Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity | 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 Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity View ORCID Profile Shuyang Yao , View ORCID Profile Arvid Harder , Fahimeh Darki , View ORCID Profile Yu-Wei Chang , View ORCID Profile Ang Li , View ORCID Profile Kasra Nikouei , Giovanni Volpe , View ORCID Profile Johan N Lundström , View ORCID Profile Jian Zeng , View ORCID Profile Naomi Wray , View ORCID Profile Yi Lu , View ORCID Profile Patrick F Sullivan , View ORCID Profile Jens Hjerling-Leffler doi: https://doi.org/10.1101/2024.01.18.24301478 Shuyang Yao 1 Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm, Sweden 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shuyang Yao Arvid Harder 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Arvid Harder Fahimeh Darki 3 Department of Clinical Neuroscience, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yu-Wei Chang 4 Department of Physics, University of Gothenburg , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yu-Wei Chang Ang Li 5 Institute for Molecular Bioscience, University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ang Li Kasra Nikouei 1 Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kasra Nikouei Giovanni Volpe 4 Department of Physics, University of Gothenburg , Gothenburg, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Johan N Lundström 3 Department of Clinical Neuroscience, Karolinska Institutet , Stockholm, Sweden 6 Monell Chemical Senses Center , Philadelphia, PA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Johan N Lundström Jian Zeng 5 Institute for Molecular Bioscience, University of Queensland , Brisbane, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jian Zeng Naomi Wray 5 Institute for Molecular Bioscience, University of Queensland , Brisbane, Australia 7 Department of Psychiatry, University of Oxford , Oxford, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Naomi Wray Yi Lu 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yi Lu Patrick F Sullivan 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet , Stockholm, Sweden 8 Departments of Genetics and Psychiatry, University of North Carolina , Chapel Hill, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Patrick F Sullivan For correspondence: jens.hjerling-leffler{at}ki.se pfsulliv{at}med.unc.edu Jens Hjerling-Leffler 1 Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jens Hjerling-Leffler For correspondence: jens.hjerling-leffler{at}ki.se pfsulliv{at}med.unc.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Understanding the temporal and spatial brain locations etiological for psychiatric disorders is essential for targeted neurobiological research. Integration of genomic insights from genome-wide association studies with single-cell transcriptomics is a powerful approach although past efforts have necessarily relied on mouse atlases. Leveraging a comprehensive atlas of the adult human brain, we prioritized cell types via the enrichment of SNP-heritabilities for brain diseases, disorders, and traits, progressing from individual cell types to brain regions. Our findings highlight specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals important patterns for schizophrenia with distinct subregions in the hippocampus and amygdala exhibiting the highest significance. Cerebral cortical regions display similar enrichments despite the known prefrontal dysfunction in those with schizophrenia highlighting the importance of subcortical connectivity. Using functional MRI connectivity from cases with schizophrenia and neurotypical controls, we identified brain networks that distinguished cases from controls that also confirmed involvement of the central and lateral amygdala, hippocampal body, and prefrontal cortex. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets. Introduction Genome-wide association studies (GWAS) have yielded fundamental insights into the nature of a wide range of human diseases, disorders, biomarkers, and traits. A recent summary 1 of 4,593 GWAS publications studying 3,908 phenotypes found 156,556 significant SNP-trait associations; notably, only 4.19% of significant SNPs were in a protein-coding region. GWAS have been particularly informative for psychiatric disorders whose enigmatic nature has long impeded progress. This body of work has shown that major psychiatric disorders are heritable, that clinically dissimilar disorders nonetheless have genetic overlap, and that this can clarify causality 2 – 6 . However, the genetic architectures of psychiatric disorders have proven to be particularly complex 7 . For example, predictions that genomic studies of schizophrenia would readily identify a few genes with near-causal effects 8 – 10 are inconsistent with the accumulated results: empirical studies of common genetic variation, rare copy number variation, rare exonic variation (both de novo and inherited), and whole genome sequencing 11 – 14 were well-powered to detect a few causal genes shared by most cases and yet none were identified. Compared to many other human diseases/disorders, schizophrenia is notably polygenic 15 with the major population impact resulting from inheritance of a large number of common variants of small effect 11 , 13 , 16 . Indeed, the most recent GWAS for schizophrenia 11 implicated 287 genomic loci (median size 652 kb, interquartile range, IQR, 238-652 kb) often intersecting multiple protein-coding genes (median 2, IQR 1-6, and 13% of all loci contained no protein-coding genes). It is highly likely that there are many more loci to be discovered. It is not clear how these findings inform understanding of the fundamental nature of schizophrenia or what the underlying neurobiology might be. In this paper, we evaluate the evidence that genomic results for brain disorders, diseases, and traits point at specific brain cell types. We evaluate the overarching hypothesis that cell types – and not a few major genes – are a principal readout of genomic studies for notably complex psychiatric disorders. We 17 , 18 and others 19 – 26 have previously evaluated this idea. However, for brain traits, the key limitations have been the sheer complexity of the brain and limited transcriptomic data that previously forced reliance on mouse brain transcriptomic surveys. Siletti et al. 27 recently published the most comprehensive human transcriptomic dataset to date: single-nucleus RNA sequencing (snRNAseq) of 3.369 million nuclei from 106 anatomical dissections within 10 brain regions. Here, we incorporate the first large-scale human brain atlas along with newer and larger GWAS. We extend our prior work by evaluating evidence for anatomical regions as well as their functional connectivity. Cell types form local networks that connect distributed brain regions. Functional magnetic resonance imaging (fMRI) is a non-invasive and widely used tool to evaluate brain regional functional connectivity in both health and disease. Systematic reviews have suggested disturbances in the Default Mode Network and the Core Network in cases with schizophrenia and their neurotypical relatives 28 – 30 . These findings suggest that genetic liability to schizophrenia can be manifest in empirically-defined cell types, anatomical regions, and in functional connectivity between brain regions. In this paper, we integrate fMRI data from schizophrenia cases and controls to illustrate how genetic information, via transcriptomics, agrees with fMRI in the prioritization of brain regions and changes in connectivity. Identifying affected brain regions and circuits is important given “interventional psychiatry” therapeutics that can modulate activity of specific brain regions (e.g., transcranial magnetic stimulation or deep brain stimulation). Results Our overarching goal was to evaluate whether the genomic regions identified by GWAS for complex brain phenotypes implicated specific brain cell types, anatomical regions, or their connectivity. As diagrammed in Figure 1 , we integrated the most comprehensive human snRNAseq brain atlas to date 27 with GWAS summary statistics for 36 primary traits including psychiatric disorders, brain traits, neurological diseases, structural MRI measures, and control traits ( Table S1 , Figure S1 , and Methods for inclusion criteria). We systematically processed summary statistics for these GWAS. Figure S2 shows the genetic correlations between the primary traits which were in accord with prior reports 3 . As in our past papers 17 , 18 , we used stratified LD score regression (S-LDSC) to estimate the enrichment of SNP-heritability for a trait in genes whose expression typified cell classes. The genetic liability of a trait can be measured by SNP-heritability, the proportion of phenotypic variance in a trait attributable to the additive genetic variation estimated from GWAS data 31 . Download figure Open in new tab Figure 1. Study schematic. We first identified cell types enriched for the SNP-heritability of 36 primary traits including major psychiatric disorders, using the most comprehensive Adult Human Brain Atlas. This was integrated with the cell type distribution across brain regions to identify brain regions enriched for the SNP-heritability of the traits. Finally, the regions suggested by cell-type-informed SNP-heritability enrichment were used to explore brain region connectivity that can differentiate schizophrenia from neurotypical controls in an independent sample. TDEP=top decile expression proportion, which were the most specifically expressed genes in each cell type (supercluster or cluster). Download figure Open in new tab Figure 2. Supercluster results. (A) SNP-heritability enrichment in 31 superclusters for five phenotype categories (psychiatric disorders, brain traits, neurological disorders, structural MRI, and control). Large dots indicate enrichment significance, FDR ≤ 0.05. Dot color indicates enrichment significance as –log10(P) with darker reds indicating greater significance. We interpret the suicide results cautiously as this GWAS did not clearly control for MDD 40 which may confound the enrichment pattern. mdd2019* summary statistics did not include data from 23andMe. (B) Conditional analysis of superclusters for enrichment of scz2022 SNP-heritability. The conditional analysis was performed in a pairwise fashion (Methods) for the significant superclusters. The y-axis is the supercluster of interest and the x-axis indicates the supercluster conditioned upon. For convenience, the unconditional results are on the diagonal. Significance was unchanged for deep-layer intratelencephalic, hippocampal CA1-3, amygdala excitatory, CGE interneuron, and eccentric medium spiny neuron, indicating the statistical independence of the results (Table S4). (C) SNP-heritability enrichment for MDD subtypes. Number of cases is shown in parenthesis in the x-axis label. Non-neuronal superclusters did not have signals for any subtype and were therefore omitted in the plot. Dot color and size are the same across panels A-C. (D) Ridge plot showing the density of the evolutionary constraint for TDEP genes of each supercluster. For each gene, the proportion of constraint 1 in its CDS bases was used as the measure of evolutionary constraint. The vertical dashed line shows the 80th percentile for evolutionary constraint for all protein-coding genes. The right column gives the proportion of TDEP genes above the 80th percentile of constraint. The plots were colored by the SNP-heritability enrichment for scz2022 (-log 10 FDR). Cellular diversity is hierarchically organized in the brain 32 , 33 , from a tripartite classification (neuronal excitatory, neuronal inhibitory, and non-neuronal) to higher-order cell superclusters that are divisible into clusters and subclusters/cell types. Following the Siletti nomenclature 27 , we analyzed 31 superclusters and their component 461 clusters. In Table S2 , we characterize the superclusters: 10 non-neuronal and 21 neuronal superclusters (13 excitatory, seven inhibitory, and one mixed neuronal supercluster). The supercluster labels capture major features but, inevitably for a complex tissue, some labels do not capture all features: for instance, “medium spiny neurons’’ and “eccentric medium spiny neurons” also contain cells from outside caudate and putamen (e.g., other long-range projecting inhibitory cells) and “amygdala excitatory neurons” also contain cells from paleocortex. Non-neuronal superclusters generally derived from dissections across the brain: e.g., astrocyte, ependymal, fibroblast, oligodendrocyte, and vascular cells were identified in many anatomical regions. Neuronal superclusters usually had a main anatomical region: e.g., deep-layer near-projecting and upper-layer intratelencephalic excitatory neurons were from neocortical dissections and the three hippocampal cell classes were from hippocampus (the chief exceptions were the more heterogeneous miscellaneous and splatter superclusters). We identified protein-coding genes whose expression was highly specific for each brain cell type as assessed by top decile expression proportion (TDEP or “gene specificity”). Li et al. (in preparation) determined that the TDEP approach combined with S-LDSC had power and false positive rates that were jointly equivalent or superior to eight other methods ( Methods ). We also compared gene selection using relative versus absolute expression (i.e., TDEP versus TPM, transcripts per million), and found that both yielded nearly identical high-dimensional visualizations ( Figure S3 ) but TPM was strongly influenced by broadly expressed “housekeeping genes” and this altered (and in some instances biased) gene ontology (GO) gene set analysis results ( Methods ). Download figure Open in new tab Figure 3. Cluster-level SNP-heritability enrichment for schizophrenia (scz2022). (A) tSNE plot from Siletti et al. colored by the significance of cluster-level SNP-heritability enrichment for scz2022. Gray indicates non-significance (FDR > 0.05). Abbreviations correspond to supercluster names in Figure 2A . AmygExc: Amygdala excitatory. DL-CT/6b: Deep-layer corticothalamic and 6b. DL-IT: Deep-layer intratelencephalic. DL-NP: Deep-layer near-projecting. CA1-3: Hippocampal CA1-3. CA4: Hippocampal CA4. HiDG: Hippocampal dentate gyrus. L RombLip: Lower rhombic lip. Mam: Mammillary body. Misc.: Miscellaneous. ThalExc: Thalamic excitatory. U RombLip: Upper rhombic lip. UL-IT: Upper-layer intratelencephalic. Cer-In: Cerebellar inhibitory. CGE-IN: CGE interneuron. eMSN: Eccentric medium spiny neuron. LLC-IN: LAMP5-LHX6 and Chandelier. MSC: Medium spiny neuron. MGE-IN: MGE interneuron. MB-In: Midbrain-derived inhibitory. (B) Top 25 significant clusters with the greatest scz2022 SNP-heritability enrichment, color as in Figure 2A . (C) Treemap plot for key GO-CC pathways in the top 25 scz2022 clusters. We performed gene set enrichment analysis for the TDEP genes in each of the top 25 significant clusters. Significantly enriched pathways in all the explored clusters were integrated to highlight higher level functions. The treemap for GO-BP and GO-MF for these clusters are shown in Figure S7 . We posit that TDEP genes for cell types are enriched for biological processes related to cellular identity and function. First, TDEP genes for different cell types generally had low overlap ( Figure S6 , median Jaccard index 0.049, IQR 0.022 - 0.099) but certain pairs had greater overlap (necessitating conditional analyses, Figure 2A ). Second, as expected, “housekeeping” genes (highly and consistently expressed across tissues) 34 were markedly less likely to be TDEP genes. Third, we conducted GO gene set analyses 35 for lists of the ∼1,300 TDEP genes per supercluster ( Table S2 ). Results for non-neuronal cells suggested a diverse range of significant GO terms consistent with the supercluster labels: astrocyte with biological adhesion, choroid plexus with cilium, microglia with immune response, oligodendrocyte with neuron ensheathment, oligodendrocyte precursor with gliogenesis, and vascular with vasculature development. GO terms for most neuronal cell types were dominated by synaptic biology, consistent with findings that forebrain neuronal cell identity is to a large extent driven by specific expression of synaptic genes 36 (exceptions were lower/upper rhombic lip and cerebellar inhibitory neurons from non-cortical regions). Fourth, the Methods section describes additional features of TDEP genes: (a) visualization of supercluster TDEP genes yielded groups of non-neuronal cells, neocortical excitatory neurons plus medium spiny neurons, and inhibitory interneurons plus non-cortical excitatory interneurons ( Figure S4A ); (b) TDEP genes tend to co-occur in genomic regions ( Figure S5 ); (c) visualizations for gene expression specificity and genomic co-occurrence were similar suggesting that TDEP genes tend to be located near each other; and (d) all neuronal TDEP genes accounted for 61-65% of the SNP-heritability for the largest brain trait GWAS (scz2020, bip2021, mdd2019*, neuroticism, education, and IQ). Identifying human brain cell types implicated by GWAS For each of the 36 primary GWAS, we estimated SNP-heritability enrichment for TDEP genes in each of the 31 superclusters (1,116 estimates). We used FDR correction for multiple comparisons per GWAS ( Figure 2A , Table S3 ). The P-values were not uniformly distributed (modes near 0 and 1) and 9.6% of all comparisons had FDR < 0.05. Of the non-neuronal superclusters, only microglia reached significance for any trait (lymphocyte count, neutrophil count, and multiple sclerosis). As in our prior reports 17 , 18 , non-neuronal superclusters were not significant for psychiatric disorders. In contrast, eight neuronal superclusters accounted for 60% of all significant enrichments. The numbers of superclusters with significant trait SNP-heritability enrichments were highly variable: (a) none of the structural MRI measures; (b) most neurological diseases had none (except for multiple sclerosis and epilepsy); (c) of the control traits, neutrophil and lymphocyte counts enriched for microglia and BMI enriched for two deep layer pyramidal cell superclusters and amygdalar excitatory neurons; (d) broad neocortical and non-cortical signals are also observed for other brain traits including educational attainment, IQ, neuroticism, and alcohol drinks per week with all neuronal neocortical superclusters significant for scz2022 and bip2021 (except for deep-layer near projecting). Non-cortical forebrain clusters from “amygdala excitatory” to “eccentric medium spiny neuron” showed strong signals for both scz2022 and bip202 ( Figure 2A ); and (e) the significant enrichments were dominated by six complex psychiatric disorder/brain traits. For the largest and most powerful GWAS traits (scz2022, bip2021, mdd2019, neuroticism, education, and IQ), the same eight neuronal superclusters had significant enrichment for all six traits. This agrees with the observation that schizophrenia, bipolar disorder, and MDD account for substantial morbidity and mortality and have considerable clinical and pharmacotherapeutic overlap (especially for severe and enduring forms of illness). Moreover, IQ, educational attainment, and the “Big 5” personality trait of neuroticism are important patient stratifiers and/or modifiers of clinical course 37 – 39 . The neuronal superclusters were five excitatory (amygdala excitatory, deep-layer intratelencephalic, hippocampal CA1-3, hippocampal CA4, and upper-layer intratelencephalic) and three inhibitory (CGE interneuron, eccentric medium spiny neuron, and LAMP5-LHX6 and chandelier). Excluding alternative explanations We evaluated a set of potential explanations for the observed overlap of eight superclusters with six GWAS traits. First, the genome-wide genetic correlations between the six traits were occasionally high but far from complete: for the 15 unique genetic correlations, the median |r g | was 0.22, IQR 0.15-0.39. The largest r g values were 0.73 for educational attainment-IQ, 0.69 for mdd2019-neuroticism, and 0.68 for bip2021-scz2022, and all other values were < 0.5. Second, the significant GWAS loci for these six traits only infrequently intersected with GWAS loci of more than one trait (median Jaccard index 0.069, IQR 0.034-0.094). Third, TDEP genes did not have fully explanatory overlap: of the 4,812 TDEP genes for the eight superclusters, 92.2% were TDEP for one (43.8%), two (24.7%), three (14.3%), or four (9.4%) superclusters. Fourth, we identified intersections of supercluster TDEP genes with GWAS loci, and found that TDEP genes (±50kb around each gene) only infrequently intersected more than one GWAS locus (7.6%). Finally and most directly, we conducted conditional analyses to evaluate the independence of the supercluster signals ( Figure 2B and Tables S4-S5 ). Briefly, we found relatively consistent patterns of independence: amygdala excitatory, deep-layer intratelencephalic, hippocampal CA1-3, CGE interneuron, and eccentric medium spiny neuron generally survived conditional analyses suggesting the independence of most of the SNP-heritability results. Thus, we could identify no alternative statistical explanation or dataset redundancy to explain away the observed overlaps in Figure 2A . Clinical subtyping MDD has the advantage of large GWAS on its clinical subtypes. For instance, SNP-heritability estimated from GWAS where cases are people with severe MDD receiving electroconvulsive therapy is greater than when estimated from GWAS where cases are identified by self-report, community, or outpatient sampling 41 . We compared the cell type enrichment between MDD subtypes viewed as clinically important (e.g., recurrent) or with empirical demonstration of greater heritability (e.g., highly severe MDD) with their counterparts 41 – 44 . Categories with any significant superclusters are presented in Figure 2C , including recurrent MDD, MDD with functional impairment, MDD with suicidal thoughts, and postpartum MDD. In general, more signals were found in the more severe subtypes. Although the MDD subtypes largely overlap with mdd2019, there were indications of specificity; e.g., hippocampal superclusters are more related to the severe/impaired MDD subtypes and neocortical superclusters are more related to MDD with suicidal thoughts. In Table S1 we provide the GWAS sample size and the number of genome-wide significant loci (both benchmarks of GWAS power) for all traits, and note that the MDD subtype GWAS are relatively underpowered. Evolutionary constraint has been of considerable interest given that SNP-heritability is notably enriched for this SNP annotation 1 . Figure 2D depicts the distributions of a gene-based measure of constraint for TDEP genes for superclusters (the fraction of all CDS bases under strong constraint in 240 eutherian mammals). TDEP genes for inhibitory superclusters are more constrained than those for excitatory superclusters (in line with previous reports) 45 , 46 . Schizophrenia has notable enrichment in evolutionary constrained genomic loci 1 , 11 ( Figure 2D ). Exome sequencing and neurodevelopmental disorders Table S6 presents analyses of supercluster TDEP genes where we evaluated gene annotations derived from LD-independent methods (e.g., whole exome sequencing). As a check, we found that TDEP genes for all superclusters were significantly less likely to be “housekeeping” genes, as expected given the definition of TDEP. There were no significant associations of any supercluster TDEP genes with genes implicated via whole exome sequencing for autism or schizophrenia 13 , 47 , but there were many associations for developmental delay and neurodevelopmental disorder (NDD) 47 . As the pattern of results was similar, we focused on NDD. NDD was significantly associated with TDEP genes for 15 of the 31 superclusters: (a) there were negative associations with Ependymal, Microglia, and Vascular superclusters (i.e., genes implicated in NDD were less likely to be TDEP genes); (b) there were eight significant associations with excitatory neuron superclusters (Amygdala excitatory, Deep-layer corticothalamic and 6b, Deep-layer intratelencephalic, Hippocampal CA1-3, Hippocampal CA4, Hippocampal dentate gyrus, Miscellaneous, and Upper-layer intratelencephalic); and (c) there were three significant associations with inhibitory neurons (Eccentric medium spiny neuron, LAMP5-LHX6 and Chandelier, and Midbrain-derived inhibitory). Notably, there was strong overlap of the NDD exome findings with the SNP-heritability enrichment for schizophrenia: of neuronal associations for NDD, 11 of 12 were also significant for schizophrenia and, of the associations with schizophrenia but not NDD, three of four were inhibitory neuronal superclusters. Clinically, a subset of people with schizophrenia have earlier NDD, and these results suggested that the two disorders may have important commonalities at a cell class level. Brain cell types implicated for schizophrenia Siletti et al. 27 also identified 461 clusters of cells; superclusters contained a median of 12 clusters (IQR 8-17, ranging from 1 for Bergman glia to 92 for Splatter neurons). We conducted TDEP/S-LDSC analyses at the cluster level for schizophrenia ( Tables S7-S8 ). The P-value distribution again had modes near 0 and 1. Of the 461 clusters, 199 (43.2%) had significant (FDR < 0.05) SNP-heritability enrichment for schizophrenia. There was a strong relationship of the SNP-heritability enrichment for schizophrenia in superclusters and their component clusters with most of the significant clusters in a significant supercluster (95.0%, 189/199). Of the 10 clusters not in a significant supercluster, Splatter 403 (GABAergic cells expressing NOS1 from amygdalar and paleocortical dissections) was exceptional (FDR 9.6e-5) and the rest had FDR values between 0.009–0.05 (eight neuronal clusters and one non-neuronal cluster with FDR = 0.04). We again find little common-variant genetic support for non-neuronal cells in schizophrenia. In Figure 3A , we visualized the supercluster and cluster findings for schizophrenia in the tSNE projection from Siletti et al. (their Figure 1B ). The uneven distribution of schizophrenia associations across superclusters is readily apparent. Most of the 25 strongest cluster associations (FDR < 5e-4) were from a few superclusters (Hippocampal CA1-3, Amygdala excitatory, and Eccentric medium spiny neuron; Figure 3B ). These clusters had a median of 1,274 TDEP genes (IQR 1,258–1,286) but with modest overlap between clusters (66.2% of unique genes were TDEP for ≤ 5 clusters). Gene set analysis of the TDEP genes in these clusters highlights synaptic function, cation channels, and neuron projection ( Figure 3C , S7 ). Analysis of anatomic regions shows distributed risk for schizophrenia risk across the brain Connecting genetic risk to specific brain regions is important for imaging (structural or functional MRI or PET) and for identifying empirical targets for Interventional Psychiatry therapeutics (e.g., transcranial magnetic stimulation). We evaluated the distributions of cell clusters across 104 dissections (2 dissections removed, Methods ) from 10 broad brain regions ( Figure 4A ) 27 . Neuronal clusters tended to be dissection-specific whereas non-neuronal clusters were widely distributed. As we observed a lack of signal in non-neuronal cell classes for psychiatric disorders ( Table S8 ), and to avoid bias from different neuron/glia composition ratios, we focused on neuronal clusters. To evaluate SNP-heritability enrichment for anatomic regions, we computed the neuronal cluster proportions per anatomical dissection as weights for the cluster-level enrichments; the sum of the weighted cluster-level enrichment was the enrichment per anatomical dissection ( Methods ). First, we observed general effects in the cerebral cortex for scz2022, bip2021, educational attainment, IQ, and neuroticism ( Figure 4B ). Hippocampal, amygdala, and striatal (Pu and CaB) regions were also significantly enriched for the SNP-heritability of these phenotypes but with greater variability. Regional differences in hippocampal enrichment is consistent with analyses in mouse brain 19 . Basal forebrain, thalamic, hypothalamic, cerebellum, and pons were enriched to lesser extents. To illustrate the distribution of genetic risk for schizophrenia across the brain, we depict the results using a 3D brain model ( Figure 4C-E ). Hippocampus and amygdala showed the highest significance of scz2022 SNP-heritability enrichment, with the top signal in the tail of hippocampus and the cortical amygdala (CoA). A detailed view of the neuronal cell type composition of the hippocampus and amygdala ( Figure 4F-G ) reveals that excitatory neuronal signals were the primary contributor to the hippocampal results. For amygdala, although the highest enrichment was found in the excitatory neurons, the inhibitory neurons had greater proportions and more significant enrichments than in the hippocampus (i.e., eccentric medium spiny neuron clusters). Download figure Open in new tab Figure 4. From clusters to anatomic dissections. (A) Heatmap of the scaled proportion of each cluster in each dissection. X-axis=clusters grouped and colored by superclusters. Abbreviations correspond to supercluster names in Figure 2A ; neuronal abbreviations are the same as Figure 3A ; non-neuronal abbreviations: Astr: Astrocyte. B: Bergmann glia. CPlx: Choroid plexus. COP: Committed oligodendrocyte precursor. Epend: Ependymal. Fibr: Fibroblast. MiG: Microglia. OPC: Oligodendrocyte precursor. Olig: Oligodendrocyte. Vasc: Vascular. Y-axis=brain anatomical dissections grouped to broader regions. Abbreviations: FrCx: frontal cortex, LimCx: limbic cortex, ParCx: parietal cortex, TempCx: temporal cortex, OccCx: occipital cortex, PalCx: paleocortex, HC: hippocampus, Amyg: amygdala, BasFB: basal forebrain, Thal: thalamus, HTH: hypothalamus, CBL: cerebellum, MidB: midbrain, Medul: medulla, SC: spinal cord. Each cell represents the scaled proportion of a cluster in a dissection. The proportions are the number of cells per cluster in a dissection divided by the total number of cells in the dissection; this number was then scaled to deciles for presentation clarity (Table S9). Nomenclature follows Siletti et al. 27. (B) Significance of S-LDSC SNP-heritability enrichment for anatomical dissections. The significance of enrichment at this was derived from cluster-level significance using the weighted-sum approach described in Methods. Dot color indicates level of significance in -log10(P), and darker blues indicate greater significance; dot size indicates the significance at FDR ≤ 0.05. Only phenotypes and brain regions with any significant signal are shown (Table S10). (C-E) Anatomic dissection results of scz2022 plotted on a 3D brain model (C-lateral view, D-sagittal view, E-enlargement of hippocampus and amygdala). Red indicates greater and yellow lesser significance at FDR ≤ 0.05, and gray and transparent indicates non-significance. Unsampled cerebral cortical regions are colored per the sampled regions (as mean of the enrichment Z-scores for sampled cerebral cortical regions). HiH: head of the hippocampus; HiB: body of the hippocampus; HiT: tail of the hippocampus; CoA: anterior cortical nucleus of the amygdala; La: lateral nucleus of the amygdala. (F-G) Greater detail for hippocampus and amygdala. The outer layer indicates clusters; the size is the proportion of the cluster, and the color indicates cluster-level significance of scz2022 SNP-heritability enrichment (color scale as in Figure 2B ). The clusters are organized by the superclusters and sorted by the enrichment significance clockwise. The middle layer is colored by superclusters, and the inner layer is colored by classes. Splatter and Miscellaneous have both excitatory and inhibitory components and were categorized as “Mix”. Connectivity differences for hippocampus, amygdala, and cerebral cortex in schizophrenia cases Although the prefrontal cortex, due to clear differences in those with schizophrenia, is the most studied brain region in schizophrenia, the cerebral cortex had consistently significant SNP-heritability enrichment. This, together with the prefrontal cortex having extensive connectivity with amygdala and hippocampus, suggested difference in the functional connectivity between the regions could contribute to schizophrenia mechanism, which we investigated using resting-state fMRI data from 46 cases with schizophrenia and 46 neurotypical controls ( Methods ) 48 . We initially prioritized 76 brain regions that were enriched of the schizophrenia SNP-heritability (FDR ≤ 0.01) ( Table S11, Methods ). We applied a deep neural network classifier to prioritize brain networks that distinguish cases from controls ( Figure 5A ). We randomly split the sample into five independent parts and performed five folds of parallel analyses, with four parts as the training set and one part as the test set ( Figure 5A ). We then performed recursive feature elimination such that the region with the lowest contribution was eliminated in the next iteration. Despite the limited sample size, four of five folds showed an upward trend for AUC ( Figure S8A, Table S12 ), which is in line with the expected performance of the feature elimination process. At similar network sizes, these data-driven networks performed as well as or better than previously established, schizophrenia-relevant brain networks (i.e., the default mode and core networks, Figure S8A ) 28 . Hippocampal and amygdalar regions presented more frequently in the models with AUC>0.5 ( Figure 5B ; Figure S8D ), and they were also enriched in regions with high numbers of connections ( Figure 5C ). To determine the most important connections across all the models in the five folds, we processed the feature importance of each pairwise connection ( Methods, Table S13 ) and plotted the regions containing the top 0.5% (n=14) connections. Of the prioritized regions, three were from the hippocampus (left body, right body, and right head of hippocampus) and four were from the amygdala (left central nuclear group and the left lateral nucleus). Other prioritized regions, included the frontal lobe (posterior intermediate orbital gyrus, left middle frontal gyrus, and right rostral gyrus), right anterior cingulate gyrus, and parietal operculum ( Figure 5D ). Download figure Open in new tab Figure 5. Functional connectivity networks derived from brain regions enriched for schizophrenia SNP-heritability distinguished cases and controls. (A) Workflow of the fMRI analysis, detailed in Method section. The gray dot to the left side of each portion indicates that the corresponding portion was used as the test set in the fold and that the rest were used as the training set. For each fold, the test set has always been kept separate from the training set, and across the folds, the test sets were independent from each other. (B) The top 20 regions that were most frequently observed in all the data-driven networks/models with AUC>0.5 across all folds. (C) The top 20 regions with the highest number of valid connections in the data-driven networks/models with AUC>0.5 across all folds. (D) Top 0.5% (n=14) connections across the data-driven networks/models with AUC>0.5 across all folds. Thicker segments indicate the top 4 connections/edges with the final edge strength>20; the rest connections/edges were marked with the thinner segments. Discussion Psychiatric genomics now has empirical data strongly supporting polygenicity: multiple risk variants in “many genes” underlie the inherited tendency of these psychiatric disorders to run in families. The “genetic architectures” 7 of schizophrenia, bipolar disorder, MDD, and other major psychiatric disorders – causes of considerable human suffering – are dominated by large numbers of common genetic variants of small effect 11 , 49 , 50 . The neurobiological implications of these secure and replicated genome findings are, however, unclear. In this paper, we rigorously evaluated the hypothesis that the accumulated findings implicate physically identifiable brain structures (i.e., cell types and anatomical regions). By necessity, our prior work was based on mouse brain atlases 17 , 18 and here we extend our work using a detailed and comprehensive human brain atlas 27 . In a data-driven model, we show that the functional connectivity network inferred from genetically implicated brain regions had increased capacity to distinguish schizophrenia cases from controls compared to previously defined networks. This finding suggests a potentially important convergence between genomic findings and functional connectivity. Human brain cell data with regional resolution Consistent with previous reports 17 , 18 , neuronal cell types had substantially increased SNP-heritability for psychiatric disorders (schizophrenia, bipolar disorder, and major depression) and brain traits (educational attainment, iq, neuroticism, insomnia, alcohol consumption, and smoking initiation). The anatomical data allowed detection of trait-relevant brain regions. Regional signals were distributed across the cerebral cortex and subcortical cerebral nuclei. While confirming previous results based on mouse scRNA-seq data for hippocampal and neocortical excitatory neurons 17 , 18 , based on human data we have identified novel relevant cell types, such as amygdala excitatory neurons, which were the most significantly enriched cell type in the entire brain as well as subcortical projecting GABAergic neurons for schizophrenia, which were undistinguishable in previous mouse datasets. Our study highlights neocortical interneurons derived from caudal ganglionic eminence which mainly contact other interneurons rather than interneurons expressing somatostatin or parvalbumin (although alterations of both of the latter have been reported in schizophrenia cases 51 , 52 ). Cross-disorder findings We observed broad involvement of brain regions in several psychiatric disorders and brain traits; at the same time, each phenotype had multiple supercluster-level signals. These signals were largely statistically independent ( Figure 2B , Table S4-5 ), suggesting different mechanistic contributions to the same cell types. Surprisingly, genes implicated by exome sequencing in neurodevelopmental disorders largely pointed at the same brain regions. Combined with our analyses of TDEP genes and GWAS loci, we believe that these results support cell types as contributing to phenotypically diverse traits. This suggests convergence, that these clinically distinctive phenotypes are rooted in different functional aspects of the same brain cell types. With the available GWAS for MDD subtypes, we were able to infer important cell types for clinical subtypes. We observed that more superclusters were implicated for severer subtypes of MDD. It is possible that more severe subtypes convey higher genetic risk and therefore greater statistical power in the GWAS. It is also possible that the different subtypes had partially distinct etiologies, as suggested by imperfect genetic correlations among subtypes 44 , 53 . More precise interpretations of the cell types can be made when larger subtype GWAS become available. At the level of brain regions, the results pointed out the importance of subcortical structures, especially the hippocampus and amygdala, underlying the mechanisms of pathological (e.g., schizophrenia) and healthy (e.g., educational attainment) phenotypes. The results in amygdala are in agreement with other findings that implicates changes in its structure in psychiatric disorders 54 – 56 . From a clinical perspective, amygdalar dysfunction agrees with decreased ability to ascribe correct valence and attention to sensory inputs 57 . Implications for schizophrenia Neocortical regions presented similar enrichments across the brain even though certain neocortical regions have been implicated in psychiatric disorders (e.g., dorsolateral prefrontal cortex and schizophrenia 58 ). This is likely explained by the similar cell type composition across the neocortical regions 59 , and highlights the importance of functional connectivity in the underlying mechanisms of schizophrenia 60 . The TDEP genes of the top scz2022 clusters highlighted synaptic functions and neuronal projection suggesting mechanistic connectivity between cells. fMRI connectivity networks that we constructed from the brain regions enriched for schizophrenia heritability were both similar (DMN) and different (Core Network) between cases and controls to those previously identified as schizophrenia-relevant 28 . Subcortical regions, although seldom studied together with neocortical regions in defined brain networks 61 , 62 , were highlighted as critical contributors in our data-driven, genetically implicated brain networks. Resting-state connectivity of subcortical structures has received relatively little attention in schizophrenia, although they connect to the neocortical regions and demonstrate a similar level of complexity 63 , 64 . Nevertheless, disturbed connection between the amygdala and the ventral prefrontal and the nearby orbitofrontal cortices has been reported in independent studies 65 , 66 . Across our data-driven models distinguishing between patients and controls, the hippocampal and amygdalar structures possessed connectivities between each other and to cortical regions. Both hippocampus and amygdala are involved in emotional memory processing, and a directed influence from the amygdala on the hippocampus has been suggested during fear processing in response to emotionally salient information 67 , 68 . Our method thus suggests reasonable brain regions and networks for schizophrenia etiology and calls for further investigations into these areas and their connectivities, which may hold new candidates for modulation using non-invasive therapeutics. Taken together, this study shows how genetic findings, combined with single-cell transcriptomics, can be used to prioritize not only cell types but brain regions and that these can be linked to disease relevant changes in functional connectivity. Our approach provides hope that the two modalities of genetics and brain imaging eventually are pointing towards the same targets. These results need to be considered with limitations (see also the Supplement of reference 17 ). First, brain regions were not equally sampled ( Table S14 ), despite the snRNA-seq dataset having the most comprehensive coverage of the adult human brain to date, and we cannot rule out enrichments of trait heritabilities in other brain regions. Second, the Human Brain Atlat is from a few adults and does not capture variability between neurotypical individuals or individuals with severe and enduring mental disorders or variability across the lifespan (especially during brain development). Third, TDEP is a relative measure that depends on which cell types are included. It is best applied in a comprehensive cell type atlas like the Human Brain Atlas and caution is warranted when comparing results from different cell type databases. Finally, we were unable to account for bilaterality given that snRNA-seq data were from the right hemisphere and the fMRI data were bilateral. In conclusion, our findings extend prior work by showing the human brain localization of genomic regions implicated in three psychiatric disorders, three relevant brain traits, and in genes implicated in neurodevelopmental disorders. The findings point at largely overlapping cell types and brain regions (albeit different subsets of genes). These findings provide a framework for understanding the polygenicity of complex psychiatric disorders and brain traits as well as suggesting hypotheses for future research. Our findings underscore the value of single-cell transcriptomics in decoding the polygenicity of psychiatric disorders and offer a promising convergence of genomic, transcriptomic, and brain imaging modalities toward common biological targets. Methods Human Brain Atlas single-nucleus RNA-seq (snRNAseq) We used the Human Brain Atlas snRNAseq data set from Siletti et al. 27 . This atlas consists of 3.369 million nuclei successfully sequenced using snRNAseq. The nuclei were from adult postmortem donors, and the dissections focused on 106 anatomical locations within 10 brain regions. Following quality control, the nuclear gene expression patterns allowed the identification of a hierarchy of cell types that were organized into 31 superclusters and 461 clusters. In the current paper we use the same naming system for the cell types and the brain regions as in Siletti et al. Genome reference and gene models The reference genome and gene models were with respect to a modified version 27 of the GENCODE primary assembly (GRCh38.p13, v35, 3/2020, hg38) 69 . As hg19 is typically used by GWAS, we also obtained GRCh37/hg19 gene coordinates from GENCODE (v35). In these analyses, we focused on 18,090 genes with these characteristics: protein-coding, mapped to canonical autosomes (chr1 to chr22), not in the extended major histocompatibility (MHC) region (chr6:25-34 mb), and expressed in ≥ 1 of the 461 cell clusters. Explanations for these choices follow. Protein-coding biotype The modified GENCODE assembly used by Siletti et al. 27 contained N=51,263 genes with TPM > 1 in one or more cluster cell types. In GENCODE, these genes are grouped into 30 biotypes ranging from rare (“scRNA” and “vault_RNA”) to common (“protein_coding” (N=19,153) and lncRNA (16,021)). Siletti et al. used the 10X Genomics Chromium Next GEM Single Cell 3’ Reagent Kits (v3) whose beads contain a 30 nt poly-dT tail and thus will most consistently capture 3’ poly-adenylated RNA transcripts (in humans, these include mature protein-coding and lncRNA transcripts). For each biotype, we summed the number of occurrences of any gene with TPM > 1 over all superclusters and found that only protein-coding and lncRNA genes had appreciable transcript detection. For instance, 20 biotypes had < 100 detected transcripts and 28 had < 8,600 detected transcripts in any supercluster. We chose to drop lncRNA genes and only include protein-coding genes. First, although a small number of lncRNAs have been shown to have biological functions, the annotation of most lncRNAs is currently unknown. In these data, 81.4% of the lncRNAs had a generic annotation (e.g., “novel transcript”). Second, the lncRNA were not strongly expressed and/or were not well-captured by the 10X Genomics kit: the largest median expression of lncRNAs in the superclusters was only 0.20 TPM (compared to 11.5 TPM for protein-coding genes). MHC The extended MHC (eMHC) is the largest block (∼8 mb) of high linkage disequilibrium (LD) in the genome (excluding pericentromeric regions) 70 . For instance, of the 23,731 significant SNP associations with schizophrenia, 4,527 (19.1%) are in the eMHC region 11 . These generally correspond to highly correlated genetic variants. We removed GWAS SNPs and snRNAseq data in the eMHC as in our prior papers and as recommended by the S-LDSC authors 17 , 18 , 71 . However, to evaluate the impact of this choice, we recalculated the TDEP estimates while including protein-coding eMHC expression data (N=259), and found that a small number of eMHC genes had a TDEP flag in superclusters (median 10 genes, IQR 8-13). As the median number of TDEP genes per supercluster was 1,287, the potential eMCH region contribution to a TDEP list is 0.8% (10/1287). The impact is likely not consequential. Autosome Sex chromosome genes were removed. chrY is rarely included in GWAS; in a recent build of the NHGRI/EBI GWAS Catalog 72 , there were only 5 significant SNP associations to any trait whereas a similarly sized chromosome (chr22) had >3,000 GWAS hits. In addition, chrX data are inconsistently included in the summary statistics from GWAS papers 73 , and are under-represented in the GWAS catalog: chrX has 1,149 hits whereas the similarly sized chr7 and chr8 have 9,438 and 9,745 associations. In a sense, the choice to exclude sex chromosomes was made for us as, for the GWAS traits we analyzed ( Table S1 ), none had chrY and a minority had chrX results. To evaluate the impact of this choice, we recalculated the TDEP estimates while including protein-coding chrX expression data (N=779) and found that some chrX genes had a TDEP flag in superclusters (median 49 genes, IQR 41-60). As the median number of TDEP genes per supercluster was 1,287, 49 genes (3.8%) may have had a small impact. We are unable to address this issue given the data available, and this is unquestionably a topic for future research. GWAS summary statistics We conducted multiple searches to identify potential GWAS (i.e., PubMed, Psychiatric Genomics Consortium downloads page, NHGRI/EBI GWAS catalog). We previously have shown the importance of genetic architecture on the informativeness of our approach (see 11 , 42 . The number of loci (genomic regions harboring multiple correlated genome-wide significant SNPs, defined below) is particularly important. We required > 10 loci for inclusion (with a few intentional exceptions). Table S1 summarizes the GWAS included in our analyses. These 36 primary GWAS are the largest studies per trait that we could obtain as of 4/2023 and whose use was compatible with our publication strategy (some prepublication GWAS required submission delays and others were not freely available, e.g. 23andMe). ● We included five psychiatric disorders (ADHD, bipolar disorder, major depressive disorder, problematic alcohol use, and schizophrenia). We did not include multiple important psychiatric disorders due to low numbers of loci (e.g., anorexia nervosa, autism). ● We included eight neurological diseases: Alzheimer’s disease, amyotrophic lateral sclerosis, epilepsy, hearing loss, migraine, multiple sclerosis, Parkinson’s disease, and stroke. For Parkinson’s disease, we used the results of Nalls et al. 2019 excluding 23andMe samples 74 . ● We included nine structural brain MRI measurements: brainstem volume, caudate volume, neocortical surface area, and putamen volume. Because these MRI measures describe important brain features (and often the anatomic regions from Siletti et al.), we also included accumbens volume, amygdala volume, neocortical thickness, pallidum volume, and thalamus volume. ● We included nine trans-diagnostic brain traits of clinical salience (alcohol use, smoking traits, and insomnia) or which may be clinical stratifiers (educational attainment, IQ, neuroticism). Suicide phenotypes were included due to their importance in the current mental health crisis. ● Finally, we selected five control traits with large numbers of loci but whose genetic architectures are not rooted in the central nervous system: height, body mass index, hematocrit, lymphocyte count, and neutrophil count. For MDD, we included 19 additional GWAS to assess within-disorder questions (no requirement for minimum number of associations; Table S1 ). As etiological heterogeneity is likely for depressive disorders, we evaluated whether heterogeneity was associated with different brain cellular enrichments. We focused on the clinical contexts in which a major depressive episode (MDE) can occur. The classical delineation of MDE is in the context of unipolar or bipolar disorder. An MDE can occur as major depressive disorder (MDD, unipolar MDE with no history of mania or hypomania), MDE with a history of mania (bipolar disorder type 1), and MDE with a history of hypomania (bipolar disorder type 2). These conditions have different genetic correlations with bipolar type 2 being more similar to MDD and bipolar type 1 being more similar to schizophrenia 49 . MDD can occur in different ways clinically and across the lifespan. We evaluated MDD subtypes viewed as clinically important (degree of severity, typical vs atypical symptom pattern, with or without comorbid anxiety disorder) or with empirical demonstration of greater heritability: highly severe MDD (people receiving electroconvulsive therapy for MDE), early-onset MDD, recurrent MDD, and postpartum depression 41 – 44 . Processing and quality control (QC) After we obtained GWAS results from the primary sources, we conducted range checks for logistic or multiple regression betas, standard errors, and P-values (removing SNPs with highly unlikely values). We then processed all sumstats using the cleansumstats pipeline ( https://github.com/BioPsyk/cleansumstats ): ● We determined genome build by comparing SNP positions to dbSNP (build 151) 75 ● Using UCSC::liftOver , we ensured we had sumstats in hg38/GRCh38 and hg19/GRCh37 coordinates (GWAS tend to use hg19 and genome annotations tend to use hg38) ● We removed insertion/deletion polymorphisms, duplicate entries, and chromosomal locations not in [chr1-chr22] and noting that sex chromosome data are inconsistently included in GWAS summary statistics 73 ● We required that each variant match dbSNP (build 151) by rsID and that the GWAS sumstats SNP alleles (effect/other allele) matched REF/ALT in dbSNP (flipping to + strand if required) ● We removed homozygous/monomorphic SNPs, SNPs with alleles not in [ACGT], and strand-ambiguous SNPs (A/T or C/G; these are also removed in LD score regression) ● Given our use of S-LDSC (below, and as typically done), we excluded the extended MHC region (chr6:25-34 mb) due to its exceptionally high LD Genomic loci We used the clumping algorithm in plink 76 to identify loci for the GWAS included in this report. The LD reference was the European subset of the 1000 Genomes Project (phase 3) 77 with parameters: p1=5e-8, p2=5e-6, r 2 =0.1, and window size of 3,000 kb. Overlapping loci and loci within 50 kb of each other were merged. Description of the primary GWAS We then conducted basic checks including the number of SNPs after QC, the number of genome-wide significant SNPs (P < 5e-8, after QC), inflation statistics (𝜆 and LDSC intercept), and SNP-heritability ( Table S1 ). For the primary GWAS traits, the numbers of significant loci were positively correlated with sample size (Spearman 𝜌 = 0.62, P = 4.9e-5) and the number of cases (binary traits, Spearman 𝜌 = 0.73, P = 0.0012). Figure S1 illustrates some key features of the GWAS included in the primary analyses. Figure S2 provides more data about the primary GWAS traits. The SNP-heritability estimates on the diagonal are consistent with the primary reports (any differences are due to our use of sample subsets like European subjects or after removing 23andMe results). The off-diagonal elements show the interrelationships of the primary GWAS traits via a heatmap of genetic correlations (rg from LDSC). The pattern of genetic correlations are consistent with prior reports 3 . In general, we note: (a) positive intercorrelations for psychiatric disorders and brain traits, (b) transdiagnostic negative correlations of educational attainment and IQ with multiple conditions, (c) relatively weak correlations for neurological diseases, and (d) isolated correlations for structural MRI measures. Relative versus absolute gene expression We use top decile of expression proportion (TDEP) to identify genes whose expression typifies each supercluster (∼1,300 genes per supercluster). Li et al. (in preparation) determined that S-LDSC with TDEP had power and false positive rates that, jointly, were equivalent or superior to 8 other methods. See the Statistical analysis section below for definition of TDEP, TPM, and the Li et al. results). Here, we compare relative vs absolute measures of gene expression. TDEP is a relative measure, the expression of a gene in one cell type divided by the total expression across all cell types. In contrast, TPM (snRNAseq count data in a cell type normalized to molecule transcripts per million) is more of an absolute method that reflects the number of RNA molecules in specific cells. We thus contrasted TDEP and TPM. \ First, as a basic data visualization, Figure S3 depicts the relation between TDEP and TPM for each of the 31 superclusters. For most superclusters, gene expression was greater in TDEP genes. This was particularly notable for non-neuronal superclusters where the median expression was far higher for TDEP genes (e.g., oligodendrocyte median 102.1 vs 18.9 TPM in TDEP genes vs all other genes). The excitatory neuronal superclusters had similar appearances in Figure S3 except for upper rhombic lip and lower rhombic lip being somewhat different. Inhibitory neuronal superclusters appeared relatively similar. Second, we created two data matrices; rows were 18,090 autosomal, protein-coding genes, columns were 31 supercluster classes, and the elements were either log 2 (TPM+1) or TDEP (1=yes, 0=no). The TPM matrix is obviously far more nuanced and detailed than the TDEP version). The results of UMAP/HDBSCAN are depicted in Figure S4 . In both instances, the high-dimensional data could be visualized as 3 distinct clusters. Cluster positions are arbitrary but the solutions are otherwise qualitatively similar, clusters containing: (a) all non-neuronal cells; (b) all neocortical excitatory neurons plus medium spiny neurons; and (c) all inhibitory neurons and non-cortical excitatory neurons. This is notable because TDEP faithfully recapitulates the multivariate structure of the supercluster data based on the more information-rich and full gene expression matrix based on TPM. The TDEP 0/1 flags efficiently capture the high-dimensional density structure of the TPM expression matrix. Third, we contrasted gene set analyses using GO 35 . The GO gene set analyses were based on TDEP genes and separately for top decile TPM. As both variables are defined by the deciles and coded TRUE/FALSE, similar numbers of genes are being compared. The background was 18,090 autosomal, protein-coding genes. These analyses yielded 322,462 comparisons (31 superclusters x 10,422 GO sets). The correlation in hypergeometric P-values for TDEP with top decile TPM was modest (Spearman 𝜌 = 0.414). For significance at FDR < 0.05, TDEP was more conservative than top decile TPM in implicating GO gene sets (3.27% vs 8.40%). Of all pathways, the two methods agreed for 92.2% (both non-significant for 291,094 or 90.30%, and both significant for 6,228 or 1.93%). There were fewer disagreements for TDEP==TRUE and top decile TPM==FALSE (4,329 or 1.34%) than the reverse (TDEP==FALSE and top decile TPM==TRUE, 20,811 or 6.45%). Checks of disagreements with top decile TPM FDR 0.5 (larger FDR applied to avoid edge cases) revealed some confusing results: e.g., synaptic genes sets with non-neuronal supercluster classes including astrocyte, Berman glia, oligodendrocyte, fibroblast, and vascular. These disagreements tended to be the same (i.e., about half of these pathways were implicated in ≥5 superclusters). We believe that the differences were strongly influenced by broadly (and often highly) expressed “housekeeping genes” that are prevalent in top decile TPM but not in TDEP (by definition). The top decile TPM gene set findings are in contrast to those for TDEP (presented in Table S2 ) that captured the expected (if not canonical) biological processes, cellular compartments, and molecular function of the 31 superclusters. Taken together, these results support our use of TDEP as a means to identify genes that are enriched for biological processes related to the cellular identity and specific function of superclusters. TDEP in human and mouse brain studies Our prior papers were based on scRNA-seq mouse neural surveys 17 , 18 with the key limitation of a necessary reliance on protein-coding genes with a high confidence, 1:1 mouse-human ortholog. Of the 18,090 genes we evaluated (autosomal, protein-coding, not in eMHC, TPM > 1 in ≥ 1 cluster), 14,398 (79.7%) had a high confidence, 1:1 mouse-human ortholog. As a sanity check, we compared TDEP genes for 23 mouse brain cell types used in Skene et al. 17 to the 31 Human Brain Atlas supercluster using hypergeometric gene set analysis. There was considerable consistency across these datasets despite different technologies and organisms. For example, there was the greatest overlap of: mouse “pyramidal CA1” with human hippocampal CA1-3 (fdr = 1e-140); mouse “pyramidal somatosensory” with human upper-layer intratelencephalic (fdr = 1e-134); mouse “oligodendrocyte” with human oligodendrocyte (fdr = 1e-183); and mouse “endothelial mural” with human vascular (fdr < 2e-208). As expected, the mouse signal for some cell types resolved into more precise human superclusters: mouse “interneurons” was associated with four human inhibitory neurons and the three mouse hypothalamic cell types contained human ependymal as well as excitatory and inhibitory neuronal TDEP genes. Summary Thus, we believe that TDEP is a defensible choice. Its relative nature can be a limitation in extreme instances but it is a principled and intentional choice that we evaluated extensively in this section. Further support can be found in method comparison studies: see discussion of Li et al. in the section titled “Choice of TDEP/S-LDSC”. The similarities in Figure S4 are reassuring and TDEP’s more conservative and the face-valid gene set results strengthen its appeal. Properties of Human Brain Atlas superclusters We use TDEP to identify genes whose expression typifies each supercluster (∼1,300 genes per supercluster). Multiple choices that we made in using TDEP are explained above, and the Statistical Analysis section below provides definitions and further justification. We posit that TDEP genes for a cell type are enriched for biological processes related to cellular identity and function, and we evaluated this assumption in multiple ways. Traditional classification and gene set analysis Table S2 characterizes the 31 supercluster cell classes: 10 non-neuronal and 21 neuronal cell classes (13 excitatory, 7 inhibitory, and 1 mixed neuronal class). Non-neuronal cell classes generally derived from dissections across the brain: e.g., astrocyte, ependymal, fibroblast, oligodendrocyte, and vascular cells were identified in many anatomical regions (with the exceptions of Bergmann glia and choroid plexus). Neuronal cell classes usually had a predominant anatomical region: e.g., deep-layer near-projecting and upper-layer intratelencephalic excitatory neurons from neocortical dissections and the 3 hippocampal cell classes were from hippocampus (the main exceptions were the miscellaneous and splatter). Table S2 contains Gene Ontology (GO) gene set analysis for TDEP genes 35 . Results for non-neuronal cells suggested a markedly diverse range of significant GO terms that were consistent with the supercluster labels: astrocyte/biological adhesion, choroid plexus/cilium, microglia/immune response, oligodendrocyte/ensheathment of neurons, oligodendrocyte precursor/gliogenesis, and vascular/vasculature development. In contrast, for most neuronal cell classes, GO terms focused directly on synaptic biology. A small set of genes (215, 1.19%) had TDEP in 10-14 supercluster classes. These genes contained multiple cadherins, calcium channel subunits, muscarinic receptors, GABA receptors, glutamate ionotropic and metabotropic receptors, potassium channel subunits, sodium channel subunits, synaptotagmins, and transmembrane proteins. Despite a small number of genes that usually limits gene set analysis, these 215 genes were enriched for 38 SynGO 78 synaptic cellular compartment and biological process annotations (e.g., presynapse P hyper = 3.9e-11 and postsynapse P hyper = 4.1e-11). We also addressed the inverse question, the 21.0% of genes that were not in a TDEP gene list for any supercluster. These genes were highly enriched for: (a) genes expressed at high and consistent levels across tissues (P hyper < 2.2e-308, a definition of “housekeeping” genes) 34 ; (b) evolutionarily constrained genes (P hyper = 1.6e-108) 1 ; (c) a range of GO biological process annotations pertaining to RNA processing, gene regulation, and cellular energetics (P hyper < 1e-40); and (d) notably, no synaptic processes (P hyper = 1) 78 . As expected, genes whose supercluster expression are non-specific were dominated by fundamental processes common to most cells and which tend to be highly constrained in placental mammals. Gene expression The Human Brain Atlas data 27 consist of snRNAseq on 3.369 million nuclei from adult postmortem donors and 106 anatomical locations within 10 brain regions that were then organized into 31 supercluster classes. We made a data frame with columns for the Ensembl gene identifier and each of the 31 superclusters along with 18,090 rows (for each autosomal, protein-coding gene expressed in ≥ 1 cluster). The elements are the expression of a gene in each cell class (as TPM, molecule transcripts per million). Figure S3 shows the relation between TPM and EP by supercluster class. At this level of analysis, there is considerable diversity in terms of the gene repertoire and expression level. Many of these genes will be responsible for core physiological processes and are robustly expressed in most cells (e.g., “housekeeping” genes). Genomic location and supercluster TDEP genes Genes are not randomly positioned in the human genome but rather show a marked tendency to occur in clumps. For instance, we can divide the genome into a regular set of 100 kb bins. After removing bins that were entirely composed of “N” (unknown) bases and after excluding chrX, chrY, and the extended MHC region (as noted above), there were 27,597 x 100 kb bins. We assigned the transcription start site (TSS) of 18,090 protein coding genes to these 100 kb bins, and tabulated the observed number of bins with 0, 1, 2, … TSS. We created an expectation using random sampling with replacement. In the human genome, we observed that 97.1% of all 100 kb bins have no TSS (i.e., 2.9% contain from 1-14 TSS). The observation is markedly different from the random expectation: the fraction of bins without a TSS ranged from 78.3-80.0% (1,000 trials). As the observed fraction of bins with no TSS (97.1%) was never approached, this implies that an empirical probability of this observation is far less than 0.001. If protein-coding genes clump or cluster together in the genome, then TDEP genes are likely to cluster as well, given that they are a subset of all protein-coding genes. We thus assessed whether TDEP genes co-occurred in excess of the fundamental clumping of protein-coding genes. For each of the 10,027 x 100 kb bins containing a TSS for a protein-coding gene, we tabulated the total number of protein-coding TSS (nTss) and the number of these that were TDEP genes (separately for each supercluster). We fit 31 linear regression models (nTdep i ∼ nTss) and saved the studentized residuals (i.e., transformed to mean 0 and standard deviation 1). The studentized residuals had minimum values > -3 and the 75th percentiles were around -0.2. However, for ∼14% of the bins, there were far more TDEP TSS than expected given the number of TSS (defined as studentized residuals > 3). To visualize these relationships, we computed the Spearman correlations for the studentized residuals of 31 supercluster cell classes and depicted the correlation matrix as a heatmap following hierarchical clustering ( Figure S5 ). Note that: (a) non-neuronal supercluster classes tend to correlate, specifically ependymal-choroid plexus, Bergman glia-astrocyte, oligodendrocytes, and vascular-fibroblast; and (b) neuronal cells classes clump in 2 groupings. The groupings in Figure S5 are strongly reminiscent of those in Figure S4 . We believe that this is notable given that the input data come from different sources (the latter from genomic location and the former from gene expression). This observation implies a role for co-expression of genes in genomic regions and supercluster identity. Statistical analysis for SNP-heritability enrichment Gene expression specificity/expression proportion We calculated gene expression specificity per cell type as expression proportions (EP). The following steps were done for cell types at the supercluster level (N = 31) and then at the cluster level (N = 461). For each cell type, we normalized the Siletti et al. 27 snRNAseq count data to molecule transcripts per million (TPM, equation I). We then computed EP per cell type as the normalized expression divided by the sum of normalized expression across cell types for each gene (equation II). As in our prior papers, we selected genes in the top decile of expression proportion (TDEP) per cell type with normalized expression >1 TPM. SNP-heritability enrichment in cell types Stratified LD score regression (S-LDSC) is widely used to evaluate whether a specific genome annotation is enriched for GWAS findings that contribute a greater proportion of SNP-heritability (also known as SNP-based heritability) to the common variant genetic architecture. It incorporates an empirical approach to LD correction via LD scores (the sums of local r 2 LD values) and includes multiple other genome features to increase model stability 71 , 79 . We used S-LDSC 79 to evaluate whether the set of ∼1,300 genes with top decile EP for each supercluster-level cell type (N = 31) or cluster-level cell type (N = 461) had significant SNP-heritability enrichment. Enrichment is calculated for the SNP-set relative to a null hypothesis that all SNP contribute equally to the SNP-heritability. Gene boundaries were expanded by ±100 kb. S-LDSC was run for each combination of GWAS summary statistics and cell type (at supercluster- and cluster-levels). We provide additional justification for these methodological choices below. As recommended, enrichment P-values were computed from the “Coefficient_z-score” 79 . For each GWAS trait, we adjusted for multiple comparisons using false discovery rate (FDR) using R::rstatix::adjust_pvalue(method="fdr”) . Choice of TDEP/S-LDSC Multiple groups have proposed algorithms by which to connect GWAS results to specific cell types. In addition to top-decile EP (TDEP)/S-LDSC, published methods include (in alphabetical order): CELLEX , DIALOGUE , EPIC , EWCE , MAGMA , RolyPoly, sc-linker , and scDRS 19 – 26 . These methods evaluate the association of GWAS signals with gene expression specificity in a given cell type as measured by single-cell or single-nucleus RNAseq. Co-authors Ang Li, Jian Zeng, and Naomi Wray (University of Queensland and Oxford University) have conducted a comparison of a representative set of these methods (manuscript in preparation). Broadly, the methods are based on SNP-level regression (e.g., LDSC), gene-level regression (e.g., MAGMA-set), and cell scoring methods (e.g., scDRS). Methods that use SNP-level or gene-level regression methods differ in their specificity metrics to determine gene sets per cell type from the RNAseq date before integration with GWAS summary statistics. In contrast, scDRS takes a set of associated genes for a trait from the GWAS summary statistics into analyses of the single-cell RNA-seq data. Li et al. compared the performance of 9 representative methods: the approach used here (TDEP/S-LDSC) 17 , 18 , MAGMA-set + EP, scDRS (using MAGMA-gene to select the top 1,000 genes), sc-linker, and 5 CELLEX statistics (DET, GES, EP w , NSI, ES µ ). They evaluated these methods with respect to empirical data using the default settings from each method: 18 GWAS trait/cell type pairs for which no association was expected (e.g., proerythroblast cell type and asthma) and 18 GWAS trait/cell type pairs for which there was independent evidence for association (e.g., proerythroblast cell type and red blood cell count). Application of these methods to empirical data sets allowed estimation of false positive control and power in real-world scenarios. In brief, Li et al. found: (a) the method we use in this report (TDEP/S-LDSC) had power and false positive rates as good as or better than other methods; (b) MAGMA-set had somewhat higher power but at the cost of high false positive rates (indeed, we observed that MAGMA-set can yield markedly discrepant evidence for GWAS-cell type linkages – e.g., P-values 5-10 logs smaller than TDEP/S-LDSC); and (c) use of scDRS was constrained by computational burden – use of all 3.3 million cells for one GWAS trait took ∼40 compute hours and 600 gb memory on a high-performance Linux cluster so that applying scDRS to the 36 primary GWAS was infeasible without down-sampling to a subset of nuclei. In addition, other authors have noted that TDEP performs well with respect to other expression metrics (Appendix 2, Figure 3 in reference 26 ). S-LDSC gene boundary expansion (±100 kb) Expanding gene boundaries by ±100 kb is often done and is generally consistent with the locations of promoters, enhancers, and eQTLs that impact gene expression 26 , 71 . We compared gene boundaries of ±100 kb and ±50 kb. For supercluster-level cell types and across the primary GWAS traits ( Table S1 ), the correlation between log 10 (enrichment-P) for ±100 kb vs ±50 kb was 0.988. We also calculated Cohen’s kappa for the significance of the results (FDR correction) between the two choices of windows [ using R::irr::kappa2() ]. Even considering the conservative impact of significance thresholding, Cohen’s kappa between the two windows was as high as 0.93. Given the small differences for these two gene expansion windows and to remain consistent with our prior papers 17 , 18 , we used gene boundaries ±100 kb. Conditional analysis of gene specificity overlap An important conceptual and practical issue is the degree of overlap in gene specificity between different cell types. We began by evaluating the overlap for all pairs of supercluster-level cell types. For the 435 unique supercluster pairs, the overlap was low (Jaccard index, JI, median 0.049, IQR 0.022-0.099, range 0.01-0.467). The lowest overlaps were for Amygdala excitatory-Vascular (JI = 0.01), Choroid plexus-Deep layer intratelencephalic (JI = 0.01), and Choroid plexus-Splatter (JI=0.01). The greatest overlaps were for Deep layer intratelencephalic-Upper layer intratelencephalic (JI = 0.467), Eccentric medium spiny neuron-Medium spiny neuron (JI = 0.372), and Astrocyte-Bergmann glia(JI = 0.370). As shown in Figure S6 there were a few instances with a modest degree of clustering. Although the overlaps were generally not marked over all pairs, all supercluster classes had at least one other class with potentially important overlap in specific genes; the maximum JI for each supercluster class had a median of 0.26 (IQR 0.19-0.33, range 0.12-0.47). Therefore, the S-LDSC SNP-heritability enrichment of one supercluster class could be dependent on another class with overlapping TDEP genes. To examine such dependency, we performed conditional analyses in a pairwise fashion. For each supercluster class, we added the TDEP genes of another class into the S-LDSC model and evaluated how that influenced the significance of enrichment for the supercluster of interest. If the result remained significant, it means that the enrichment for the supercluster of interest is statistically independent of the effect of the supercluster being conditioned upon. For instance, the signal for the “Upper layer intratelencephalic” class became non-significant after conditioning on the “Deep layer intratelencephalic” class ( Figure 3A ), suggesting that the signal from the former was statistically dependent on the latter potentially due to overlap between TDEP genes. On the other hand, the signal of “CGE interneuron” supercluster was statistically independent from those of “MGE interneuron” and “LAMP5-LHX6 and Chandelier” despite overlap in TDEP genes ( Figure S6 ). Gene set analysis Gene set analyses were conducted using hypergeometric tests versus a background of 18,090 genes. Summarizing text from the beginning of the Methods : the 18,090 genes are based on the GENCODE primary assembly (GRCh38.p13, v35, 3/2020, hg38) 27 , 69 . In these analyses, we focused on 18,090 genes that were protein-coding, mapped to canonical autosomes (chr1 to chr22), not in the extended MHC region (chr6:25-34 mb), and expressed in ≥ 1 of the 461 cell clusters. Hypergeometric P-values were FDR-corrected. The gene set analysis is performed for the TDEP genes of the 31 superclusters, as well as the top 25 clusters for schizophrenia. The major Gene Ontology (GO) terms in the gene sets enriched for the top 25 clusters for schizophrenia were summarized in treemaps using R::rrvgo. In addition to the Cellular Component (GO-CC) category ( Figure 3C ), Figures S7 present the Biological Processes (GO-BP) and Molecular Function (GO-MF) domains, respectively. Density-based visualization of high-dimensional data To improve understanding of these high dimensional data, we applied UMAP to visualize these data in two dimensions and HDBSCAN to identify groupings within the UMAP projection 80 , 81 . Analysis of brain anatomic dissections The dissection “HTHso” (supraoptic region of hypothalamus) had a high proportion of neocortical neurons, and “A35r” had a high proportion of non-neuronal cells 27 , suggesting potential contamination or technical issues with the dissection depth or margins. We therefore excluded HTHso and A35r and focused on 104 anatomic dissections. Anatomic dissections may contain highly heterogeneous cell type compositions 27 , and the function of the same genes can differ between cell types. Therefore, we deem that S-LDSC is most appropriate at cell type levels (i.e., superclusters and clusters) and evaluate enrichment at the level of dissections (or, detailed brain regions) by integrating the cluster-level enrichment. For visualizing cell type components in Figure 4A , we calculated the proportion of each cell cluster per brain dissection as the number of cells in the cluster in the dissection divided by the total number of cells in the dissection. This number was scaled into deciles for plotting. Table S9 presents the scaled proportion and the range and mean of the actual proportion. Of note, neuronal cell types are dominantly enriched for the SNP-heritability of psychiatric disorders, we therefore focused on the neuronal cells in calculating dissection-level enrichment. Specifically, for each anatomic dissection, the proportion of each neuronal cluster was calculated as the number of cells in the cluster in the dissection divided by the total number of neuronal cells in the dissection. Next, for each dissection, we weighted the cluster-level enrichment Z-score (from S-LDSC) of each neuronal cluster by its proportion within the dissection, and calculated the weighted sum as the Z-score of enrichment for the dissection. Finally, P-value for each dissection was calculated based on the weighted-summed Z-score. where k is a cluster, and 𝑝𝑐𝑡 6 is the percentage of cluster k in the dissection. Visualizing anatomic dissection results in a 3D human brain model The Allen Brain Atlas has a powerful 3D human brain model 82 . As the snRNA-seq dissections were performed according to the 2D Allen Brain Atlas, we had the opportunity to transfer the labels to the 3D map, although the names of the dissections were not completely matched between the 3D and 2D Atlas. We mapped 106 snRNA-seq dissections to 84 regions in the 3D brain model 83 via expert curation ( Table S14 ). The matched regions were used for visualizing dissection-level SNP-heritability results of schizophrenia ( Figure 4C-E ) and to obtain 3D coordinates in the fMRI analysis ( Figure 5 ). Each 3D label was assigned a value as the -log 10 (P) of scz2022 SNP-heritability enrichment of the corresponding anatomic dissection in the snRNA-seq dataset. If a 3D label corresponded to multiple snRNA-seq anatomic dissections, the most significant value was taken. Not all the cerebral cortical regions in the 3D model were sampled in the snRNA-seq dataset. Given our observation that the enrichment of scz2022 heritability was generally distributed across the cerebral dissections ( Figure 4B ), we assigned the unsampled neocortical regions the mean Z-score of all the sampled neocortical regions. For unsampled regions not in the cerebral cortex, we did not make any specific assumption, and their significance of enrichment was treated as missing. ITK_SNAP (v3.8.0) was used to visualize the 3D model 84 . Functional magnetic resonance imaging (fMRI) To evaluate our findings in the context of intrinsic functional connectivity, we compared patients with schizophrenia to neurotypical controls. Resting-state fMRI scans of 46 schizophrenia cases and 46 age- and sex-matched controls from the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study ( https://www.openfmri.org/dataset/ds000030/ ) 48 . From the public dataset, 50 cases with schizophrenia and 127 healthy controls (age: 21-50 years) were acquired. Functional scans were pre-processed, head motion-corrected, and normalized to MNI space (resolution of 3 mm 3 ) 85 . Framewise displacement (measuring the head motion during scanning) showed significantly more head motion for cases than controls. To minimize group differences, participants with framewise displacement >2mm were excluded. Next, we gradually excluded the healthy controls with fewer head motions until the difference between the groups was no longer significant. Finally, 46 patients with schizophrenia and 46 age- and sex-matched controls were included in the analysis. In order to define an initial search space in the fMRI imaging data, we started with 45 brain regions that were mapped to the 3D brain model and showed significant schizophrenia SNP-heritability enrichment at FDR ≤ 0.01 ( Table S15 ). We specified spherical regions of interest (ROIs) centering the MNI coordinates with a radius of 5mm. Regions that were too small to be identified from the fMRI data were removed, and overlapping ROIs were selected based on higher enrichment of schizophrenia SNP-heritability. Specifically, substantial overlap was found between the ROIs of the amygdala corticomedial nuclear group (CMN), namely the medial nucleus (Me), the amygdalohippocampal area (AHA), and the cortical amygdaloid nuclei (specifically the anterior part, CoA); we used the CoA (highest significance of schizophrenia SNP-heritability enrichment) to represent CMN. Likewise, the lateral nucleus (La) of the amygdala was prioritized over the nearby, overlapping structures basolateral nucleus (BL) and basomedial nucleus (BM), to represent the basolateral nuclear group (BLN). The ambiens gyrus (AG), frontal agranular insular cortex (FI), and claustrum (Cla) were too small for ROI identification and thus removed. This resulted in 38 unique brain ROIs; taking laterality into account, we considered 76 regions (38 in each hemisphere) in the following analysis. The resting state fMRI time-series of all voxels within each ROI were then averaged to obtain the regional time-series. The connectivity between two ROIs was defined as the Pearson’s correlation of the functional time-series of the two ROIs, and we fed the pairwise connectivities into a deep neural network classifier 86 as features to distinguish cases from controls using Braph2 87 . We randomly split the sample into five equal portions (10 cases and 10 controls in portion 1, and 9 cases and 9 controls per portion in portions 2-5) and ran five folds of parallel analyses. For each fold, we took one portion as the test set and the rest as the training set, and trained a deep neural network classifier using Braph2 to distinguish cases from controls. In each fold, recursive feature elimination strategy 88 was adopted to reduce the number of ROIs in a stepwise manner, where the least contributing ROI was removed from the next iteration. In each run, we applied a deep neural network classifier, Braph2 (an unpublished update of Braph 87 ), to model the network to distinguish cases from controls. The deep neural network classifier was trained with five-fold cross-validation on randomly shuffled data within the training set. We applied the trained network to the independent test set in the fold and acquired the area under curve (AUC) to evaluate the network. In total, we obtained 75 data-driven networks and their AUCs per fold. The same cross validation setup was applied to the defined brain regions for the Core and Default Mode Networks, without the recursive feature elimination process 29 . Regions included in these two networks are listed in Table S11 . Cross-entropy loss function was used to calculate the model error in the optimization of the neural network classifier. In each run, 1000 permutations were performed to evaluate the contribution of all features (i.e. the input ROI-to-ROI pairwise connections/edges) 89 . In this approach, a single feature value was randomly shuffled 1000 times, which in turn established the 95% confidence interval of the model error. If the model error of the original loss model fell outside the 95% confidence interval, the feature was considered as a contributing feature to the model. For a contributing feature, we calculated its relative feature importance (FI) as: where mean_e_perm is the averaged model error across the permutations, and e_orig is the original loss without any permutation. By doing this, we obtained a FI for each feature (i.e., ROI-to-ROI pairwise connection/edge) in each cross-validation and summed those FIs across cross-validations as the final relative FI for the feature. Since each ROI could have several contributing features and thus several FI, we summed up all final relative FI of each ROI as the measure of the contribution of the ROI to the classifier in the specific run. The ROI with the least contribution was removed from the input for the next iteration, which realized the recursive feature elimination. Given the variation in AUC ( Figure S8A , Table S12 ), which was likely to be attributable to the limited number in each test set, we opted to describe all models with AUC>0.5 instead of picking the model with highest AUC per fold. We focused the description on two levels, namely the level of regions and the level of connections. According to recessive feature elimination described above, regions with higher contributions tend to remain during the feature reduction. We therefore counted the frequency of each region across all folds along the feature reduction runs ( Figure S8B ) and among only models with AUC>0.5 ( Figure 5B ). As expected, hippocampal and amygdalar regions indeed presented more frequently in the process, which remained true when focusing on only models with AUC>0.5. The hippocampal and subcortical regions occupied greater portions in the top observed regions ( Figure S8D ), although they were only minorities in all the included regions in the models with AUC>0.5 ( Figure S8C ). Next, at the level of connections, we counted the number of valid connections per region among all the models with AUC>0.5, where valid connections were defined as final relative FI >0.5 (“final relative FI” was introduced in the paragraph above). Next, we calculate the strength of each pairwise connection/edge/feature as below: per model per edge, weight the final relative FI by the AUC; per fold per edge, sum up the weighted final relative FI; per fold, standardize the summed weighted final relative FI for all edges to mean=0 and standard deviation=1, so the measure became comparable between folds; and finally, per edge, sum across folds the summed weighted final relative FI to obtain the final edge strength. This allowed direct comparison between the pairwise connections. The top connections were enriched in the hippocampal, subcortical, and some cortical regions ( Figure S8E , Table S13 ). We plotted the distribution of these strength measures and applied a cutoff at 0.5% ( Figure S8F ), which resulted in 14 top connections to be plotted on the superior view of the brain ( Figure 5D ). Cell type and evolutionary constraint As described in Sullivan et al. 1 , we used the Zoonomia alignment of 241 placental mammals to create a gene constraint metric. In comparing multiple different constraint metrics, the simplest metric appeared to be the best (cdsFracCons, the number of constrained CDS bases divided by the total number of CDS bases). cdsFracCons does not have the limitations of alternative measures (e.g., pLI is close to a dichotomy and LOEUF has a strong residual correlation with CDS size) 90 , 91 . Code available at: https://github.com/Hjerling-Leffler-Lab/TDEP-sLDSC Download figure Open in new tab Figure S1. Depiction of primary GWAS trait features (data in Table S1). X-axis depicts liability-scale SNP-heritability estimated using LDSC. Y-axis is sample size on log 10 scale. Point colors correspond to disorder type. Point size shows quartiles of the number of LD-clumped loci: quartile 1 had 3-13 loci (e.g., insomnia, suicide attempt); quartile 2 had 13-37 loci (e.g., Alzheimer’s, epilepsy); quartile 3 had 40-103 loci (e.g., bipolar disorder, MDD); and quartile 4 had 180-2705 loci (BMI, educational attainment, height). Download figure Open in new tab Figure S2 Heatmap of genetic correlations (r g ) for the primary GWAS traits. Trait labels are defined in Table S1. We used LDSC to estimate SNP-heritability of each trait (shown on the diagonal) and genetic correlations between traits (off-diagonal values). Traits are grouped by disorder type. Colors indicate the strength and direction of genetic correlations. Asterisks indicate significant genetic correlation at FDR < 0.05 per trait. We have blanked out the genetic correlations of subcortical volumes with IQ, educational attainment, and alcohol use (the data owners prohibit use for “research into the genetics of intelligence, education, … or addictions”). Download figure Open in new tab Figure S3. Relation of TPM (x-axis) and expression proportion (EP, y-axis) separately for each of the 31 superclusters. Both axes were log transformed to add separation. Each point is a brain-expressed protein-coding gene. Green points show the top-decile EP (TDEP) genes for a supercluster, and gray all other genes. The blue line is a lowess smoother. Download figure Open in new tab Figure S4. UMAP (n_neighbors=5) / HDBSCAN (minPts = 5) analysis. Panel A is for TDEP and panel B for log2(TMP+1). Download figure Open in new tab Figure S5. Genomic bin analysis. For each supercluster and 100 kg genomic bins, we numerically assessed the degree to which the observed number of TDEP gene TSS per bin deviated from the total number of protein-coding TSS per bin (studentized residuals). The above is a heatmap of the Spearman correlation matrix (following hierarchical clustering). Download figure Open in new tab Figure S6. Jaccard index heatmap. For all pairs of superclusters, we computed the Jaccard index for overlap of TDEP genes (these gene sets are the input annotation for S-LDSC). For two sets of genes, Jaccard index of 1 means complete overlap and 0 means no overlap. Download figure Open in new tab Figure S7. Treemap for gene ontology (GO) gene set enrichment for the TDEP genes of the top 25 clusters enriched of schizophrenia SNP-heritability. (A) GO-BP gene sets enriched in the clusters. (B) GO-MF gene sets enriched in the clusters. Download figure Open in new tab Figure S8. fMRI analysis supplementary figures. (A) AUC of each model across the 5 parallel folds. Round dots are AUCs of the data-driven networks/models. Triangle dots are the AUCs of the core network, and the square dots are the AUCs of the default mode network (DMN); these two reference networks were included once each in every fold and they were plotted at the Run (x-axis) that corresponds to their sizes. (B) Regions ranked by the total number of appearances across all 5 folds in each run. The shade indicates the number of appearances, and the color indicates the broader brain areas. (C)-(D): pie-charts showing the proportion of each broader brain area in all regions in models with AUC>0.5 (C) and in the top 20 regions in these models (D), which corresponds to the bar plot in Figure 5B . Although hippocampal and amygdalar regions were relatively small portions of all the regions, they occupied larger portions among the top important regions. (E) Heatmap of the pairwise connections included in all models with AUC>0.5. The strength of each connection/edge was calculated as below: per model per edge, weight the final relative FI by the AUC; per fold per edge, sum up the weighted final relative FI; per fold, standardize the summed weighted final relative FI for all edges to mean=0 and standard deviation=1, so the measures are comparable between folds; and finally, per edge, sum across folds the summed weighted final relative FI to obtain the final edge strength. (F) The density plot of the final edge strength for all the edges/connections/features in panel (E). We applied a cutoff of the top 0.5% to select the most important edges/connections/features for plotting in Figure 5D . View this table: View inline View popup Download powerpoint Table S1. Summary of GWAS included. The image shows 10 rows. The full table is in the supplemental-tables file. Data Availability All data produced in the present study are available upon reasonable request to the authors https://github.com/Hjerling-Leffler-Lab/TDEP-sLDSC Potential conflicts of interest PFS is a scientific consultant and shareholder for Neumora Therapeutics. Collaborators International Suicide Genetics Consortium Anna R Docherty 1,2,3 , Niamh Mullins 4,5 , Allison E Ashley-Koch 6 , Xuejun Qin 6 , Jonathan R I Coleman 7,8 , Andrey Shabalin 1,2 , JooEun Kang 9 , Balasz Murnyak 1,2 , Frank Wendt 10 , Mark Adams 11 , Adrian I Campos12,13 , Emily DiBlasi 1,2 , Janice M Fullerton 14,15 , Henry R Kranzler 16,17 , Amanda Bakian 2 , Eric T Monson 2 , Miguel E Rentería 12,18 , Consuelo Walss-Bass 19 , Ole A Andreassen 20,21 , Cynthia M Bulik 22,23,24 , Howard J Edenberg 25,26 , Ronald C Kessler 27 , J John Mann 28 , John I Nurnberger Jr 29,29 , Giorgio Pistis 30 , Fabian Streit 31 , Robert J Ursano 32 , Renato Polimonti 10 , Michelle Dennis 33 , Melanie Garrett 34 , Lauren Hair 35 , Philip Harvey 36 , Elizabeth R Hauser 6,37 , Michael A Hauser 6 , Jennifer Huffman 38 , Daniel Jacobson 39 , Jennifer H Lindquist 40 , Ravi Madduri 41 , Benjamin McMahon 42 , David W Oslin 43,44 , Jodie Trafton 45 , Swapnil Awasthi 46 , Andrew W Bergen 47,48 , Wade H Berrettini 49 , Martin Bohus 50 , Harry Brandt 51,52 , Xiao Chang 53 , Hsi-Chung Chen 54 , Wei J Chen 54,55,56 , Erik D Christensen 57,58 , Steven Crawford 51,52 , Scott Crow 59 , Philibert Duriez 60,61 , Alexis C Edwards 3 , Fernando Fernández-Aranda 62 , Manfred M Fichter 63,64 , Hanga Galfalvy 65,66 , Steven Gallinger 67 , Michael Gandal 68 , Philip Gorwood 60,61 , Yiran Guo 53 , Jonathan D Hafferty 11 , Hakon Hakonarson 53,69 , Katherine A Halmi 70 , Akitoyo Hishimoto 71 , Sonia Jain 72 , Stéphane Jamain 73 , Susana Jiménez-Murcia 62 , Craig Johnson 74 , Allan S Kaplan 75,76,77 , Walter H Kaye 78 , Pamela K Keel 79 , James L Kennedy 75,76,77 , Minsoo Kim 68 , Kelly L Klump 80 , Daniel F Levey 81,82 , Dong Li 53 , Shih-Cheng Liao 54 , Klaus Lieb 83 , Lisa Lilenfeld 84 , Adriana Lori 85 , Pierre J Magistretti 86,87 , Christian R Marshall 88 , James E Mitchell 89 , Richard M Myers 90 , Satoshi Okazaki 91 , Ikuo Otsuka 66,91 , Dalila Pinto 4,5 , Abigail Powers 85 , Nicolas Ramoz 61 , Stephan Ripke 46,92,93 , Stefan Roepke 94 , Vsevolod Rozanov 95,96 , Stephen W Scherer 97,98 , Christian Schmahl 50 , Marcus Sokolowski 99 , Anna Starnawska 100,101,102,103 , Michael Strober 104,105 , Mei-Hsin Su 56 , Laura M Thornton 24 , Janet Treasure 106,107 , Erin B Ware 108,109 , Hunna J Watson 24,110,111 , Stephanie H Witt 31 , D Blake Woodside 76,77,112,113 , Zeynep Yilmaz 24,114,115 , Lea Zillich 31 , Rolf Adolfsson 116 , Ingrid Agartz 117,118,119 , Tracy M Air 120 , Martin Alda 121,122 , Lars Alfredsson 123,124 , Adebayo Anjorin 125 , Vivek Appadurai 126,127 , María Soler Artigas 128,129,130,131 , Sandra Van der Auwera 132,133 , M Helena Azevedo 134 , Nicholas Bass 135 , Claiton HD Bau 136,137 , Bernhard T Baune 138,139 , Frank Bellivier 140,141,142,143 , Klaus Berger 144 , Joanna M Biernacka 145 , Tim B Bigdeli 3,146 , Elisabeth B Binder 85,147 , Michael Boehnke 148 , Marco P Boks 149 , Rosa Bosch 128,129,150 , David L Braff 151 , Richard Bryant 152 , Monika Budde 153 , Enda M Byrne 13,154 , Wiepke Cahn 155 , Miguel Casas 128,129,131,150 , Enrique Castelao 30 , Jorge A Cervilla 156 , Boris Chaumette 157,158,159 , Sven Cichon 160,161,162,163 , Aiden Corvin 164 , Nicholas Craddock 165 , David Craig 166 , Franziska Degenhardt 163 , Srdjan Djurovic 167,168 , Ayman H Fanous 3,146 , Jerome C Foo 169 , Andreas J Forstner 160,163,170 , Mark Frye 171 , Justine M Gatt 14,152 , Pablo V Gejman 172,173 , Ina Giegling 174,175 , Hans J Grabe 132,133 , Melissa J Green 14,176 , Eugenio H Grevet 177,178 , Maria Grigoroiu-Serbanescu 179 , Blanca Gutierrez 180 , Jose Guzman-Parra 181 , Steven P Hamilton 182 , Marian L Hamshere 165 , Annette M Hartmann 174 , Joanna Hauser 183 , Stefanie Heilmann-Heimbach 163 , Per Hoffmann 161,162,163 , Marcus Ising 184 , Ian Jones 165 , Lisa A Jones 185 , Lina Jonsson 186 , René S Kahn 5,187 , John R Kelsoe 151,188 , Kenneth S Kendler 3 , Stefan Kloiber 75,184,189 , Karestan C Koenen 92,190,191 , Manolis Kogevinas 192 , Bettina Konte 174 , Marie-Odile Krebs 157,158,159 , Mikael Landén 22,193 , Jacob Lawrence 194 , Marion Leboyer 195,196,197 , Phil H Lee 92,93,198 , Douglas F Levinson 199 , Calwing Liao 200,201 , Jolanta Lissowska 202 , Susanne Lucae 184 , Fermin Mayoral 181 , Susan L McElroy 203 , Patrick McGrath 204 , Peter McGuffin 8 , Andrew McQuillin 135 , Divya Mehta 205,206 , Ingrid Melle 20,207 , Yuri Milaneschi 208 , Philip B Mitchell 176 , Esther Molina 209 , Gunnar Morken 210,211 , Preben Bo Mortensen 101,114,127,212 , Bertram Müller-Myhsok 147,213,214 , Caroline Nievergelt 151 , Vishwajit Nimgaonkar 215 , Markus M Nöthen 163 , Michael C O’Donovan 165 , Roel A Ophoff 68,216 , Michael J Owen 165 , Carlos Pato 217,217 , Michele T Pato 218 , Brenda WJH Penninx 219 , Jonathan Pimm 135 , James B Potash 220 , Robert A Power 8,221,222 , Martin Preisig 30 , Digby Quested 223 , Josep Antoni Ramos-Quiroga 128,129,131,150 , Andreas Reif 224 , Marta Ribasés 128,129,130,131 , Vanesa Richarte 128,129,150 , Marcella Rietschel 225 , Margarita Rivera 8,226 , Andrea Roberts 227 , Gloria Roberts 176 , Guy A Rouleau 228,229 , Diego L Rovaris 230 , Dan Rujescu 174 , Cristina Sánchez-Mora 128,129,130,131 , Alan R Sanders 172,173 , Peter R Schofield 14,15 , Thomas G Schulze 153,169,231,232,233 , Laura J Scott 148 , Alessandro Serretti 234 , Jianxin Shi 235 , Stanley I Shyn 236 , Lea Sirignano 169 , Pamela Sklar 4,5,237 , Olav B Smeland 20,21 , Jordan W Smoller 92,191,238 , Edmund J S Sonuga-Barke 239 , Gianfranco Spalletta 240,241 , John S Strauss 75,189 , Beata Świątkowska 242 , Maciej Trzaskowski 13 , Ming T Tsuang 243 , Gustavo Turecki 244 , Laura Vilar-Ribó 128,131 , John B Vincent 245 , Henry Völzke 246 , James TR Walters 165 , Cynthia Shannon Weickert 14,176 , Thomas W Weickert 14,176 , Myrna M Weissman 247,248 , Leanne M Williams 249 , Naomi R Wray 13,206 , Clement C Za i 92,189,190,250,251,252 , Esben Agerbo 114,212,253 , Anders D Børglum 100,101,102,103 , Gerome Breen 7,8 , Ditte Demontis 100,101,102,103 , Annette Erlangsen 103,254,255,256 , Tõnu Esko 257,258 , Joel Gelernter 81,82 , Stephen J Glatt 259 , David M Hougaard 253,260 , Hai-Gwo Hwu 261 , Po-Hsiu Kuo 54,56 , Cathryn M Lewis 8,262 , Qingqin S Li 263 , Chih-Min Liu 54 , Nicholas G Martin 12 , Andrew M McIntosh 11 , Sarah E Medland 12 , Ole Mors 253,264 , Merete Nordentoft 253,265 , Catherine M Olsen 266 , David Porteous 267 , Daniel J Smith 268 , Eli A Stahl 4,257,269 , Murray B Stein 270 , Danuta Wasserman 99 , Thomas Werge 126,253,271,272 , David C Whiteman 266 , Virginia Willour 273 , the VA Million Veteran Program (MVP), the MVP Suicide Exemplar Workgroup, Suicide Working Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Eating Disorder Working Group of the Psychiatric Genomics Consortium, German Borderline Genomics Consortium, Hilary Coon 1,2,274 , Jean C Beckham 275,276 , Nathan A Kimbrel 275,276 , Douglas M Ruderfer 9,277,278 1 Huntsman Mental Health Institute, Salt Lake City, UT, USA 2 University of Utah School of Medicine, Department of Psychiatry, Salt Lake City, UT, USA 3 Virginia Commonwealth University, Department of Psychiatry, Richmond, VA, USA 4 Icahn School of Medicine at Mount Sinai, Department of Genetics and Genomic Sciences, New York, NY, USA 5 Icahn School of Medicine at Mount Sinai, Department of Psychiatry, New York, NY, USA 6 Duke University Medical Center, Duke Molecular Physiology Institute, Durham, NC, USA 7 King’s College London, National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK 8 King’s College London, Social Genetic and Developmental Psychiatry Centre, London, UK 9 Vanderbilt University Medical Center, Division of Genetic Medicine, Department of Medicine, Vanderbilt Genetics Institute, Nashville, TN, USA 10 Yale University School of Medicine, Department of Psychiatry, New Haven, CT, USA 11 University of Edinburgh, Division of Psychiatry, Edinburgh, UK 12 QIMR Berghofer Medical Research Institute, Mental Health and Neuroscience Research Program, Brisbane, QLD, Australia 13 The University of Queensland, Institute for Molecular Bioscience, Brisbane, QLD, Australia 14 Neuroscience Research Australia, Sydney, NSW, Australia 15 University of New South Wales, School of Medical Sciences, Sydney, NSW, Australia 16 University of Pennsylvania Perelman School of Medicine, Department of Psychiatry, Philadelphia, PA, USA 17 Crescenz VAMC, VISN 4 MIRECC, Philadelphia, PA, USA 18 The University of Queensland, School of Biomedical Sciences, Faculty of Medicine, Brisbane, QLD, Australia 19 University of Texas Health Science Center, Department of Psychiatry and Behavioral Sciences, Houston, TX, USA 20 Oslo University Hospital, Division of Mental Health and Addiction, Oslo, Norway 21 University of Oslo, NORMENT, Oslo, Norway 22 Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Stockholm, Sweden 23 University of North Carolina at Chapel Hill, Department of Nutrition, Chapel Hill, NC, USA 24 University of North Carolina at Chapel Hill, Department of Psychiatry, Chapel Hill, NC, USA 25 Indiana University, Department of Medical & Molecular Genetics, Indianapolis, IN, USA 26 Indiana University School of Medicine, Biochemistry and Molecular Biology, Indianapolis, IN, USA 27 Harvard Medical School, Department of Health Care Policy, Boston, MA, USA 28 Columbia University, Departments of Psychiatry and Radiology, New York, NY, USA 29 Indiana University School of Medicine, Departments of Psychiatry and Medical and Molecular Genetics, Indianapolis, IN, USA 30 Lausanne University Hospital and University of Lausanne, Department of Psychiatry, Lausanne, Vaud, Switzerland 31 Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Department of Genetic Epidemiology in Psychiatry, Mannheim, Germany 32 Uniformed Services University of the Health Sciences, Department of Psychiatry, Bethesda, MD, USA 33 Duke University Medical Center, Department of Psychiatry and Behavioral Sciences, Durham, NC, USA 34 Duke University Medical Center, Durham, NC, USA 35 Durham Veterans Affairs Health Care System, Durham, NC, USA 36 Miami VA Health Care System, Miami, FL, USA 37 Durham Veterans Affairs Health Care System, Cooperative Studies Program Epidemiology Center, Durham, NC, USA 38 Boston VA Health Care System, Boston, MA, USA 39 Oak Ridge National Laboratory, Oak Ridge, TN, USA 40 Durham Veterans Affairs Health Care System, VA Health Services Research and Development Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC, USA 41 Argonne National Laboratory, University of Chicago Consortium for Advanced Science and Engineering, Chicago, IL, USA 42 Los Alamos National Laboratory, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA 43 Corporal Michael J. Crescenz VA Medical Center, VISN 4 Mental Illness Research, Education, and Clinical Center, Philadelphia, PA, USA 44 Perelman School of Medicine, University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, USA 45 VA Palo Alto Health Care System, VA Program Evaluation and Resource Center, Palo Alto, CA, USA 46 Charité - Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Berlin, Germany 47 BioRealm, LLC, Walnut, CA, USA 48 Oregon Research Institute, Eugene, OR, USA 49 Perelman School of Medicine at the University of Pennsylvania, Department of Psychiatry, Center for Neurobiology and Behavior, Philadelphia, PA, USA 50 Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Department of Psychosomatic Medicine and Psychotherapy, Mannheim, Germany 51 ERCPathlight, Baltimore, MD, USA 52 University of Maryland St. Joseph Medical Center, Baltimore, MD, USA 53 Children’s Hospital of Philadelphia, Center for Applied Genomics, Philadelphia, PA, USA 54 National Taiwan University Hospital, Department of Psychiatry, Taipei, Taiwan 55 National Health Research Institutes, Center for Neuropsychiatric Research, Miaoli County, Taiwan 56 National Taiwan University, Institute of Epidemiology and Preventive Medicine, College of Public Health, Taipei, Taiwan 57 Utah Department of Health and Human Services, Utah Office of the Medical Examiner, Taylorsville, UT, USA 58 University of Utah, Department of Pathology, Salt Lake City, UT, USA 59 University of Minnesota, Department of Psychiatry, Minneapolis, MN, USA 60 GHU Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, Paris, France 61 Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Paris, France 62 University Hospital Bellvitge-IDIBELL and CIBEROBN, Department of Psychiatry, Barcelona, Spain 63 Ludwig-Maximilians-University (LMU), Department of Psychiatry and Psychotherapy, Munich, Germany 64 Schön Klinik Roseneck affiliated with the Medical Faculty of the University of Munich (LMU), Munich, Germany 65 Columbia University, Department of Biostatistics, New York, NY, USA 66 Columbia University, Department of Psychiatry, New York, NY, USA 67 University of Toronto, Department of Surgery, Faculty of Medicine, Toronto, Canada 68 University of California, Los Angeles, Department of Psychiatry and Biobehavioral Science, Semel Institute, David Geffen School of Medicine, Los Angeles, CA, USA 69 University of Pennsylvania, The Perelman School of Medicine, Philadelphia, PA, USA 70 Weill Cornell Medical College, Department of Psychiatry, New York, NY, USA 71 Yokohama City University Graduate School of Medicine, Department of Psychiatry, Yokohama, Japan 72 University of California San Diego, Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, La Jolla, CA, USA 73 Univ Paris-Est-Créteil, INSERM, IMRB, Translational Neuropsychiatry, Fondation FondaMental, Créteil, France 74 Eating Recovery Center, Denver, CO, USA 75 Centre for Addiction and Mental Health, Toronto, ON, Canada 76 University of Toronto, Department of Psychiatry, Toronto, Canada 77 University of Toronto, Institute of Medical Science, Toronto, Canada 78 University of California San Diego, Department of Psychiatry, San Diego, CA, USA 79 Florida State University, Department of Psychology, Tallahassee, FL, USA 80 Michigan State University, Department of Psychology, Lansing, MI, USA 81 Veterans Affairs Connecticut Healthcare Center, Department of Psychiatry, West Haven, CT, USA 82 Yale University School of Medicine, Division of Human Genetics, Department of Psychiatry, New Haven, CT, USA 83 University Medical Center, Department of Psychiatry and Psychotherapy, Mainz, Germany 84 The Chicago School of Professional Psychology, Washington DC, Department of Clinical Psychology, Washington, DC, USA 85 Emory University School of Medicine, Department of Psychiatry and Behavioral Sciences, Atlanta, GA, USA 86 King Abdullah University of Science and Technology, BESE Division, Thuwal, Saudi Arabia 87 University of Lausanne-University Hospital of Lausanne (UNIL-CHUV), Department of Psychiatry, Lausanne, Switzerland 88 The Hospital for Sick Children, Department of Paediatric Laboratory Medicine, Toronto, Canada 89 University of North Dakota School of Medicine and Health Sciences, Department of Psychiatry and Behavioral Science, Fargo, ND, USA 90 HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA 91 Kobe University Graduate School of Medicine, Department of Psychiatry, Kobe, Japan 92 Broad Institute, Stanley Center for Psychiatric Research, Cambridge, MA, USA 93 Massachusetts General Hospital, Analytical and Translational Genetics Unit, Boston, MA, USA 94 Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Campus Benjamin Franklin, Department of Psychiatry, Berlin, Germany 95 Saint-Petersburg State University, Department of Psychology, Saint-Petersburg, Russian Federation 96 V.M. Bekhterev National Medical Research Center for Psychiatry and Neurology, Department of Borderline Disorders and Psychotherapy, Saint-Petersburg, Russian Federation 97 The Hospital for Sick Children, Department of Genetics and Genomic Biology, Toronto, Canada 98 University of Toronto, McLaughlin Center, Toronto, Canada 99 Karolinska Institutet, National Centre for Suicide Research and Prevention of Mental Ill-Health (NASP), LIME, Stockholm, Sweden 100 Aarhus University, Centre for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark 101 Aarhus University, Centre for Integrative Sequencing, iSEQ, Aarhus, Denmark 102 Aarhus University, Department of Biomedicine, Aarhus, Denmark 103 Aarhus University, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark 104 University of California Los Angeles, David Geffen School of Medicine, Los Angeles, LA, USA 105 University of California Los Angeles, Department of Psychiatry and Biobehavioral Science, Semel Institute for Neuroscience and Human Behavior, Los Angeles, LA, USA 106 King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Department of Psychological Medicine, London, UK 107 King’s College London and South London and Maudsley National Health Service Foundation Trust, National Institute for Health Research Biomedical Research Centre, London, UK 108 University of Michigan, Population Studies Center, Institute for Social Research, Ann Arbor, MI, USA 109 University of Michigan, Survery Research Center, Institute for Social Research, Ann Arbor, MI, USA 110 Curtin University, School of Psychology, Perth, Western Australia, Australia 111 The University of Western Australia, Division of Paediatrics, Perth, Western Australia, Australia 112 University Health Network, Centre for Mental Health, Toronto, Canada 113 University Health Network, Program for Eating Disorders, Toronto, Canada 114 Aarhus University, National Centre for Register-Based Research, Aarhus, Denmark 115 University of North Carolina at Chapel Hill, Department of Genetics, Chapel Hill, NC, USA 116 Umeå University Medical Faculty, Department of Clinical Sciences, Psychiatry, Umeå, Sweden 117 Diakonhjemmet Hospital, Department of Psychiatric Research, Oslo, Norway 118 Karolinska Institutet, Department of Clinical Neuroscience, Centre for Psychiatry Research, Stockholm, Sweden 119 University of Oslo, NORMENT, Institute of Clinical Medicine, Oslo, Norway 120 University of Adelaide, Discipline of Psychiatry, Adelaide, SA, Australia 121 Dalhousie University, Department of Psychiatry, Halifax, NS, Canada 122 National Institute of Mental Health, Klecany, CZ 123 Karolinska Institutet, Department of Clinical Neuroscience, Stockholm, Sweden 124 Karolinska Institutet, Inst of Environmental Medicine, Stockholm, Sweden 125 Berkshire Healthcare NHS Foundation Trust, Psychiatry, Bracknell, UK 126 Copenhagen University Hospital, Institute of Biological Psychiatry, Copenhagen Mental Health Services, Copenhagen, Denmark 127 iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Copenhagen, Denmark 128 Hospital Universitari Vall d’Hebron, Department of Psychiatry, Barcelona, Spain 129 Instituto de Salud Carlos III, Biomedical Network Research Centre on Mental Health (CIBERSAM), Madrid, Spain 130 University of Barcelona, Department of Genetics, Microbiology & Statistics, Barcelona, Spain 131 Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Psychiatric Genetics Unit, Group of Psychiatry, Mental Health and Addiction,, Barcelona, Spain 132 University Medicine Greifswald, Department of Psychiatry and Psychotherapy, Greifswald, Mecklenburg-Vorpommern, Germany 133 German Centre for Neurodegenerative Diseases (DZNE), Partner Site Rostock/Greifswald, Greifswald, Mecklenburg-Vorpommern, Germany 134 University of Coimbra, Department of Psychiatry, Coimbra, Portugal 135 University College London, Division of Psychiatry, London, UK 136 Hospital de Clínicas de Porto Alegre, Laboratory of Developmental Psychiatry, Porto Alegre, RS, Brazil 137 Universidade Federal do Rio Grande do Sul, Department of Genetics, Porto Alegre, RS, Brazil 138 University of Melbourne, Department of Psychiatry, Melbourne Medical School, Melbourne, Australia 139 University of Münster, Department of Psychiatry, Münster, Germany 140 Assistance Publique - Hôpitaux de Paris, Department of Psychiatry and Addiction Medicine, Paris, France 141 FondaMental Foundation, Paris Bipolar and TRD Expert Centres, Paris, France 142 INSERM, UMR-S1144 Team 1 : Biomarkers of relapse and therapeutic response in addiction and mood disorders, Paris, France 143 Université Paris Cité, Psychiatry, Paris, France 144 University of Münster, Institute of Epidemiology and Social Medicine, Münster, Nordrhein-Westfalen, Germany 145 Mayo Clinic, Health Sciences Research, Rochester, MN, USA 146 State University of New York Downstate Medical Center, Department of Psychiatry and Behavioral Sciences, New York, NY, USA 147 Max Planck Institute of Psychiatry, Department of Translational Research in Psychiatry, Munich, Germany 148 University of Michigan, Center for Statistical Genetics and Department of Biostatistics, Ann Arbor, MI, USA 149 UMC Utrecht Brain Center, Psychiatry, Utrecht, Netherlands 150 Universitat Autònoma de Barcelona, Department of Psychiatry and Legal Medicine, Barcelona, Spain 151 University of California San Diego, Department of Psychiatry, La Jolla, CA, USA 152 University of New South Wales, School of Psychology, Sydney, NSW, Australia 153 University Hospital, LMU Munich, Institute of Psychiatric Phenomics and Genomics (IPPG), Munich, Germany 154 The University of Queensland, Child Health Research Centre, Brisbane, QLD, Australia 155 UMC Utrecht Hersencentrum Rudolf Magnus, Department of Psychiatry, Utrecht, Netherlands 156 University of Granada, Mental Health Unit, Department of Psychiatry, Faculty of Medicine, Granada University Hospital Complex, Granada, Spain 157 CNRS GDR 3557, Institut de Psychiatrie, Paris, France 158 GHU Paris Psychiatrie et Neurosciences, Department of Evaluation, Prevention and Therapeutic innovation, Paris, France 159 Université de Paris, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team Pathophysiology of psychiatric diseases, Paris, France 160 Research Centre Jülich, Institute of Neuroscience and Medicine (INM-1), Jülich, Germany 161 University Hospital Basel, Institute of Medical Genetics and Pathology, Basel, Switzerland 162 University of Basel, Department of Biomedicine, Basel, Switzerland 163 University of Bonn, School of Medicine & University Hospital Bonn, Institute of Human Genetics, Bonn, Germany 164 Trinity College Dublin, Neuropsychiatric Genetics Research Group, Dept of Psychiatry and Trinity Translational Medicine Institute, Dublin, Ireland 165 Cardiff University, Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff, UK 166 University of Southern California, Department of Translational Genomics, Pasadena, CA, USA 167 Oslo University Hospital, Department of Medical Genetics, Oslo, Norway 168 University of Bergen, NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, Bergen, Norway 169 Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Department of Genetic Epidemiology in Psychiatry, Mannheim, Germany 170 University of Marburg, Centre for Human Genetics, Marburg, Germany 171 Mayo Clinic, Department of Psychiatry & Psychology, Rochester, MN, USA 172 NorthShore University HealthSystem, Department of Psychiatry and Behavioral Sciences, Evanston, IL, USA 173 University of Chicago, Department of Psychiatry and Behavioral Neuroscience, Chicago, IL, USA 174 Medical University of Vienna, Department of Psychiatry and Psychotherapy, Vienna, Austria 175 University of Munich, Department of Psychiatry, Munich, Germany 176 University of New South Wales, School of Psychiatry, Sydney, NSW, Australia 177 Hospital de Clínicas de Porto Alegre, ADHD Outpatient Program, Adult Division, Porto Alegre, RS, Brazil 178 Universidade Federal do Rio Grande do Sul, Department of Psychiatry, Porto Alegre, RS, Brazil 179 Alexandru Obregia Clinical Psychiatric Hospital, Biometric Psychiatric Genetics Research Unit, Bucharest, Romania 180 University of Granada, Department of Psychiatry, Faculty of Medicine and Biomedical Research Centre (CIBM), Granada, Spain 181 University Regional Hospital. Biomedicine Institute (IBIMA), Mental Health Department, Málaga, Spain 182 Kaiser Permanente Northern California, Psychiatry, San Francisco, CA, USA 183 Poznan University of Medical Sciences, Psychiatric Genetics, Department of Psychiatry, Poznan, Poland 184 Max Planck Institute of Psychiatry, Munich, Germany 185 University of Worcester, Department of Psychological Medicine, Worcester, UK 186 University of Gothenburg, Department of Psychiatry and Neuroscience, Gothenburg, Sweden 187 UMC Utrecht Brain Center Rudolf Magnus, Psychiatry, Utrecht, Netherlands 188 University of California San Diego, Institute for Genomic Medicine, La Jolla, CA, USA 189 University of Toronto, Department of Psychiatry, Toronto, ON, Canada 190 Harvard TH Chan School of Public Health, Department of Epidemiology, Boston, MA, USA 191 Massachusetts General Hospital, Department of Psychiatry, Boston, MA, USA 192 Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain 193 University of Gothenburg, Institute of Neuroscience and Physiology, Gothenburg, Sweden 194 North East London NHS Foundation Trust, Psychiatry, Ilford, UK 195 Univ Paris Est Créteil, INSERM, AP-HP, IMRB, Translational Neuropsychiatry, DMU IMPACT, FHU ADAPT, Fondation FondaMental, Créteil, France 196 INSERM, Paris, France 197 Université Paris Est, Faculté de Médecine, Créteil, France 198 Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit, Boston, MA, USA 199 Stanford University, Psychiatry & Behavioral Sciences, Stanford, CA, USA 200 Broad Institute of MIT and Harvard, Stanley Center for Psychiatric Research, Cambridge, MA, USA 201 Massachusetts General Hospital, Analytical and Translational Genetics Unit, Cambridge, MA, USA 202 M. Sklodowska-Curie Cancer Center and Institute of Oncology, Cancer Epidemiology and Prevention, Warsaw, Poland 203 Lindner Center of HOPE, Research Institute, Mason, OH, USA 204 Columbia University College of Physicians and Surgeons, Psychiatry, New York, NY, USA 205 Queensland University of Technology, School of Psychology and Counseling, Brisbane, QLD, Australia 206 The University of Queensland, Queensland Brain Institute, Brisbane, QLD, Australia 207 University of Oslo, Institute of Clinical Medicine, Division of Mental Health and Addiction, Oslo, Norway 208 Amsterdam UMC, Vrije Universiteit and GGZ inGeest, Department of Psychiatry, Amsterdam, Netherlands 209 University of Granada, Department of Nursing, Faculty of Health Sciences and Biomedical Research Centre (CIBM), Granada, Spain 210 Norwegian University of Science and Technology - NTNU, Mental Health, Faculty of Medicine and Health Sciences, Trondheim, Norway 211 St Olavs University Hospital, Psychiatry, Trondheim, Norway 212 Aarhus University, Centre for Integrated Register-based Research, Aarhus, Denmark 213 Munich Cluster for Systems Neurology (SyNergy), Munich, Germany 214 University of Liverpool, Liverpool, UK 215 University of Pittsburgh, Psychiatry and Human Genetics, Pittsburgh, PA, USA 216 Erasmus University Medical Center, Psychiatry, Rotterdam, Netherlands 217 Rutgers University, RWJMS,NJMS,UBHC, Pisctatway, NJ, USA 218 Rutgers University, RWJMS,NJMS, Pisctatway, NJ, USA 219 Amsterdam UMC, Vrije Universiteit, Department of Psychiatry and Amsterdam Neuroscience, Amsterdam, Netherlands 220 Johns Hopkins University School of Medicine, Psychiatry, Baltimore, MD, USA 221 BioMarin Pharmaceuticals, Genetics, London, UK 222 University of Oxford, St Edmund Hall, Oxford, UK 223 University of Oxford, Department of Psychiatry, Oxford, UK 224 University Hospital Frankfurt, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Frankfurt, Germany 225 Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Department of Genetic Epidemiology in Psychiatry, Mannheim, Baden-Württemberg, Germany 226 University of Granada, Department of Biochemistry and Molecular Biology II and Institute of Neurosciences, Biomedical Research Centre (CIBM), Granada, Spain 227 Harvard TH Chan School of Public Health, Department of Environmental Health, Boston, MA, USA 228 McGill University, Faculty of Medicine, Department of Neurology and Neurosurgery, Montreal, QC, Canada 229 Montreal Neurological Institute and Hospital, Montreal, QC, Canada 230 Instituto de Ciencias Biomedicas Universidade de Sao Paulo, Department of Physiology and Biophysics, São Paulo, SP, Brazil 231 Johns Hopkins University School of Medicine, Department of Psychiatry and Behavioral Sciences, Baltimore, MD, USA 232 National Institute of Mental Health, Human Genetics Branch, Intramural Research Program, Bethesda, MD, USA 233 University Medical Center Göttingen, Department of Psychiatry and Psychotherapy, Göttingen, Germany 234 University of Bologna, Department of Biomedical and NeuroMotor Sciences, Bologna, Italy 235 National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA 236 Kaiser Permanente Washington, Behavioral Health Services, Seattle, WA, USA 237 Icahn School of Medicine at Mount Sinai, Department of Neuroscience, New York, NY, USA 238 Massachusetts General Hospital, Psychiatric and Neurodevelopmental Genetics Unit (PNGU), Boston, MA, USA 239 King’s College London, Institute of Psychology, Psychiatry & Neuroscience, London, UK 240 Baylor College of Medicine, Houston, Menninger Department of Psychiatry and Behavioral Sciences, Houston, TX, USA 241 IRCCS Santa Lucia Foundation, Rome, Laboratory of Neuropsychiatry, Rome, Italy 242 Nofer Institute of Occupational Medicine, Department of Environmental Epidemiology, Lodz, Poland 243 University of California, San Diego, Center for Behavioral Genomics, Department of Psychiatry, La Jolla, CA, USA 244 McGill University, Department of Psychiatry, Montreal, QC, Canada 245 Centre for Addiction and Mental Health, Molecular Brain Science, Toronto, ON, Canada 246 University Medicine Greifswald, Institute for Community Medicine, Greifswald, Mecklenburg-Vorpommern, Germany 247 Columbia University College of Physicians and Surgeons, New York, NY, USA 248 New York State Psychiatric Institute, Division of Translational Epidemiology, New York, NY, USA 249 Stanford University, Department of Psychiatry and Behavioral Sciences, Stanford, CA, USA 250 University of Toronto, Institute of Medical Science, Toronto, ON, Canada 251 Centre for Addiction and Mental Health, Molecular Brain Science, Campbell Family Mental Health Research Institute, Toronto, ON, Canada 252 University of Toronto, Laboratory Medicine and Pathobiology, Toronto, ON, Canada 253 iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark 254 Australian National University, Center of Mental Health Research, Canberra, Australia 255 Johns Hopkins Bloomberg School of Public Health, Department of Mental Health, Baltimore, MD, USA 256 Mental Health Centre Copenhagen, Danish Research Institute for Suicide Prevention, Copenhagen, Denmark 257 Broad Institute, Program in Medical and Population Genetics, Cambridge, MA, USA 258 University of Tartu, Estonian Genome Center, Institute of Genomics, Tartu, Estonia 259 SUNY Upstate Medical University, Department of Psychiatry and Behavioral Sciences, Syracuse, NY, USA 260 Statens Serum Institut, Center for Neonatal Screening, Department for Congenital Disorders, Copenhagen, Denmark 261 National Taiwan University Hospital and College of Medicine, Department of Psychiatry, Taipei, Taiwan 262 King’s College London, Department of Medical & Molecular Genetics, London, UK 263 Janssen Research & Development, LLC, Neuroscience, Titusville, NJ, USA 264 Aarhus University Hospital, Risskov, Psychosis Research Unit, Aarhus, Denmark 265 Copenhagen University Hospital, Mental Health Center Copenhagen, Copenhagen, Denmark 266 QIMR Berghofer Medical Research Institute, Department of Population Health, Brisbane, QLD, Australia 267 University of Edinburgh, Institute for Genetics and Molecular Medicine, Edinburgh, UK 268 University of Edinburgh, Centre for Clinical Brain Sciences, Edinburgh, UK 269 Regeneron Genetics Center, Analytical Genetics and Data Science, Tarrytown, NY, USA 270 University of California San Diego, Department of Psychiatry and School of Public Health, La Jolla, CA, USA 271 University of Copenhagen, Department of Clinical Medicine, Copenhagen, Denmark 272 University of Copenhagen, Lundbeck Foundation GeoGenetics Centre, GLOBE Institute,, Copenhagen, Denmark 273 University of Iowa, Department of Psychiatry, Iowa City, IA, USA 274 University of Utah School of Medicine, Biomedical Informatics, Salt Lake City, UT, USA 275 Durham Veterans Affairs Health Care System, VISN 6 Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, NC, USA 276 Duke University School of Medicine, Department of Psychiatry and Behavioral Sciences, Durham, NC, USA 277 Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN, USA 278 Vanderbilt University Medical Center, Department of Psychiatry and Behavioral Sciences, Nashville, TN, USA MVP Suicide Exemplar Workgroup Silvia Crivelli, Ph.D. (Lawrence Berkeley National Laboratory), Michelle F. Dennis, B.A. (Durham Veterans Affairs Health Care System & Duke University School of Medicine), Phillip D. Harvey, Ph.D. (University of Miami Miller School of Medicine, Miami, FL), Bruce W. Carter (VA Medical Center), Jennifer E. Huffman, Ph.D. (Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System), Daniel Jacobson, Ph.D. (Oak Ridge National Laboratory), Ravi Madduri, Ph.D. (Argonne National Laboratory), Maren K. Olsen, Ph.D. (Duke University School of Medicine), and John Pestian, Ph.D. (Oak Ridge National Laboratory). Veterans Administration Million Veteran Program (MVP) J. Michael Gaziano, M.D., M.P.H. (co-chair, VA Boston Healthcare System), Sumitra Muralidhar, Ph.D. (co-chair, U.S. Department of Veterans Affairs), Rachel Ramoni, D.M.D., Sc.D. (U.S. Department of Veterans Affairs), Jean Beckham, Ph.D. (Durham VA Medical Center), Kyong-Mi Chang, M.D. (Philadelphia VA Medical Center), Christopher J. O’Donnell, M.D., M.P.H. (VA Boston Healthcare System), Philip S. Tsao, Ph.D. (VA Palo Alto Health Care System), James Breeling, M.D. (Ex-Officio, U.S. Department of Veterans Affairs), Grant Huang, Ph.D. (Ex-Officio, U.S. Department of Veterans Affairs), and J.P. Casas Romero, M.D., Ph.D. (Ex-Officio, VA Boston Healthcare System). MVP Program Office: Sumitra Muralidhar, Ph.D., and Jennifer Moser, Ph.D., both of U.S. Department of Veterans Affairs. MVP Recruitment/Enrollment: Recruitment/Enrollment Director/Deputy Director, Boston—Stacey B. Whitbourne, Ph.D., Jessica V. Brewer, M.P.H. (VA Boston Healthcare System). MVP Coordinating Centers: Clinical Epidemiology Research Center (CERC), West Haven—Mihaela Aslan, Ph.D. (West Haven VA Medical Center). Cooperative Studies Program Clinical Research Pharmacy Coordinating Center, Albuquerque—Todd Connor, Pharm.D., Dean P. Argyres, B.S., M.S. (New Mexico VA Health Care System). Genomics Coordinating Center, Palo Alto—Philip S. Tsao, Ph.D. (VA Palo Alto Health Care System). MVP Boston Coordinating Center, Boston—J. Michael Gaziano, M.D., M.P.H. (VA Boston Healthcare System). MVP Information Center, Canandaigua—Brady Stephens, M.S. (Canandaigua VA Medical Center). VA Central Biorepository, Boston—Mary T. Brophy, M.D., M.P.H., Donald E. Humphries, Ph.D., Luis E. Selva, Ph.D. (VA Boston Healthcare System). MVP Informatics, Boston—Nhan Do, M.D., Shahpoor Shayan (VA Boston Healthcare System). MVP Data Operations/Analytics, Boston—Kelly Cho, Ph.D. (VA Boston Healthcare System). MVP Science: Science Operations—Christopher J. O’Donnell, M.D., M.P.H. (VA Boston Healthcare System). Genomics Core— Christopher J. O’Donnell, M.D., M.P.H., Saiju Pyarajan, Ph.D. (VA Boston Healthcare System), Philip S. Tsao, Ph.D. (VA Palo Alto Health Care System). Phenomics Core—Kelly Cho, M.P.H., Ph.D. (VA Boston Healthcare System). Data and Computational Sciences—Saiju Pyarajan, Ph.D. (VA Boston Healthcare System). Statistical Genetics—Elizabeth Hauser, Ph.D. (Durham VA Medical Center). Yan Sun, Ph.D. (Atlanta VA Medical Center). Hongyu Zhao, Ph.D. (West Haven VA Medical Center. Current MVP Local Site Investigators: Peter Wilson, M.D. (Atlanta VA Medical Center); Rachel McArdle, Ph.D. (Bay Pines VA Healthcare System); Louis Dellitalia, M.D. (Birmingham VA Medical Center); Kristin Mattocks, Ph.D., M.P.H. (Central Western Massachusetts Healthcare System); John Harley, M.D., Ph.D. (Cincinnati VA Medical Center); Clement J. Zablocki (VA Medical Center); Jeffrey Whittle, M.D., M.P.H.; Frank Jacono, M.D. (VA Northeast Ohio Healthcare System); Jean Beckham, Ph.D. (Durham VA Medical Center); Edith Nourse Rogers Memorial Veterans Hospital; Salvador Gutierrez, M.D. (Edward Hines, Jr. VA Medical Center); Gretchen Gibson, D.D.S., M.P.H. (Veterans Health Care System of the Ozarks); Kimberly Hammer, Ph.D. (Fargo VA Health Care System); Laurence Kaminsky, Ph.D. (VA Health Care Upstate New York); Gerardo Villareal, M.D. (New Mexico VA Health Care System); Scott Kinlay, M.B.B.S., Ph.D. (VA Boston Healthcare System); Junzhe Xu, M.D. (VA Western New York Healthcare System); Mark Hamner, M.D. (Ralph H. Johnson VA Medical Center); Roy Mathew, M.D. (Columbia VA Health Care System); Sujata Bhushan, M.D. (VA North Texas Health Care System); Pran Iruvanti, DO, Ph.D. (Hampton VA Medical Center); Michael Godschalk, M.D. (Richmond VA Medical Center); Zuhair Ballas, M.D. (Iowa City VA Health Care System); Douglas Ivins, M.D. (Eastern Oklahoma VA Health Care System); Stephen Mastorides, M.D. (James A. Haley Veterans’ Hospital); Jonathan Moorman, M.D., Ph.D. (James H. Quillen VA Medical Center); Saib Gappy, M.D. (John D. Dingell VA Medical Center); Jon Klein, M.D., Ph.D. (Louisville VA Medical Center); Nora Ratcliffe, M.D. (Manchester VA Medical Center); Hermes Florez, M.D., Ph.D. (Miami VA Health Care System); Olaoluwa Okusaga, M.D. (Michael E. DeBakey VA Medical Center); Maureen Murdoch, M.D., M.P.H. (Minneapolis VA Health Care System); Peruvemba Sriram, M.D. (N FL/S GA Veterans Health System); Shing Shing Yeh, Ph.D., M.D. (Northport VA Medical Center); Neeraj Tandon, M.D. (Overton Brooks VA Medical Center); Darshana Jhala, M.D. (Philadelphia VA Medical Center); Samuel Aguayo, M.D. (Phoenix VA Health Care System); David Cohen, M.D. (Portland VA Medical Center); Satish Sharma, M.D. (Providence VA Medical Center); Suthat Liangpunsakul, M.D., M.P.H. (Richard Roudebush VA Medical Center); Kris Ann Oursler, M.D. (Salem VA Medical Center); Mary Whooley, M.D. (San Francisco VA Health Care System); Sunil Ahuja, M.D. (South Texas Veterans Health Care System); Joseph Constans, Ph.D. (Southeast Louisiana Veterans Health Care System); Paul Meyer, M.D., Ph.D. (Southern Arizona VA Health Care System); Jennifer Greco, M.D. (Sioux Falls VA Health Care System); Michael Rauchman, M.D. (St. Louis VA Health Care System); Richard Servatius, Ph.D. (Syracuse VA Medical Center); Melinda Gaddy, Ph.D. (VA Eastern Kansas Health Care System); Agnes Wallbom, M.D., M.S. (VA Greater Los Angeles Health Care System); Timothy Morgan, M.D. (VA Long Beach Healthcare System); Todd Stapley, D.O. (VA Maine Healthcare System); Scott Sherman, M.D., M.P.H. (VA New York Harbor Healthcare System); George Ross, M.D. (VA Pacific Islands Health Care System); Philip Tsao, Ph.D. (VA Palo Alto Health Care System); Patrick Strollo Jr., M.D. (VA Pittsburgh Health Care System); Edward Boyko, M.D. (VA Puget Sound Health Care System); Laurence Meyer, M.D., Ph.D. (VA Salt Lake City Health Care System); Samir Gupta, M.D., M.S.C.S. (VA San Diego Healthcare System); Mostaqul Huq, Pharm.D., Ph.D. (VA Sierra Nevada Health Care System); Joseph Fayad, M.D. (VA Southern Nevada Healthcare System); Adriana Hung, M.D., M.P.H. (VA Tennessee Valley Healthcare System); Jack Lichy, M.D., Ph.D. (Washington, DC VA Medical Center); Robin Hurley, M.D. (W.G., Bill Hefner VA Medical Center); Brooks Robey, M.D. (White River Junction VA Medical Center); and Robert Striker, M.D., Ph.D. (William S. Middleton Memorial Veterans Hospital). Acknowledgements JHL was supported by the Swedish Research Council (Vetenskapsrådet, award 2018-00799), Swedish Brain Foundation (Hjärnfonden, award FO2018-0272) and European Research Council (SCHIZTYPE, grant agreement 819540). PFS was supported by the Swedish Research Council (Vetenskapsrådet, award D0886501) and the US National Institute of Mental Health (R01s MH124871, MH121545, and MH123724). YL was supported by the European Research Council (SUBTREAT, grant agreement 101042183) and the US National Institute of Mental Health (R01 MH123724). SY was supported by the StratNeuro postdoctoral grant. We would like to acknowledge the International Headache Genetics Consortium for sharing the GWAS summary statistics on migraine. References 1. ↵ Sullivan , P.F. et al. Leveraging base-pair mammalian constraint to understand genetic variation and human disease . Science 380 , eabn2937 ( 2023 ). 2. ↵ Bulik-Sullivan , B.K. et al. An atlas of genetic correlations across human diseases and traits . Nature Genetics 47 , 1236 – 41 ( 2015 ). OpenUrl CrossRef PubMed 3. ↵ Anttila , V. et al. Analysis of shared heritability in common disorders of the brain . Science 360 ( 2018 ). 4. Cross-Disorder Group of the Psychiatric Genomics Consortium . Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders . Cell 179 , 1469 – 1482 e11 ( 2019 ). OpenUrl CrossRef PubMed 5. Choi , K.W. et al. An Exposure-Wide and Mendelian Randomization Approach to Identifying Modifiable Factors for the Prevention of Depression . Am J Psychiatry 177 , 944 – 954 ( 2020 ). OpenUrl CrossRef 6. ↵ Revez , J.A. et al. Genome-wide association study identifies 143 loci associated with 25 hydroxyvitamin D concentration . Nat Commun 11 , 1647 ( 2020 ). OpenUrl CrossRef PubMed 7. ↵ Sullivan , P.F. & Geschwind , D.H. Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders . Cell 177 , 162 – 183 ( 2019 ). OpenUrl CrossRef PubMed 8. ↵ Kallmann , F.J . The genetic theory of schizophrenia: and analysis of 691 schizophrenic twin index families . American Journal of Psychiatry 103 , 309 – 322 ( 1946 ). OpenUrl CrossRef PubMed Web of Science 9. Goldstein , D.B . Common genetic variation and human traits . N Engl J Med 360 , 1696 – 8 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 10. ↵ McClellan , J. & King , M.C . Genetic heterogeneity in human disease . Cell 141 , 210 – 7 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 11. ↵ Trubetskoy , V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia . Nature 604 , 502 – 508 ( 2022 ). OpenUrl PubMed 12. CNV Working Group of the Psychiatric Genomics Consortium & Schizophrenia Working Group of the Psychiatric Genomics Consortium . Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects . Nat Genet 49 , 27 – 35 ( 2017 ). OpenUrl CrossRef PubMed 13. ↵ Singh , T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia . Nature 604 , 509 – 516 ( 2022 ). OpenUrl PubMed 14. ↵ Halvorsen , M. et al. Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia . Nat Commun 11 , 1842 ( 2020 ). OpenUrl 15. ↵ O’Connor , L.J. et al. Extreme Polygenicity of Complex Traits Is Explained by Negative Selection . Am J Hum Genet 105 , 456 – 476 ( 2019 ). OpenUrl CrossRef 16. ↵ Purcell , S.M. et al. A polygenic burden of rare disruptive mutations in schizophrenia . Nature 506 , 185 – 90 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 17. ↵ Skene , N.G. et al. Genetic identification of brain cell types underlying schizophrenia . Nat Genet 50 , 825 – 833 ( 2018 ). OpenUrl CrossRef PubMed 18. ↵ Bryois , J. et al. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease . Nat Genet 52 , 482 – 493 ( 2020 ). OpenUrl PubMed 19. ↵ Zhang , M.J. et al. Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data . Nat Genet 54 , 1572 – 1580 ( 2022 ). OpenUrl 20. Watanabe , K. , Umicevic Mirkov , M. , de Leeuw , C.A. , van den Heuvel , M.P. & Posthuma , D. Genetic mapping of cell type specificity for complex traits . Nat Commun 10 , 3222 ( 2019 ). OpenUrl PubMed 21. Wang , R. , Lin , D.Y. & Jiang , Y . EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing . PLoS Genet 18 , e1010251 ( 2022 ). OpenUrl 22. Skene , N.G. & Grant , S.G . Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment . Front Neurosci 10 , 16 ( 2016 ). 23. Jerby-Arnon , L. & Regev , A . DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data . Nat Biotechnol 40 , 1467 – 1477 ( 2022 ). OpenUrl 24. Jagadeesh , K.A. et al. Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics . Nat Genet 54 , 1479 – 1492 ( 2022 ). OpenUrl CrossRef 25. Calderon , D. et al. Inferring Relevant Cell Types for Complex Traits by Using Single-Cell Gene Expression . Am J Hum Genet 101 , 686 – 699 ( 2017 ). OpenUrl CrossRef PubMed 26. ↵ Timshel , P.N. , Thompson , J.J. & Pers , T.H . Genetic mapping of etiologic brain cell types for obesity . Elife 9 ( 2020 ). 27. ↵ Siletti , K. et al. Transcriptomic diversity of cell types across the adult human brain . Science 382 , eadd7046 ( 2023 ). 28. ↵ Li , S. et al. Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity . Front Psychiatry 10 , 482 ( 2019 ). 29. ↵ Hu , M.L. et al. A Review of the Functional and Anatomical Default Mode Network in Schizophrenia . Neurosci Bull 33 , 73 – 84 ( 2017 ). OpenUrl CrossRef 30. ↵ Whitfield-Gabrieli , S. et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia . Proc Natl Acad Sci U S A 106 , 1279 – 84 ( 2009 ). OpenUrl Abstract / FREE Full Text 31. ↵ Yang , J. , Zeng , J. , Goddard , M.E. , Wray , N.R. & Visscher , P.M . Concepts, estimation and interpretation of SNP-based heritability . Nat Genet 49 , 1304 – 1310 ( 2017 ). OpenUrl CrossRef PubMed 32. ↵ Zeisel , A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq . Science 347 , 1138 – 42 ( 2015 ). OpenUrl Abstract / FREE Full Text 33. ↵ Sugino , K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain . Nat Neurosci 9 , 99 – 107 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 34. ↵ Eisenberg , E. & Levanon , E.Y . Human housekeeping genes, revisited . Trends Genet 29 , 569 – 74 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 35. ↵ Gene Ontology Consortium . The Gene Ontology resource: enriching a GOld mine . Nucleic Acids Res 49 , D325 – D334 ( 2021 ). OpenUrl CrossRef PubMed 36. ↵ Roig Adam , A., et al. Transcriptional diversity in specific synaptic gene sets discriminates cortical neuronal identity . Biol Direct 18 , 22 ( 2023 ). 37. ↵ Adams , C.D . A multivariable Mendelian randomization to appraise the pleiotropy between intelligence, education, and bipolar disorder in relation to schizophrenia . Sci Rep 10 , 6018 ( 2020 ). OpenUrl CrossRef 38. MacCabe , J.H. et al. Excellent school performance at age 16 and risk of adult bipolar disorder: national cohort study . Br J Psychiatry 196 , 109 – 15 ( 2010 ). OpenUrl Abstract / FREE Full Text 39. ↵ Eysenck , H.J. & Eysenck , S.B.G . Manual of the Eysenck Personality Questionnaire , (Hodder and Stoughton , London , 1975 ). 40. ↵ Kimbrel , N.A. et al. Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans . JAMA Psychiatry 80 , 135 – 145 ( 2023 ). OpenUrl 41. ↵ Clements , C.C. et al. Genome-wide association study of patients with a severe major depressive episode treated with electroconvulsive therapy . Mol Psychiatry ( 2021 ). 42. ↵ Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression . Nat Genet 50 , 668 – 681 ( 2018 ). OpenUrl CrossRef PubMed 43. Viktorin , A. et al. Heritability of Perinatal Depression and Genetic Overlap With Nonperinatal Depression . Am J Psychiatry 173 , 158 – 65 ( 2016 ). OpenUrl 44. ↵ Nguyen , T.D. et al. Genetic heterogeneity and subtypes of major depression . Mol Psychiatry 27 , 1667 – 1675 ( 2022 ). OpenUrl CrossRef 45. ↵ Yu , B. et al. Molecular and cellular evolution of the amygdala across species analyzed by single-nucleus transcriptome profiling . Cell Discov 9 , 19 ( 2023 ). 46. ↵ Tosches , M.A. et al. Evolution of pallium, hippocampus, and cortical cell types revealed by single-cell transcriptomics in reptiles . Science 360 , 881 – 888 ( 2018 ). OpenUrl Abstract / FREE Full Text 47. ↵ Fu , J.M. et al. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism . Nat Genet 54 , 1320 – 1331 ( 2022 ). OpenUrl PubMed 48. ↵ Poldrack , R.A. et al. A phenome-wide examination of neural and cognitive function . Sci Data 3 , 160110 ( 2016 ). 49. ↵ Bipolar Disorder Working Group of the PGC et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology . Nat Genet 53 , 817 – 829 ( 2021 ). OpenUrl CrossRef PubMed 50. ↵ Howard , D.M. et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions . Nat Neurosci 22 , 343 – 352 ( 2019 ). OpenUrl CrossRef PubMed 51. ↵ Batiuk , M.Y. et al. Upper cortical layer-driven network impairment in schizophrenia . Sci Adv 8 , eabn8367 ( 2022 ). 52. ↵ Lewis , D.A. , Curley , A.A. , Glausier , J.R. & Volk , D.W . Cortical parvalbumin interneurons and cognitive dysfunction in schizophrenia . Trends Neurosci 35 , 57 – 67 ( 2012 ). OpenUrl CrossRef PubMed Web of Science 53. ↵ Nguyen , T.D. et al. Genetic Contribution to the Heterogeneity of Major Depressive Disorder: Evidence From a Sibling-Based Design Using Swedish National Registers . Am J Psychiatry 180 , 714 – 722 ( 2023 ). OpenUrl 54. ↵ Zandi , P.P. et al. Amygdala and anterior cingulate transcriptomes from individuals with bipolar disorder reveal downregulated neuroimmune and synaptic pathways . Nat Neurosci 25 , 381 – 389 ( 2022 ). OpenUrl CrossRef PubMed 55. Barth , C. et al. In Vivo Amygdala Nuclei Volumes in Schizophrenia and Bipolar Disorders . Schizophr Bull 47 , 1431 – 1441 ( 2021 ). OpenUrl 56. ↵ Chang , X. et al. RNA-seq analysis of amygdala tissue reveals characteristic expression profiles in schizophrenia . Transl Psychiatry 7 , e1203 ( 2017 ). OpenUrl 57. ↵ Smith , D.M. & Torregrossa , M.M . Valence encoding in the amygdala influences motivated behavior . Behav Brain Res 411 , 113370 ( 2021 ). 58. ↵ Smucny , J. , Dienel , S.J. , Lewis , D.A. & Carter , C.S . Mechanisms underlying dorsolateral prefrontal cortex contributions to cognitive dysfunction in schizophrenia . Neuropsychopharmacology 47 , 292 – 308 ( 2022 ). OpenUrl CrossRef 59. ↵ Tasic , B. et al. Shared and distinct transcriptomic cell types across neocortical areas . Nature 563 , 72 – 78 ( 2018 ). OpenUrl CrossRef PubMed 60. ↵ Harris , K.D. & Mrsic-Flogel , T.D. Cortical connectivity and sensory coding . Nature 503 , 51 – 8 ( 2013 ). OpenUrl CrossRef PubMed Web of Science 61. ↵ Glasser , M.F. et al. A multi-modal parcellation of human cerebral cortex . Nature 536 , 171 – 178 ( 2016 ). OpenUrl CrossRef PubMed 62. ↵ Zhao , B. & Zhu , H . Genetic influences on the intrinsic and extrinsic functional organizations of the cerebral cortex . medRxiv ( 2023 ). 63. ↵ Karbasforoushan , H. & Woodward , N.D . Resting-state networks in schizophrenia . Curr Top Med Chem 12 , 2404 – 14 ( 2012 ). OpenUrl CrossRef PubMed 64. ↵ Ji , J.L. et al. Mapping the human brain’s cortical-subcortical functional network organization . Neuroimage 185 , 35 – 57 ( 2019 ). OpenUrl 65. ↵ Tian , L. et al. Convergent evidence from multimodal imaging reveals amygdala abnormalities in schizophrenic patients and their first-degree relatives . PLoS One 6 , e28794 ( 2011 ). OpenUrl CrossRef PubMed 66. ↵ Hoptman , M.J. et al. Amygdalofrontal functional disconnectivity and aggression in schizophrenia . Schizophr Bull 36 , 1020 – 8 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 67. ↵ Qasim , S.E. , Mohan , U.R. , Stein , J.M. & Jacobs , J . Neuronal activity in the human amygdala and hippocampus enhances emotional memory encoding . Nat Hum Behav 7 , 754 – 764 ( 2023 ). OpenUrl 68. ↵ Zheng , J. et al. Amygdala-hippocampal dynamics during salient information processing . Nat Commun 8 , 14413 ( 2017 ). 69. ↵ Frankish , A. et al. Gencode 2021 . Nucleic Acids Res 49 , D 916 – D923 ( 2021 ). OpenUrl CrossRef 70. ↵ Price , A.L. et al. Long-range LD can confound genome scans in admixed populations . Am J Hum Genet 83 , 132 – 5 ; author reply 135-9 ( 2008 ). OpenUrl CrossRef PubMed Web of Science 71. ↵ Finucane , H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics . Nat Genet 47 , 1228 – 35 ( 2015 ). OpenUrl CrossRef PubMed 72. ↵ Welter , D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations . Nucleic Acids Res 42 , D1001 – 6 ( 2014 ). OpenUrl CrossRef PubMed Web of Science 73. ↵ Wise , A.L. , Gyi , L. & Manolio , T.A . eXclusion: Toward Integrating the X Chromosome in Genome-wide Association Analyses . American journal of human genetics 92 , 643 – 7 ( 2013 ). OpenUrl CrossRef PubMed 74. ↵ Nalls , M.A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies . Lancet Neurol 18 , 1091 – 1102 ( 2019 ). OpenUrl CrossRef PubMed 75. ↵ Sayers , E.W. et al. Database resources of the National Center for Biotechnology Information . Nucleic Acids Res 49 , D10 – D17 ( 2021 ). OpenUrl CrossRef PubMed 76. ↵ Chang , C.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets . Gigascience 4 , 7 ( 2015 ). 77. ↵ Durbin , R.M. et al. A map of human genome variation from population-scale sequencing . Nature 467 , 1061 – 73 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 78. ↵ Koopmans , F. et al. SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse . Neuron 103 , 217 – 234 e4 ( 2019 ). OpenUrl 79. ↵ Gazal , S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection . Nat Genet 49 , 1421 – 1427 ( 2017 ). OpenUrl CrossRef PubMed 80. ↵ McInnes , L.A. , Healy , J. & Melville , J. UMAP : Uniform Manifold Approximation and Projection for Dimension Reduction . in arXiv.org ( 2020 ). 81. ↵ Hahsler , M. , Piekenbrock , M. & Doran , D. dbscan: Fast Density-Based Clustering with R . Journal of Statistical Software 91 , 1 – 30 ( 2019 ). OpenUrl 82. ↵ Ding , S.-L. , et al. Allen Human Reference Atlas – 3D . ( 2020 ). 83. ↵ Ding , S.L. et al. Comprehensive cellular-resolution atlas of the adult human brain . J Comp Neurol 524 , 3127 – 481 ( 2016 ). OpenUrl CrossRef PubMed 84. ↵ Yushkevich , P.A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability . Neuroimage 31 , 1116 – 28 ( 2006 ). OpenUrl CrossRef PubMed Web of Science 85. ↵ Gorgolewski , K.J. , Durnez , J. & Poldrack , R.A . Preprocessed Consortium for Neuropsychiatric Phenomics dataset . F1000Res 6 , 1262 ( 2017 ). OpenUrl 86. ↵ Yin , W. , Mostafa , S. & Wu , F.X . Diagnosis of Autism Spectrum Disorder Based on Functional Brain Networks with Deep Learning . J Comput Biol 28 , 146 – 165 ( 2021 ). OpenUrl CrossRef 87. ↵ Mijalkov , M. et al. BRAPH: A graph theory software for the analysis of brain connectivity . PLoS One 12 , e0178798 ( 2017 ). OpenUrl 88. ↵ Mwangi , B. , Tian , T.S. & Soares , J.C . A review of feature reduction techniques in neuroimaging . Neuroinformatics 12 , 229 – 44 ( 2014 ). OpenUrl CrossRef 89. ↵ Breiman , L. Random Forests . Machine Learning 45 , 5 – 32 ( 2001 ). OpenUrl CrossRef 90. ↵ Karczewski , K.J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans . Nature 581 , 434 – 443 ( 2020 ). OpenUrl CrossRef PubMed 91. ↵ Exome Aggregation Consortium et al. Analysis of protein-coding genetic variation in 60,706 humans . Nature 536 , 285 – 91 ( 2016 ). OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted January 20, 2024. 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. 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Share Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity Shuyang Yao , Arvid Harder , Fahimeh Darki , Yu-Wei Chang , Ang Li , Kasra Nikouei , Giovanni Volpe , Johan N Lundström , Jian Zeng , Naomi Wray , Yi Lu , Patrick F Sullivan , Jens Hjerling-Leffler medRxiv 2024.01.18.24301478; doi: https://doi.org/10.1101/2024.01.18.24301478 Share This Article: Copy Citation Tools Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity Shuyang Yao , Arvid Harder , Fahimeh Darki , Yu-Wei Chang , Ang Li , Kasra Nikouei , Giovanni Volpe , Johan N Lundström , Jian Zeng , Naomi Wray , Yi Lu , Patrick F Sullivan , Jens Hjerling-Leffler medRxiv 2024.01.18.24301478; doi: https://doi.org/10.1101/2024.01.18.24301478 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 Subject Areas All Articles Addiction Medicine (574) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4460) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (611) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15250) Forensic Medicine (31) Gastroenterology (1132) Genetic and Genomic Medicine (6621) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4563) Health Policy (1372) Health Systems and Quality Improvement (1617) Hematology (544) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1107) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6640) Nursing (346) Nutrition (1001) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3350) Ophthalmology (981) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1698) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5464) Public and Global Health (9258) Radiology and Imaging (2211) Rehabilitation Medicine and Physical Therapy (1372) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (533) Surgery (715) Toxicology (100) Transplantation (289) 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:'a0386d685cace7d8',t:'MTc4MDA4ODQxMw=='};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())}}}})();