer defects and stroke . Stroke . Jun 2014 ; 45 ( 6 ): 1651 – 6 . doi: 10.1161/strokeaha.113.004629 OpenUrl Abstract / FREE Full Text 19. ↵ Sheehan N , Bannai D , Silverstein SM , Lizano P . Neuroretinal Alterations in Schizophrenia and Bipolar Disorder: An Updated Meta-analysis . Schizophr Bull . Jul 2 2024 ; doi: 10.1093/schbul/sbae102 OpenUrl CrossRef 20. ↵ Shew W , Zhang DJ , Menkes DB , Danesh-Meyer HV . Optical Coherence Tomography in Schizophrenia Spectrum Disorders: A Systematic Review and Meta-analysis . Biological Psychiatry Global Open Science . 2024/01/01/ 2024 ; 4 ( 1 ): 19 – 30 . doi: 10.1016/j.bpsgos.2023.08.013 OpenUrl CrossRef 21. ↵ Minakaran N , de Carvalho ER , Petzold A , Wong SH . Optical coherence tomography (OCT) in neuro-ophthalmology . Eye . 2021/01/01 2021 ; 35 ( 1 ): 17 – 32 . doi: 10.1038/s41433-020-01288-x OpenUrl CrossRef 22. ↵ Menon M , Mohammadi S , Davila-Velderrain J , et al. Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration . Nat Commun . Oct 25 2019 ; 10 ( 1 ): 4902 . doi: 10.1038/s41467-019-12780-8 OpenUrl CrossRef PubMed 23. Li M , Jia C , Kazmierkiewicz KL , et al. Comprehensive analysis of gene expression in human retina and supporting tissues . Hum Mol Genet . Aug 1 2014 ; 23 ( 15 ): 4001 – 14 . doi: 10.1093/hmg/ddu114 OpenUrl CrossRef PubMed 24. Grassmann F , Kiel C , Zimmermann ME , et al. Genetic pleiotropy between age-related macular degeneration and 16 complex diseases and traits . Genome Med . Mar 27 2017 ; 9 ( 1 ): 29 . doi: 10.1186/s13073-017-0418-0 OpenUrl CrossRef 25. ↵ Gao XR , Huang H , Kim H . Genome-wide association analyses identify 139 loci associated with macular thickness in the UK Biobank cohort . Hum Mol Genet . Apr 1 2019 ; 28 ( 7 ): 1162 – 1172 . doi: 10.1093/hmg/ddy422 OpenUrl CrossRef PubMed 26. ↵ Rabe F , Smigielski L , Georgiadis F , et al. Genetic susceptibility to schizophrenia through neuroinflammatory pathways is associated with retinal thinning: Findings from the UK-Biobank . medRxiv . Apr 17 2024 ; doi: 10.1101/2024.04.05.24305387 OpenUrl Abstract / FREE Full Text 27. ↵ Sekimitsu S , Shweikh Y , Shareef S , et al. Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline . Br J Ophthalmol . Mar 20 2024 ; 108 ( 4 ): 599 – 606 . doi: 10.1136/bjo-2022-322762 OpenUrl Abstract / FREE Full Text 28. Shinsato RN , Correa CG , Herai RH . Genetic network analysis indicate that individuals affected by neurodevelopmental conditions have genetic variations associated with ophthalmologic alterations: A critical review of literature . Gene . May 25 2024 ; 908 : 148246 . doi: 10.1016/j.gene.2024.148246 OpenUrl CrossRef 29. ↵ Zhao B , Li Y , Fan Z , et al. Eye-brain connections revealed by multimodal retinal and brain imaging genetics . Nat Commun . Jul 18 2024 ; 15 ( 1 ): 6064 . doi: 10.1038/s41467-024-50309-w OpenUrl CrossRef 30. ↵ Gabilondo I , Martínez-Lapiscina EH , Martínez-Heras E , et al. Trans-synaptic axonal degeneration in the visual pathway in multiple sclerosis . Ann Neurol . Jan 2014 ; 75 ( 1 ): 98 – 107 . doi: 10.1002/ana.24030 OpenUrl CrossRef PubMed 31. ↵ Silverstein SM , Fradkin SI , Demmin DL . Schizophrenia and the retina: Towards a 2020 perspective . Schizophr Res . May 2020 ; 219 : 84 – 94 . doi: 10.1016/j.schres.2019.09.016 OpenUrl CrossRef 32. ↵ Blose BA , Silverstein SM , Stuart KV , Keane PA , Khawaja AP , Wagner SK . Association between polygenic risk for schizophrenia and retinal morphology: A cross-sectional analysis of the United Kingdom Biobank . Psychiatry Research . 2024/09/01/ 2024 ; 339 : 116106 . doi: 10.1016/j.psychres.2024.116106 OpenUrl CrossRef 33. ↵ de Leeuw CA , Mooij JM , Heskes T , Posthuma D . MAGMA: generalized gene-set analysis of GWAS data . PLoS Comput Biol . Apr 2015 ; 11 ( 4 ): e1004219 . doi: 10.1371/journal.pcbi.1004219 OpenUrl CrossRef PubMed 34. ↵ Skene NG , Bryois J , Bakken TE , et al. Genetic identification of brain cell types underlying schizophrenia . Nat Genet . Jun 2018 ; 50 ( 6 ): 825 – 833 . doi: 10.1038/s41588-018-0129-5 OpenUrl CrossRef PubMed 35. ↵ Bryois J , Skene NG , Hansen TF , et al. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease . Nat Genet . May 2020 ; 52 ( 5 ): 482 – 493 . doi: 10.1038/s41588-020-0610-9 OpenUrl CrossRef 36. ↵ Cowan CS , Renner M , De Gennaro M , et al. Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution . Cell . 2020/09/17/ 2020 ; 182 ( 6 ): 1623 – 1640 .e34. doi: 10.1016/j.cell.2020.08.013 OpenUrl CrossRef 37. ↵ Chen R , Zuo Z , Cheng X , et al. Single Cell Multiome Atlas of the Human Developing Retina . Preprint (Version 1) available at Research Square [ https://doiorg/1021203/rs3rs-3417373/v1 ]. 10 October 2023 2023 ; doi: 10.21203/rs.3.rs-3417373/v1 OpenUrl CrossRef 38. ↵ Li J , Wang J , Ibarra IL , et al. Integrated multi-omics single cell atlas of the human retina . Res Sq . Nov 17 2023 ; doi: 10.21203/rs.3.rs-3471275/v1 OpenUrl CrossRef 39. ↵ Peng YR , Shekhar K , Yan W , et al. Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina . Cell . Feb 21 2019 ; 176 ( 5 ): 1222 – 1237 .e22. doi: 10.1016/j.cell.2019.01.004 OpenUrl CrossRef PubMed 40. ↵ Li J , Choi J , Cheng X , et al. Comprehensive single-cell atlas of the mouse retina . bioRxiv . Jan 28 2024 ; doi: 10.1101/2024.01.24.577060 OpenUrl Abstract / FREE Full Text 41. ↵ Mishra A , Malik R , Hachiya T , et al. Stroke genetics informs drug discovery and risk prediction across ancestries . Nature . Nov 2022 ; 611 ( 7934 ): 115 – 123 . doi: 10.1038/s41586-022-05165-3 OpenUrl CrossRef 42. ↵ Wightman DP , Jansen IE , Savage JE , et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease . Nat Genet . Sep 2021 ; 53 ( 9 ): 1276 – 1282 . doi: 10.1038/s41588-021-00921-z OpenUrl CrossRef PubMed 43. ↵ Kim JJ , Vitale D , Otani DV , et al. Multi-ancestry genome-wide association meta-analysis of Parkinson’s disease . Nat Genet . Jan 2024 ; 56 ( 1 ): 27 – 36 . doi: 10.1038/s41588-023-01584-8 OpenUrl CrossRef 44. ↵ International Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility . Science . Sep 27 2019 ; 365 ( 6460 ) doi: 10.1126/science.aav7188 OpenUrl Abstract / FREE Full Text 45. ↵ Meng X , Navoly G , Giannakopoulou O , et al. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference . Nat Genet . Feb 2024 ; 56 ( 2 ): 222 – 233 . doi: 10.1038/s41588-023-01596-4 OpenUrl CrossRef 46. ↵ Mullins N , Forstner AJ , O’Connell KS , et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology . Nat Genet . Jun 2021 ; 53 ( 6 ): 817 – 829 . doi: 10.1038/s41588-021-00857-4 OpenUrl CrossRef PubMed 47. ↵ Trubetskoy V , Pardiñas AF , Qi T , et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia . Nature . Apr 2022 ; 604 ( 7906 ): 502 – 508 . doi: 10.1038/s41586-022-04434-5 OpenUrl CrossRef PubMed 48. ↵ Chen EY , Tan CM , Kou Y , et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool . BMC Bioinformatics . Apr 15 2013 ; 14 : 128 . doi: 10.1186/1471-2105-14-128 OpenUrl CrossRef PubMed 49. Kuleshov MV , Jones MR , Rouillard AD , et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update . Nucleic Acids Res . Jul 8 2016 ; 44 ( W1 ): W90 – 7 . doi: 10.1093/nar/gkw377 OpenUrl CrossRef PubMed 50. ↵ Xie Z , Bailey A , Kuleshov MV , et al. Gene Set Knowledge Discovery with Enrichr . Curr Protoc . Mar 2021 ; 1 ( 3 ): e90 . doi: 10.1002/cpz1.90 OpenUrl CrossRef PubMed 51. ↵ Korotkevich G , Sukhov V , Budin N , Shpak B , Artyomov M , Sergushichev A . Fast gene set enrichment analysis . bioRxiv ; 2016 . 52. ↵ Kim T , Soto F , Kerschensteiner D . An excitatory amacrine cell detects object motion and provides feature-selective input to ganglion cells in the mouse retina . Elife . May 19 2015 ; 4doi doi: :10.7554/eLife.08025 OpenUrl CrossRef 53. ↵ Masland RH . The tasks of amacrine cells . Vis Neurosci . Jan 2012 ; 29 ( 1 ): 3 – 9 . doi: 10.1017/s0952523811000344 OpenUrl CrossRef PubMed 54. ↵ Weinberger DR . Future of Days Past: Neurodevelopment and Schizophrenia . Schizophr Bull . Oct 21 2017 ; 43 ( 6 ): 1164 – 1168 . doi: 10.1093/schbul/sbx118 OpenUrl CrossRef 55. ↵ Hall J , Bray NJ . Schizophrenia Genomics: Convergence on Synaptic Development, Adult Synaptic Plasticity, or Both? Biol Psychiatry . Apr 15 2022 ; 91 ( 8 ): 709 – 717 . doi: 10.1016/j.biopsych.2021.10.018 OpenUrl CrossRef 56. ↵ Koopmans F , van Nierop P , Andres-Alonso M , et al. SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse . Neuron . Jul 17 2019 ; 103 ( 2 ): 217 – 234 .e4. doi: 10.1016/j.neuron.2019.05.002 OpenUrl CrossRef PubMed 57. ↵ Yao J , Gaffaney JD , Kwon SE , Chapman ER . Doc2 is a Ca2+ sensor required for asynchronous neurotransmitter release . Cell . Oct 28 2011 ; 147 ( 3 ): 666 – 77 . doi: 10.1016/j.cell.2011.09.046 OpenUrl CrossRef PubMed Web of Science 58. ↵ Seeman P . Dopamine D2 receptors as treatment targets in schizophrenia . Clin Schizophr Relat Psychoses . Apr 2010 ; 4 ( 1 ): 56 – 73 . doi: 10.3371/csrp.4.1.5 OpenUrl CrossRef PubMed 59. ↵ Platzer K , Sticht H , Bupp C , et al. De Novo Missense Variants in SLC32A1 Cause a Developmental and Epileptic Encephalopathy Due to Impaired GABAergic Neurotransmission . Ann Neurol . Dec 2022 ; 92 ( 6 ): 958 – 973 . doi: 10.1002/ana.26485 OpenUrl CrossRef 60. ↵ Volland S , Esteve-Rudd J , Hoo J , Yee C , Williams DS . A comparison of some organizational characteristics of the mouse central retina and the human macula . PLoS One . 2015 ; 10 ( 4 ): e0125631 . doi: 10.1371/journal.pone.0125631 OpenUrl CrossRef PubMed 61. ↵ Masland RH . The neuronal organization of the retina . Neuron . Oct 18 2012 ; 76 ( 2 ): 266 – 80 . doi: 10.1016/j.neuron.2012.10.002 OpenUrl CrossRef PubMed Web of Science 62. ↵ Balasubramanian R , Gan L . Development of Retinal Amacrine Cells and Their Dendritic Stratification . Current Ophthalmology Reports . 2014/09/01 2014 ; 2 ( 3 ): 100 – 106 . doi: 10.1007/s40135-014-0048-2 OpenUrl CrossRef PubMed 63. ↵ Howes OD , Onwordi EC . The synaptic hypothesis of schizophrenia version III: a master mechanism . Molecular Psychiatry . 2023/05/01 2023 ; 28 ( 5 ): 1843 – 1856 . doi: 10.1038/s41380-023-02043-w OpenUrl CrossRef 64. ↵ Wu XL , Yan QJ , Zhu F . Abnormal synaptic plasticity and impaired cognition in schizophrenia . World J Psychiatry . Apr 19 2022 ; 12 ( 4 ): 541 – 557 . doi: 10.5498/wjp.v12.i4.541 OpenUrl CrossRef 65. ↵ McCutcheon RA , Krystal JH , Howes OD . Dopamine and glutamate in schizophrenia: biology, symptoms and treatment . World Psychiatry . 2020 ; 19 ( 1 ): 15 – 33 . doi: 10.1002/wps.20693 OpenUrl CrossRef 66. ↵ Thompson DJ , Wells D , Selzam S , et al. UK Biobank release and systematic evaluation of optimised polygenic risk scores for 53 diseases and quantitative traits . medRxiv . 2022 :2022.06.16.22276246. doi: 10.1101/2022.06.16.22276246 OpenUrl Abstract / FREE Full Text 67. ↵ Helmstaedter M , Briggman KL , Turaga SC , Jain V , Seung HS , Denk W . Connectomic reconstruction of the inner plexiform layer in the mouse retina . Nature . 2013/08/01 2013 ; 500 ( 7461 ): 168 – 174 . doi: 10.1038/nature12346 OpenUrl CrossRef PubMed Web of Science 68. ↵ Serrano ME , Kim E , Petrinovic MM , Turkheimer F , Cash D . Imaging Synaptic Density: The Next Holy Grail of Neuroscience? Front Neurosci . 2022 ; 16 : 796129 . doi: 10.3389/fnins.2022.796129 OpenUrl CrossRef 69. ↵ Romero C , Werme J , Jansen PR , et al. Exploring the genetic overlap between twelve psychiatric disorders . Nat Genet . Dec 2022 ; 54 ( 12 ): 1795 – 1802 . doi: 10.1038/s41588-022-01245-2 OpenUrl CrossRef 70. ↵ Adam D . Mental health: On the spectrum . Nature . Apr 25 2013 ; 496 ( 7446 ): 416 – 8 . doi: 10.1038/496416a OpenUrl CrossRef PubMed Web of Science 71. ↵ Choi S , Guo L , Cordeiro MF . Retinal and Brain Microglia in Multiple Sclerosis and Neurodegeneration . Cells . Jun 15 2021 ; 10 (6) doi: 10.3390/cells10061507 OpenUrl CrossRef 72. ↵ Sáez ME , García-Sánchez A , de Rojas I , et al. Genome-wide association study and polygenic risk scores of retinal thickness across the cognitive continuum: data from the NORFACE cohort . Alzheimers Res Ther . Feb 16 2024 ; 16 ( 1 ): 38 . doi: 10.1186/s13195-024-01398-8 OpenUrl CrossRef 73. ↵ Zekavat SM , Jorshery SD , Rauscher FG , et al. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health . Sci Transl Med . Jan 24 2024 ; 16 ( 731 ): eadg4517 . doi: 10.1126/scitranslmed.adg4517 OpenUrl CrossRef 74. ↵ Ortuño-Lizarán I , Sánchez-Sáez X , Lax P , et al. Dopaminergic Retinal Cell Loss and Visual Dysfunction in Parkinson Disease . Ann Neurol . Nov 2020 ; 88 ( 5 ): 893 – 906 . doi: 10.1002/ana.25897 OpenUrl CrossRef 75. ↵ Hansen DV , Hanson JE , Sheng M . Microglia in Alzheimer’s disease . J Cell Biol . Feb 5 2018 ; 217 ( 2 ): 459 – 472 . doi: 10.1083/jcb.201709069 OpenUrl Abstract / FREE Full Text 76. ↵ Palko SI , Benoit MR , Yao AY , Mohan R , Yan R . ER-stress response in retinal Müller glia occurs significantly earlier than amyloid pathology in the Alzheimer’s mouse brain and retina . Glia . Jun 2024 ; 72 ( 6 ): 1067 – 1081 . doi: 10.1002/glia.24514 OpenUrl CrossRef 77. Koronyo Y , Rentsendorj A , Mirzaei N , et al. Retinal pathological features and proteome signatures of Alzheimer’s disease . Acta Neuropathol . Apr 2023 ; 145 ( 4 ): 409 – 438 . doi: 10.1007/s00401-023-02548-2 OpenUrl CrossRef 78. Xu QA , Boerkoel P , Hirsch-Reinshagen V , et al. Müller cell degeneration and microglial dysfunction in the Alzheimer’s retina . Acta Neuropathologica Communications . 2022/10/05 2022 ; 10 ( 1 ): 145 . doi: 10.1186/s40478-022-01448-y OpenUrl CrossRef 79. ↵ Fernández-Albarral JA , Salobrar-García E , Martínez-Páramo R , et al. Retinal glial changes in Alzheimer’s disease - A review . J Optom . Jul-Sep 2019 ; 12 ( 3 ): 198 – 207 . doi: 10.1016/j.optom.2018.07.001 OpenUrl CrossRef 80. ↵ Alvarado CX , Weller CA , Johnson N , et al. Human brain single nucleus cell type enrichments in neurodegenerative diseases . medRxiv . Jul 1 2023 ; doi: 10.1101/2023.06.30.23292084 OpenUrl Abstract / FREE Full Text 81. ↵ Cuevas-Diaz Duran R , González-Orozco JC , Velasco I , Wu JQ . Single-cell and single-nuclei RNA sequencing as powerful tools to decipher cellular heterogeneity and dysregulation in neurodegenerative diseases . Front Cell Dev Biol . 2022 ; 10 : 884748 . doi: 10.3389/fcell.2022.884748 OpenUrl CrossRef 82. ↵ Häfner H , Maurer K , Löffler W , Riecher-Rössler A . The influence of age and sex on the onset and early course of schizophrenia . Br J Psychiatry . Jan 1993 ; 162 : 80 – 6 . doi: 10.1192/bjp.162.1.80 OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted August 12, 2024. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. 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 Genetic analysis of retinal cell types reveals synaptic pathology in schizophrenia Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv 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 Genetic analysis of retinal cell types reveals synaptic pathology in schizophrenia Emanuel Boudriot , Marius Stephan , Finn Rabe , Lukasz Smigielski , Andrea Schmitt , Peter Falkai , Michael J. Ziller , Moritz J. Rossner , Philipp Homan , Sergi Papiol , Florian J. Raabe bioRxiv 2024.08.09.607343; doi: https://doi.org/10.1101/2024.08.09.607343 Share This Article: Copy Citation Tools Genetic analysis of retinal cell types reveals synaptic pathology in schizophrenia Emanuel Boudriot , Marius Stephan , Finn Rabe , Lukasz Smigielski , Andrea Schmitt , Peter Falkai , Michael J. Ziller , Moritz J. Rossner , Philipp Homan , Sergi Papiol , Florian J. Raabe bioRxiv 2024.08.09.607343; doi: https://doi.org/10.1101/2024.08.09.607343 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 Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7644) Biochemistry (17726) Bioengineering (13916) Bioinformatics (42033) Biophysics (21486) Cancer Biology (18635) Cell Biology (25549) Clinical Trials (138) Developmental Biology (13397) Ecology (19940) Epidemiology (2067) Evolutionary Biology (24361) Genetics (15620) Genomics (22541) Immunology (17763) Microbiology (40468) Molecular Biology (17207) Neuroscience (88739) Paleontology (667) Pathology (2842) Pharmacology and Toxicology (4834) Physiology (7659) Plant Biology (15175) Scientific Communication and Education (2047) Synthetic Biology (4304) Systems Biology (9834) Zoology (2272)