Mapping genomic loci implicates genes and synaptic biology in schizophrenia.
- Authors
- Trubetskoy, Vassily; Pardiñas, Antonio F; Qi, Ting; Panagiotaropoulou, Georgia; Awasthi, Swapnil; Bigdeli, Tim B; Bryois, Julien; Chen, Chia-Yen; Dennison, Charlotte A; Hall, Lynsey S; Lam, Max; Watanabe, Kyoko; Frei, Oleksandr; Ge, Tian; Harwood, Janet C; Koopmans, Frank; Magnusson, Sigurdur; Richards, Alexander L; Sidorenko, Julia; Wu, Yang; Zeng, Jian; Grove, Jakob; Kim, Minsoo; Li, Zhiqiang; Voloudakis, Georgios; Zhang, Wen; Adams, Mark; Agartz, Ingrid; Atkinson, Elizabeth G; Agerbo, Esben; Al Eissa, Mariam; Albus, Margot; Alexander, Madeline; Alizadeh, Behrooz Z; Alptekin, Köksal; Als, Thomas D; Amin, Farooq; Arolt, Volker; Arrojo, Manuel; Athanasiu, Lavinia; Azevedo, Maria Helena; Bacanu, Silviu A; Bass, Nicholas J; Begemann, Martin; Belliveau, Richard A; Bene, Judit; Benyamin, Beben; Bergen, Sarah E; Blasi, Giuseppe; Bobes, Julio; Bonassi, Stefano; Braun, Alice; Bressan, Rodrigo Affonseca; Bromet, Evelyn J; Bruggeman, Richard; Buckley, Peter F; Buckner, Randy L; Bybjerg-Grauholm, Jonas; Cahn, Wiepke; Cairns, Murray J; Calkins, Monica E; Carr, Vaughan J; Castle, David; Catts, Stanley V; Chambert, Kimberley D; Chan, Raymond C K; Chaumette, Boris; Cheng, Wei; Cheung, Eric F C; Chong, Siow Ann; Cohen, David; Consoli, Angèle; Cordeiro, Quirino; Costas, Javier; Curtis, Charles; Davidson, Michael; Davis, Kenneth L; de Haan, Lieuwe; Degenhardt, Franziska; DeLisi, Lynn E; Demontis, Ditte; Dickerson, Faith; Dikeos, Dimitris; Dinan, Timothy; Djurovic, Srdjan; Duan, Jubao; Ducci, Giuseppe; Dudbridge, Frank; Eriksson, Johan G; Fañanás, Lourdes; Faraone, Stephen V; Fiorentino, Alessia; Forstner, Andreas; Frank, Josef; Freimer, Nelson B; Fromer, Menachem; Frustaci, Alessandra; Gadelha, Ary; Genovese, Giulio; Gershon, Elliot S; Giannitelli, Marianna; Giegling, Ina; Giusti-Rodríguez, Paola; Godard, Stephanie; Goldstein, Jacqueline I; González Peñas, Javier; González-Pinto, Ana; Gopal, Srihari; Gratten, Jacob; Green, Michael F; Greenwood, Tiffany A; Guillin, Olivier; Gülöksüz, Sinan; Gur, Raquel E; Gur, Ruben C; Gutiérrez, Blanca; Hahn, Eric; Hakonarson, Hakon; Haroutunian, Vahram; Hartmann, Annette M; Harvey, Carol; Hayward, Caroline; Henskens, Frans A; Herms, Stefan; Hoffmann, Per; Howrigan, Daniel P; Ikeda, Masashi; Iyegbe, Conrad; Joa, Inge; Julià, Antonio; Kähler, Anna K; Kam-Thong, Tony; Kamatani, Yoichiro; Karachanak-Yankova, Sena; Kebir, Oussama; Keller, Matthew C; Kelly, Brian J; Khrunin, Andrey; Kim, Sung-Wan; Klovins, Janis; Kondratiev, Nikolay; Konte, Bettina; Kraft, Julia; Kubo, Michiaki; Kučinskas, Vaidutis; Kučinskiene, Zita Ausrele; Kusumawardhani, Agung; Kuzelova-Ptackova, Hana; Landi, Stefano; Lazzeroni, Laura C; Lee, Phil H; Legge, Sophie E; Lehrer, Douglas S; Lencer, Rebecca; Lerer, Bernard; Li, Miaoxin; Lieberman, Jeffrey; Light, Gregory A; Limborska, Svetlana; Liu, Chih-Min; Lönnqvist, Jouko; Loughland, Carmel M; Lubinski, Jan; Luykx, Jurjen J; Lynham, Amy; Macek, Milan; Mackinnon, Andrew; Magnusson, Patrik K E; Maher, Brion S; Maier, Wolfgang; Malaspina, Dolores; Mallet, Jacques; Marder, Stephen R; Marsal, Sara; Martin, Alicia R; Martorell, Lourdes; Mattheisen, Manuel; McCarley, Robert W; McDonald, Colm; McGrath, John J; Medeiros, Helena; Meier, Sandra; Melegh, Bela; Melle, Ingrid; Mesholam-Gately, Raquelle I; Metspalu, Andres; Michie, Patricia T; Milani, Lili; Milanova, Vihra; Mitjans, Marina; Molden, Espen; Molina, Esther; Molto, María Dolores; Mondelli, Valeria; Moreno, Carmen; Morley, Christopher P; Muntané, Gerard; Murphy, Kieran C; Myin-Germeys, Inez; Nenadić, Igor; Nestadt, Gerald; Nikitina-Zake, Liene; Noto, Cristiano; Nuechterlein, Keith H; O'Brien, Niamh Louise; O'Neill, F Anthony; Oh, Sang-Yun; Olincy, Ann; Ota, Vanessa Kiyomi; Pantelis, Christos; Papadimitriou, George N; Parellada, Mara; Paunio, Tiina; Pellegrino, Renata; Periyasamy, Sathish; Perkins, Diana O; Pfuhlmann, Bruno; Pietiläinen, Olli; Pimm, Jonathan; Porteous, David; Powell, John; Quattrone, Diego; Quested, Digby; Radant, Allen D; Rampino, Antonio; Rapaport, Mark H; Rautanen, Anna; Reichenberg, Abraham; Roe, Cheryl; Roffman, Joshua L; Roth, Julian; Rothermundt, Matthias; Rutten, Bart P F; Saker-Delye, Safaa; Salomaa, Veikko; Sanjuan, Julio; Santoro, Marcos Leite; Savitz, Adam; Schall, Ulrich; Scott, Rodney J; Seidman, Larry J; Sharp, Sally Isabel; Shi, Jianxin; Siever, Larry J; Sigurdsson, Engilbert; Sim, Kang; Skarabis, Nora; Slominsky, Petr; So, Hon-Cheong; Sobell, Janet L; Söderman, Erik; Stain, Helen J; Steen, Nils Eiel; Steixner-Kumar, Agnes A; Stögmann, Elisabeth; Stone, William S; Straub, Richard E; Streit, Fabian; Strengman, Eric; Stroup, T Scott; Subramaniam, Mythily; Sugar, Catherine A; Suvisaari, Jaana; Svrakic, Dragan M; Swerdlow, Neal R; Szatkiewicz, Jin P; Ta, Thi Minh Tam; Takahashi, Atsushi; Terao, Chikashi; Thibaut, Florence; Toncheva, Draga; Tooney, Paul A; Torretta, Silvia; Tosato, Sarah; Tura, Gian Battista; Turetsky, Bruce I; Üçok, Alp; Vaaler, Arne; van Amelsvoort, Therese; van Winkel, Ruud; Veijola, Juha; Waddington, John; Walter, Henrik; Waterreus, Anna; Webb, Bradley T; Weiser, Mark; Williams, Nigel M; Witt, Stephanie H; Wormley, Brandon K; Wu, Jing Qin; Xu, Zhida; Yolken, Robert; Zai, Clement C; Zhou, Wei; Zhu, Feng; Zimprich, Fritz; Atbaşoğlu, Eşref Cem; Ayub, Muhammad; Benner, Christian; Bertolino, Alessandro; Black, Donald W; Bray, Nicholas J; Breen, Gerome; Buccola, Nancy G; Byerley, William F; Chen, Wei J; Cloninger, C Robert; Crespo-Facorro, Benedicto; Donohoe, Gary; Freedman, Robert; Galletly, Cherrie; Gandal, Michael J; Gennarelli, Massimo; Hougaard, David M; Hwu, Hai-Gwo; Jablensky, Assen V; McCarroll, Steven A; Moran, Jennifer L; Mors, Ole; Mortensen, Preben B; Müller-Myhsok, Bertram; Neil, Amanda L; Nordentoft, Merete; Pato, Michele T; Petryshen, Tracey L; Pirinen, Matti; Pulver, Ann E; Schulze, Thomas G; Silverman, Jeremy M; Smoller, Jordan W; Stahl, Eli A; Tsuang, Debby W; Vilella, Elisabet; Wang, Shi-Heng; Xu, Shuhua; Indonesia Schizophrenia Consortium; PsychENCODE; Psychosis Endophenotypes International Consortium; SynGO Consortium; Adolfsson, Rolf; Arango, Celso; Baune, Bernhard T; Belangero, Sintia Iole; Børglum, Anders D; Braff, David; Bramon, Elvira; Buxbaum, Joseph D; Campion, Dominique; Cervilla, Jorge A; Cichon, Sven; Collier, David A; Corvin, Aiden; Curtis, David; Forti, Marta Di; Domenici, Enrico; Ehrenreich, Hannelore; Escott-Price, Valentina; Esko, Tõnu; Fanous, Ayman H; Gareeva, Anna; Gawlik, Micha; Gejman, Pablo V; Gill, Michael; Glatt, Stephen J; Golimbet, Vera; Hong, Kyung Sue; Hultman, Christina M; Hyman, Steven E; Iwata, Nakao; Jönsson, Erik G; Kahn, René S; Kennedy, James L; Khusnutdinova, Elza; Kirov, George; Knowles, James A; Krebs, Marie-Odile; Laurent-Levinson, Claudine; Lee, Jimmy; Lencz, Todd; Levinson, Douglas F; Li, Qingqin S; Liu, Jianjun; Malhotra, Anil K; Malhotra, Dheeraj; McIntosh, Andrew; McQuillin, Andrew; Menezes, Paulo R; Morgan, Vera A; Morris, Derek W; Mowry, Bryan J; Murray, Robin M; Nimgaonkar, Vishwajit; Nöthen, Markus M; Ophoff, Roel A; Paciga, Sara A; Palotie, Aarno; Pato, Carlos N; Qin, Shengying; Rietschel, Marcella; Riley, Brien P; Rivera, Margarita; Rujescu, Dan; Saka, Meram C; Sanders, Alan R; Schwab, Sibylle G; Serretti, Alessandro; Sham, Pak C; Shi, Yongyong; St Clair, David; Stefánsson, Hreinn; Stefansson, Kari; Tsuang, Ming T; van Os, Jim; Vawter, Marquis P; Weinberger, Daniel R; Werge, Thomas; Wildenauer, Dieter B; Yu, Xin; Yue, Weihua; Holmans, Peter A; Pocklington, Andrew J; Roussos, Panos; Vassos, Evangelos; Verhage, Matthijs; Visscher, Peter M; Yang, Jian; Posthuma, Danielle; Andreassen, Ole A; Kendler, Kenneth S; Owen, Michael J; Wray, Naomi R; Daly, Mark J; Huang, Hailiang; Neale, Benjamin M; Sullivan, Patrick F; Ripke, Stephan; Walters, James T R; O'Donovan, Michael C; Schizophrenia Working Group of the Psychiatric Genomics Consortium
- Year
- 2022
- Journal
- Nature
- PMID
- 35396580
- DOI
- 10.1038/s41586-022-04434-5
- PMCID
- PMC9392466
Schizophrenia has a heritability of 60-80%, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.
Overview of GWAS and gene prioritisation.Flow diagram summarising GWAS, fine-mapping and SMR analyses and how these informed gene prioritisation.
LLM interpretation
This is a flow diagram illustrating the process of GWAS and gene prioritisation. The workflow moves from initial GWAS and downstream analyses (blue boxes), including FINEMAP and SMR, through an extended GWAS filter to various prioritisation criteria (orange boxes). The final output is a set of 120 unique prioritised genes, consisting of 70 FINEMAP genes and 55 SMR genes.
Gene set enrichment tests at genome-wide level and for protein coding genes containing FINEMAP credible SNPs.Gene sets tested were retrieved from sequencing studies of schizophrenia (SCHEMA; companion paper), autism-spectrum disorder33 and developmental disorders32. Sets representing genes that are intolerant to loss-of function mutations40 (LoF-intolerant) and brain-expressed genes41are also shown. A) MAGMA gene set enrichment analysis, dotted line indicates nominal significance (p=0.05). B) Logistic regression (with Firth’s bias reduction method) showing the odds-ratio (and 95% CI) for association between protein-coding genes containing at least 1 credible FINEMAP SNP (N=418 after excluding genes with no LoF-intolerance data) and genes from the sets indicated. Odds-ratios are relative to protein-coding genes within GWAS K≤3.5 loci (1,283 genes, squares) or across the genome excluding the xMHC (19,547 genes; circles). Dotted line indicates no enrichment.
LLM interpretation
This figure consists of two panels analyzing gene set enrichment. Panel A is a bar chart showing $-\log_{10} P_{\text{MAGMA}}$ values for various gene sets, with the highest enrichment seen in the "SCHEMA (p<0.001)" and "SCHEMA (p<0.05)" groups, all exceeding the nominal significance threshold (dotted line). Panel B is a forest plot displaying Firth's odds ratios and 95% confidence intervals for protein-coding genes containing FINEMAP credible SNPs compared to GWAS loci (squares) or genome-wide genes (circles), showing enrichment (OR > 1) for most tested sets.
Mapping of all FINEMAP/SMR genes (A) and prioritised genes (B) to synaptic locations using SYNGO.Sunburst plots depict synaptic locations with child terms in concentric rings, starting with the synapse (center), pre- and postsynaptic locations in the first ring and child terms in subsequent ring. The number of genes in each term is indicated by the colour scheme in the legend. A) FINEMAP/SMR genes are protein coding genes tagged by at least one credible SNP identified by FINEMAP and/or associated using SMR (N=470) of which N=58 are SynGO annotated, 51 to cellular components. B) Prioritised (Extended Data Table 1; N=106) of which 15 are SynGO annotated, 14 to cellular components.
LLM interpretation
This figure consists of two sunburst plots mapping genes to synaptic locations using SYNGO, with the center representing the synapse and outer rings representing increasingly specific child terms. Plot A shows the localization of all FINEMAP/SMR genes (N=470), while Plot B shows prioritized genes (N=106), with color intensity indicating gene count. Both plots categorize genes into "PRESYNAPSE" and "POSTSYNAPSE" regions, with Plot B specifically labeling individual genes such as *DLGAP2*, *SNAP91*, and *GRIN2A* associated with specific synaptic locations.
Associations between schizophrenia and cell types from multiple brain regions in human and mouse.a, b, The mean of the evidence (−log10 P value) obtained from two methods (MAGMA and LDSC) for testing GWAS data for enrichment of associations in genes with high expression in cell types. 15 human cell types (derived from single nuclei) from the cortex and hippocampus (a) and 24 cell types (derived from single-cell RNA-seq) from 5 different brain regions in mouse (cortex, hippocampus, striatum, midbrain and hypothalamus) and from specific enrichments of oligodendrocytes, serotonergic neurons, dopaminergic neurons and cortical parvalbuminergic interneurons (b). Bar colour indicates whether the cell type is significantly associated with both methods, one method or none. The black vertical line represents the significance threshold corrected for the total number of cell types tested in each analysis. Results obtained for previous iterations of schizophrenia GWAS12,18 are shown for comparison. Pyramidal SS, pyramidal neurons from the somatosensory cortex; pyramidal CA1/CA3, pyramidal neurons from the CA1/CA3 region of the hippocampus. Where types of cell (such as interneurons) formed sub-clusters in the source data, these are designated by the suffix 1 or 2.
LLM interpretation
This figure consists of two sets of horizontal bar charts (a and b) showing the mean evidence (−log10 P value) for associations between schizophrenia GWAS data and various cell types in humans (a) and mice (b). The x-axes represent the mean significance across three different GWAS iterations (2011, 2014, and 2020/2021), with a black vertical line indicating the corrected significance threshold. Bars are color-coded to indicate whether the association was significant for both methods (red), MAGMA only (olive), LDSC only (blue), or neither (purple).
Primary GWAS Manhattan plotThe x-axis indicates chromosomal position and the y-axis is the significance of association (−log10(P)). The red line represents genome-wide significance level (5×10−8). SNPs in green are in linkage disequilibrium (LD; R2 >0.1) with index SNPs (diamonds) which represent LD independent genome-wide significant associations.
LLM interpretation
This is a GWAS Manhattan plot showing the association between genetic variants and a trait across the genome. The x-axis represents chromosomal position and the y-axis represents the significance of association as $-\log_{10}(P)$. Multiple peaks exceed the genome-wide significance threshold (red line at $5 \times 10^{-8}$), with the most significant association appearing on chromosome 6. Index SNPs are marked as diamonds, with associated SNPs in linkage disequilibrium shown in green.
Polygenic risk predictionA) Distributions of liability scale R2 across 98 left-out-cohorts for polygenic risk scores built from SNPs with different p-value thresholds. Distributions of liability R2 (assuming schizophrenia life-time risk of 1%) are shown for each p-value threshold, with point size representing size of the left-out cohort and colour representing ancestry. The median liability R2 is represented as a horizontal black line. B) Liability R2 of predicted and observed phenotypes in left-out cohorts using variants with p-value threshold p=0.05, from the fixed effect meta-analysis of variant effects, unadjusted for multiple comparisons. The polygenic risk scores are derived from two separate sets of leave-one-out GWAS meta-analyses: y-axis R2 based on the results of primary GWAS including all ancestries; x axis R2 based on cohorts of the same ancestry as the test samples. Circles denote core PGC samples. Triangles denote African American and Latino samples processed external to PGC by the providing author.
LLM interpretation
Figure A is a violin plot showing the distribution of liability $R^2$ across 98 cohorts for various p-value thresholds, with median values indicated by horizontal black lines and points colored by ancestry. Liability $R^2$ generally increases as the p-value threshold increases, peaking around $P_T=0.05$ to $0.2$. Figure B is a scatter plot comparing combined-ancestry liability $R^2$ (y-axis) against within-ancestry liability $R^2$ (x-axis) for a p-value threshold of 0.05, with data points categorized by ancestry and study size.
Association between 37 human tissues and schizophrenia.The mean of the evidence (-log10P) obtained from two methods (MAGMA, LDSC) for testing GWAS data for enrichment of association in genes with high expression in each tissue as determined from bulk RNA-seq20. The bar colour indicates whether gene expression in the tissue is significantly associated with both methods, one method or none. The black vertical line represents the significance threshold corrected for the total number of tissues tested in this experiment. We also analysed previous waves of PGC schizophrenia GWAS11,21 for comparison.
LLM interpretation
This figure consists of three horizontal bar charts comparing the association between 37 human tissues and schizophrenia across three GWAS datasets (PGC 2011, 2014, and 2021). The x-axis represents the mean $-\log_{10}(p\text{-value})$, with a black vertical line indicating the significance threshold. Bars are color-coded by significance across two methods (MAGMA and LDSC), showing that brain-related tissues consistently exhibit the highest association and significance across all three datasets.
| Name | Type |
|---|---|
| 313 independent SNPs local | variant |
| 9 cohorts local | cohort |
| ACE | gene |
| African American | cohort |
| African-Americans | cohort |
| all ancestry primary GWAS local | cohort |
| amygdala | anatomy |
| anti-hypertensive drugs local | drug |
| ASD | phenotype |
| Asian | cohort |
| ATP2A2 local | gene |
| autism spectrum disorder | phenotype |
| Bcl11b | gene |
| bipolar disorder | phenotype |
| blood | drug |
| brain | anatomy |
| brain tissue | anatomy |
| CACNA1C | gene |
| case cohort | cohort |
| case-control sample | cohort |
| cases | cohort |
| chr8 inversion local | variant |
| Chronic symptoms local | phenotype |
| CLCN3 local | gene |
| clozapine | drug |
| CNS neurons | phenotype |
| CNVs | variant |
| cognition | phenotype |
| combined ancestry GWAS local | cohort |
| common variant genetic liability local | variant |
| common variants | cohort |
| control cohorts local | cohort |
| controls | cohort |
| cortex | anatomy |
| credible SNP local | variant |
| CUL9 local | gene |
| Darier Disease local | phenotype |
| DCC | gene |
| DCLK3 | gene |
| DD | phenotype |
| deCODE | cohort |
| decode genetics | cohort |
| dentate gyrus | anatomy |
| Dlgap2 | gene |
| East Asian | cohort |
| EpiXcan45 local | drug |
| European ancestry | cohort |
| European ancestry cohort (deCODE genetics) local | cohort |
| European population | cohort |
| Extended GWAS local | cohort |
| FAM120A | gene |
| family-based cohorts local | cohort |
| fetal brain | anatomy |
| fetal brain local | cohort |
| FINEMAP | drug |
| FINEMAP35 local | drug |
| FINEMAP credible SNPs local | variant |
| Foxp1 | gene |
| FUSION44 local | drug |
| GABA | phenotype |
| Gabbr2 | gene |
| genes with high expression in cortical inhibitory interneurons local | gene |
| genes with high expression in excitatory glutamatergic neurons (cerebral cortex) local | gene |
| genes with high expression in excitatory glutamatergic neurons (hippocampus) local | gene |
| genes with high expression in inhibitory medium spiny neurons local | gene |
| Genes with rare variants increasing risk of ASD and DD local | gene |
| glutamatergic neurons | phenotype |
| GPM6A | gene |
| GRIN2A | gene |
| GRM1 | gene |
| GTEx | cohort |
| GTEx v8 | cohort |
| Haplotype Reference Consortium | cohort |
| Health and Retirement Study | cohort |
| Health and social costs local | phenotype |
| hindbrain | anatomy |
| hippocampus | anatomy |
| Hispanic | phenotype |
| HRC reference dataset local | cohort |
| hypothalamus | anatomy |
| IL1RAPL1 | gene |
| index SNP | cohort |
| IRF3 | gene |
| Jackson Laboratory local | cohort |
| Klf6 | gene |
| Latino cohort local | cohort |
| LD clumping procedure local | drug |
| LD-independent variants local | variant |
| LD‑independent variants (r2 > 0.1) local | variant |
| loss of function intolerance local | phenotype |
| LRRC4B local | gene |
| MAPK3 | gene |
| matched ancestry GWAS local | cohort |
| meta-analysis | cohort |
| MHC local | variant |
| MHC region | gene |
| midbrain | anatomy |
| missense variants | variant |
| Mouse CNS cell type dataset (265 cell types) local | cohort |
| Myt1l | gene |
| neurodevelopmental disorder | phenotype |
| neuroimaging measures local | phenotype |
| NMDA receptor | drug |
| non-Asian samples local | cohort |
| non-EUR samples local | cohort |
| PAK6 local | gene |
| PGC | cohort |
| PGC cohorts | cohort |
| PGC dataset | cohort |
| PGC GWAS | cohort |
| PJA1 local | gene |
| polygenic risk score | cohort |
| prefrontal cortex | anatomy |
| primary GWAS local | cohort |
| primary GWAS dataset local | cohort |
| protein-coding gene | gene |
| PTPRD | gene |
| rare copy number variants | variant |
| rare damaging coding variants local | variant |
| rare variant | cohort |
| Reduced life expectancy local | phenotype |
| RERE | gene |
| rs13107325 | variant |
| rs3768644 local | variant |
| rs4766428 local | variant |
| SCHEMA local | cohort |
| SCHEMA consortium local | cohort |
| SCHEMA genes local | gene |
| schizophrenia | phenotype |
| schizophrenia GWAS | cohort |
| schizophrenia/schizoaffective disorder local | phenotype |
| serious physical illness | phenotype |
| sex | phenotype |
| SLC39A8 | gene |
| SMR | drug |
| SNAP91 local | gene |
| SNP | cohort |
| SNP-based heritability | phenotype |
| SP4 | gene |
| STAG1 | gene |
| striatum | anatomy |
| subjects | cohort |
| suicide | phenotype |
| SynGO database local | drug |
| thalamus | anatomy |
| THAP8 local | gene |
| trio-based cohort local | cohort |
| UTR variant local | variant |
| voltage-gated calcium channel | drug |
| whole blood | anatomy |
| WSCD2 local | gene |
| X-Chromosomal genotypes local | variant |
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In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| 3D genetic architecture of schizophrenia risk across three neuronal subtypes. | Powell SK et al. | — | 2026 | → |
| A brief visual evoked potential (VEP) modulation assessment of experience-dependent plasticity recorded via wireless dry-EEG headset in Phase 1 clinical units. | McWilliams EC et al. | — | 2026 | → |
| A causal coding variant regulating alternative splicing of <i>DOC2A</i> at 16p.11.2 GWAS locus influences susceptibility to schizophrenia. | Zhou D et al. | — | 2026 | → |
| A co-culture model of dopaminergic and glutamatergic neurons derived from patients with idiopathic schizophrenia reveals a hypodopaminergic phenotype. | Hartmann SM et al. | — | 2026 | → |
| Adaptive evolution of gene regulatory networks in mammalian neocortex. | Li Z et al. | — | 2026 | → |
| A genetic atlas of relationships between circulating metabolites and liability to psychiatric conditions. | Kiltschewskij DJ et al. | — | 2026 | → |
| A longitudinal DNA methylation atlas and its link to brain structure and mental health. | Chen D et al. | — | 2026 | → |
| Altered Neuronal Architecture in Induced Pluripotent Stem Cells-Derived Neurons from Patients with Schizophrenia Harboring <i>CNTNAP2</i> Deletion. | Shekhar BR et al. | — | 2026 | → |
| A meta-analysis of single-nucleus expression quantitative trait loci linking genetic risk to brain disorders. | Jang B et al. | — | 2026 | → |
| A multi-cohort assessment of the polygenic prediction in ADHD treatment response. | Rovaris DL et al. | — | 2026 | → |
| Antipsychotic-like effects of the selective Rho-kinase 2 inhibitor KD025 in genetic and pharmacological mouse models of schizophrenia. | Tanaka R et al. | — | 2026 | → |
| Are polygenic scores for psychiatric and substance use outcomes "ready" for clinical application? Current state and next steps. | Dick DM et al. | — | 2026 | → |
| Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium. | Valone JM et al. | — | 2026 | → |
| Association Between Maternal Genome-Wide Polygenic Scores for Psychiatric and Neurodevelopmental Disorders and Adverse Perinatal Events: A Danish Population-Based Study. | Ge F et al. | — | 2026 | → |
| Association Between Peptide Antigen-Related Antibody Levels and the Short- and Long-Term Efficacy of Antipsychotic Treatment in Drug-Naïve First-Episode Schizophrenia Patients. | Zheng Y et al. | — | 2026 | → |
| Associations between cerebellum and major psychiatric disorders: a bidirectional Mendelian randomization study. | Zhang R et al. | — | 2026 | → |
| Associations Between Genetic Risk, Physical Activities, and Distressing Psychotic-like Experiences. | Ku BS et al. | — | 2026 | → |
| Associations between mosaic loss and schizophrenia or bipolar disorder of young age. | Uchiyama S et al. | — | 2026 | → |
| Associations of polygenic risk scores for major depression and depression severity: an investigation of 105 623 individuals with 16 years follow-up. | Haram M et al. | — | 2026 | → |
| Astrocytic connexin 43 hemichannel dysregulation drives prefrontal circuit dysfunction and schizophrenia-like behaviors. | Wang L et al. | — | 2026 | → |
| ATP and major affective disorders: the involvement of P2X receptors in pathophysiology. | Mattova S et al. | — | 2026 | → |
| A transcriptomic dimension of neuronal and immune gene programs within the subgenual anterior cingulate cortex in schizophrenia. | Smith RL et al. | — | 2026 | → |
| A trans-synaptic IgLON adhesion molecular complex directly contacts and clusters a nicotinic receptor. | Mialon M et al. | — | 2026 | → |
| Autism Heterogeneity Related to Preterm Birth: Multi-Ancestry Results From the Simons Foundation Powering Autism Research for Knowledge Sample. | Chatzigeorgiou C et al. | — | 2026 | → |
| B cell pathways implicate shared genetic architecture between schizophrenia and immune-mediated diseases. | Yuan R et al. | — | 2026 | → |
| Biological insights into schizophrenia from ancestrally diverse populations. | Bigdeli TB et al. | — | 2026 | → |
| Biological underpinnings and genetic predisposition to schizophrenia within microrna-137 regulatory pathways across brain development. | Stella C et al. | — | 2026 | → |
| Biophysical modeling of excitation/inhibition balance and conversion to psychosis in the clinical high risk syndrome. | Rodriguez-Sanchez J et al. | — | 2026 | → |
| Brain structures mediate the causal effects of socioeconomic status on mental disorders: A multi-stage Mendelian randomization study. | Pan S et al. | — | 2026 | → |
| Can psychiatric genetics advance without incorporating a lifecourse perspective? | Dennis JK et al. | — | 2026 | → |
| CARLIS: covariate-assisted replicability analysis for genome-wide association studies via triplet hidden Markov models. | Li Y et al. | — | 2026 | → |
| Causal effects of gut microbiota on schizophrenia: deciphering profiles mediated by cerebrospinal fluid metabolites in the gut-brain axis. | Yang Y et al. | — | 2026 | → |
| Causal inference in psychiatric research: how to critically evaluate and interpret mendelian randomization studies. | Garcia-Argibay M et al. | — | 2026 | → |
| Causal links among gut microbiota, immune-inflammatory and compensatory immune-regulatory systems, and schizophrenia. | Yang BZ et al. | — | 2026 | → |
| Causal relationship between tinnitus and 8 psychiatric phenotypes: a Mendelian randomization study. | Guo T et al. | — | 2026 | → |
| Characterization of the expression and function of schizophrenia risk gene Dtnbp1 in the suprachiasmatic nucleus. | Maloney GE et al. | — | 2026 | → |
| Clinical deep phenotyping of treatment response in schizophrenia (CDP-STAR): design and methodology of a prospective multimodal observational study. | Yakimov V et al. | — | 2026 | → |
| Clinical Genetic Testing in Schizophrenia: A Systematic Review and Meta-Analysis. | Brah HS et al. | — | 2026 | → |
| Cognitive reserve in non-affective first-episode psychosis: contributions of polygenic scores, early clinical features, and environment. | Forte MF et al. | — | 2026 | → |
| Combining xQTL and genome-wide association studies from diverse populations improves druggable gene discovery. | Lorincz-Comi N et al. | — | 2026 | → |
| Computation and resource efficient genome-wide association analysis for large-scale imaging studies. | Jiang Z et al. | — | 2026 | → |
| Context-specific expression quantitative trait loci dynamics uncover genetic pleiotropy in schizophrenia. | Ye L et al. | — | 2026 | → |
| Convergent coexpression reveals shared biological mechanisms underlying common and rare variant risk in six neuropsychiatric disorders. | Abe H et al. | — | 2026 | → |
| Cortical excitability mapping stratifies neurobiological subtypes of schizophrenia with genetic and molecular signatures. | Guo S et al. | — | 2026 | → |
| Detection of pleiotropic genetic factors and critical brain cell types linking insomnia with psychiatric disorders. | Xue B et al. | — | 2026 | → |
| Developmental modulation of schizophrenia risk gene methylation in offspring exhibiting cognitive deficits following maternal immune activation. | Woods RM et al. | — | 2026 | → |
| Differential <i>SP4</i> expression and HSP60 abundance in buccal swabs from patients with schizophrenia. | Crosta CM et al. | — | 2026 | → |
| DisCP-Atlas: a comprehensive resource mapping cellular processes to complex diseases. | Wang FQ et al. | — | 2026 | → |
| Disorder-Specific Genetic Effects Drive the Associations Between Psychopathology and Cognitive Functioning. | Liao W et al. | — | 2026 | → |
| Dissecting gene regulatory networks governing human cortical cell fate. | Ding JW et al. | — | 2026 | → |
| Dissecting the genetic interaction between breast cancer risk and schizophrenia: evidence from crosstrait genome-wide analysis. | Zhang M et al. | — | 2026 | → |
| Dissecting the genetic relationship between severe mental disorders and autoimmune diseases. | Wiström ED et al. | — | 2026 | → |
| Dissecting the shared genetic landscape of schizophrenia and hippocampal subfields: A genome-wide cross-trait analysis. | Guo L et al. | — | 2026 | → |
| Disturbances of paraventricular thalamic nucleus neurons in bipolar disorder revealed by single-nucleus analysis. | Nishioka M et al. | — | 2026 | → |
| Divergent Patterns of Genetic Overlap Between Severe Mental Disorders and Metabolic Markers. | van der Meer D et al. | — | 2026 | → |
| DNA methylation signatures associated with early-onset schizophrenia in Chinese patients. | Zhan N et al. | — | 2026 | → |
| Dynamic subtype- and context-specific subcellular RNA regulation in growth cones of developing neurons of the cerebral cortex. | Veeraraghavan P et al. | — | 2026 | → |
| Dysregulated miRNAs and downstream gene expression associated with poor treatment response in first-episode psychosis. | Yu SC et al. | — | 2026 | → |
| Dysregulation of the BRD2-FGF17 Signal Pathway Induces Abnormal Forebrain Development Associated with Schizophrenia. | Yu X et al. | — | 2026 | → |
| Elevated calneuron-1, an accessory subunit of muscarinic receptors, induces frontotemporal dysconnectivity and schizophrenia-like deficits. | Oelschlegel AM et al. | — | 2026 | → |
| Elucidating biopsychosocial mechanisms in migraine: an integrative analytics approach combining genetics, neuroimaging, and machine learning. | Liu YC et al. | — | 2026 | → |
| Enduring Mental Health in Childhood and Adolescence: Prevalence, Prediction, and Genetic Architecture. | Alrouh H et al. | — | 2026 | → |
| Executive function mediates the effects of genetic liability to schizophrenia on behavior and functioning in a community sample of children and adolescents. | Fonseca L et al. | — | 2026 | → |
| Exploration of the relationship between autoimmune neurologic diseases and mental disorders: evidence from Mendelian randomization study. | Shen Y et al. | — | 2026 | → |
| Exploring the Behavioral and Emotional Manifestation of Polygenic Risk for Psychiatric Disorders Among Schoolchildren. | Alemany S et al. | — | 2026 | → |
| Exploring the genetic overlap between substance use disorder and educational attainment. | Cabana-Domínguez J et al. | — | 2026 | → |
| Familial coaggregation and shared genetic loading of mental disorders and cardiovascular diseases. | Chen PC et al. | — | 2026 | → |
| Fine-mapping a genome-wide meta-analysis of 98,374 migraine cases identifies 181 sets of candidate causal variants. | Hautakangas H et al. | — | 2026 | → |
| Functional genomic profiling of schizophrenia-associated genes reveals key microglial regulators. | Horng JE et al. | — | 2026 | → |
| Functional implications of polygenic risk for schizophrenia in human neurons. | Michael Deans PJ et al. | — | 2026 | → |
| Functional variants at 1p36.23 confer risk of schizophrenia through modulating RERE. | Liu Y et al. | — | 2026 | → |
| Gene-Environment Correlation Across Trauma Subtypes and Developmental Timing: Evidence From the EU-GEI and ALSPAC. | Trotta G et al. | — | 2026 | → |
| Gene-Environment Interaction of Rims1 and Adolescent Social Isolation on Schizophrenia-Like Behaviors in Mice. | Lin LH et al. | — | 2026 | → |
| Genetic and functional insights into long noncoding RNAs in schizophrenia. | Dang X et al. | — | 2026 | → |
| Genetic architecture of postpartum psychosis: from common to rare genetic variation. | Jung S et al. | — | 2026 | → |
| Genetic control of non-coding RNAs in the human brain and their implications for complex traits. | Chen L et al. | — | 2026 | → |
| Genetic correlates of self-harm hospitalization: Insights from polygenic scores in the Taiwan Biobank. | Hsu YC et al. | — | 2026 | → |
| Genetic Evidence for a Neuroimmune Model of Schizophrenia. | van der Walt K et al. | — | 2026 | → |
| Genetic evidence for causal relationship between general cognition and treatment resistance in schizophrenia. | Li C et al. | — | 2026 | → |
| Genetic evidence for the association between schizophrenia and obesity. | Su MH et al. | — | 2026 | → |
| Genetic exploration of the relationship between liability to psychiatric disorders and acne vulgaris. | Mitchell BL et al. | — | 2026 | → |
| Genetic interactions between bioactive ingredients in traditional Chinese medicine and major depressive disorder, bipolar disorder, and schizophrenia. | Xu Y et al. | — | 2026 | → |
| Genetic pathways linking oxytocin-vasotocin hypothalamic subunit architecture with psychiatric and metabolic traits. | Sartorius AI et al. | — | 2026 | → |
| Genetic predisposition to unwanted side effects under antidepressants and antipsychotics: a molecular-genetic study of 902 patients over 6 weeks. | Stassen HH et al. | — | 2026 | → |
| Genetics of SSRI antidepressant use and relationship to psychiatric and medical traits. | Levey D et al. | — | 2026 | → |
| Genetics of tardive dyskinesia: a review of progress over the past decade. | Zai CC et al. | — | 2026 | → |
| Genetics to Improve Outcomes in Schizophrenia (GENios): A within-case molecular genetic study protocol. | Smart SE et al. | — | 2026 | → |
| Genetic studies of psychosocial disability establish correlations and causal relationships with neuropsychiatric disorders. | Doherty E et al. | — | 2026 | → |
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| Genetic Variations in the Dopaminergic Signaling Pathways and Their Implications for Antipsychotics Pharmacogenetics. | Olasore HSA et al. | — | 2026 | → |
| Genome-wide association studies of lifetime and frequency of cannabis use in 131,895 individuals. | Thorpe HHA et al. | — | 2026 | → |
| Genome-wide association study of major anxiety disorders in 122,341 European-ancestry cases identifies 58 loci and highlights GABAergic signaling. | Strom NI et al. | — | 2026 | → |
| Genome-Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes. | Wang S et al. | — | 2026 | → |
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| Genetic analyses point to alterations in immune-related pathways underpinning the association between psychiatric disorders and COVID-19. | Monistrol-Mula A et al. | — | 2025 | → |
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| Integrating genetic regulation and schizophrenia-specific splicing quantitative expression with GWAS prioritizes novel risk genes for schizophrenia. | Li X et al. | — | 2025 | → |
| Integrating genetics and transcriptomics to characterize shared mechanisms in digestive diseases and psychiatric disorders. | Ding H et al. | — | 2025 | → |
| Integrating genome-wide and epigenome-wide associations for antipsychotic induced extrapyramidal side effects. | Yao K et al. | — | 2025 | → |
| Integrating HiTOP and RDoC frameworks part II: shared and distinct biological mechanisms of externalizing and internalizing psychopathology. | Davis CN et al. | — | 2025 | → |
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| Integrating rare variant genetics and brain transcriptome data implicates novel schizophrenia putative risk genes. | Han S et al. | — | 2025 | → |
| Integrative analysis of cerebrospinal fluid biomarkers, metabolomics, and polygenic risk reveals novel metabolite associations with Alzheimer's disease. | Francia M et al. | — | 2025 | → |
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| In the genetics of the beholder: gene-environment interplay for internalising and externalising behaviours using polygenic scores and adolescent perceptions of parenting. | Rainy NR et al. | — | 2025 | → |
| Investigating antidiabetic drug targets as potential therapeutic modulators for schizophrenia. | Huang YS et al. | — | 2025 | → |
| Investigating the association of the plasma lipidomic profile with cognitive performance and genetic risk in the PsyCourse study. | Oraki Kohshour M et al. | — | 2025 | → |
| Investigating the causal associations between neuroticism, schizophrenia, and cardiovascular diseases using Mendelian randomization. | Pu L et al. | — | 2025 | → |
| Investigating the gut microbiome in schizophrenia cases versus controls: South Africa's version. | Rust C et al. | — | 2025 | → |
| Investigating the shared genetic architecture between schizophrenia and sex hormone traits. | He X et al. | — | 2025 | → |
| iPSC-modelling reveals genetic associations and morphological alterations of oligodendrocytes in schizophrenia. | Chang MH et al. | — | 2025 | → |
| Isoform-level profiling of m<sup>6</sup>A epitranscriptomic signatures in human brain. | Gleeson J et al. | — | 2025 | → |
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| Joint analysis of <i>de novo</i> mutations from autism spectrum disorder, schizophrenia, congenital heart disease, and other developmental disorders improves detection power and implicates shared molecular pathways and CNS processes. | Kealhofer M et al. | — | 2025 | → |
| Just a SNP away: The future of <i>in vivo</i> massively parallel reporter assay. | Degner KN et al. | — | 2025 | → |
| Key regions aberrantly connected within cerebello-thalamo-cortical circuit and their genetic mechanism in schizophrenia: an fMRI meta-analysis and transcriptome study. | Wei Y et al. | — | 2025 | → |
| Kynurenic Acid and Promotion of Activity-Dependent Synapse Elimination in Schizophrenia. | Orhan F et al. | — | 2025 | → |
| Leveraging genomic and transcriptomic data of diverse ancestry to uncover mechanisms of psychiatric risk in the adult and developing brain. | Jajoo A et al. | — | 2025 | → |
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| Limitations and Potential of Polygenic Risk Scores for Major Psychiatric Disorders. | Shen X | — | 2025 | → |
| Limited evidence of association between dysregulated immune marker levels and telomere length in severe mental disorders. | Ormerod MBEG et al. | — | 2025 | → |
| Linking Genome-Wide Association Studies to Pharmacological Treatments for Psychiatric Disorders. | Arnatkeviciute A et al. | — | 2025 | → |
| Linking genomic and proteomic signatures to brain amyloid burden: insights from GR@ACE/DEGESCO. | Puerta R et al. | — | 2025 | → |
| Lithium partially rescues gene expression and enhancer activity from heterozygous knockout of AKAP11 while inducing novel differential changes. | Farhangdoost N et al. | — | 2025 | → |
| Local patterns of genetic sharing between neuropsychiatric and insulin resistance-related conditions. | Fanelli G et al. | — | 2025 | → |
| Long-read sequencing reveals the RNA isoform repertoire of neuropsychiatric risk genes in human brain. | De Paoli-Iseppi R et al. | — | 2025 | → |
| Loss of the neuronal kinase DCLK3 leads to anxiety-like behaviour and memory deficits. | de Longprez L et al. | — | 2025 | → |
| Low Stability and Specificity of Polygenic Risk Scores for Major Psychiatric Disorders Limit Their Clinical Utility. | Mollon J et al. | — | 2025 | → |
| <i>Nkapl</i> deletion drives cognitive deficits through mPFC interneuron dysfunction in a mouse model of schizophrenia. | Yang Y et al. | — | 2025 | → |
| Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics. | Abe H et al. | — | 2025 | → |
| Mapping the cellular etiology of schizophrenia and complex brain phenotypes. | Duncan LE et al. | — | 2025 | → |
| Mapping the Genetic Landscape of Psychiatric Disorders With the MiXeR Toolset. | van der Meer D et al. | — | 2025 | → |
| Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions. | Vattathil SM et al. | — | 2025 | → |
| Measures of General Intelligence and Risk for Alcohol Use Disorder. | Capusan AJ et al. | — | 2025 | → |
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| Meta-analysis of the brain transcriptomes of multiple genetic mouse models of schizophrenia highlights dysregulation in striatum and thalamus. | Perzel Mandell KA et al. | — | 2025 | → |
| Metabolomics Network Analysis of Various Genotypes Associated with Schizophrenia Gene Variant. | Rajula HSR et al. | — | 2025 | → |
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| Microdosing Psychedelics to Restore Synaptic Density in Schizophrenia. | Sapienza J et al. | — | 2025 | → |
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| Direct targets of MEF2C are enriched for genes associated with schizophrenia and cognitive function and are involved in neuron development and mitochondrial function. | Ali D et al. | — | 2024 | → |
| Discovering functional interactions among schizophrenia-risk genes by combining behavioral genetics with cell biology. | Ma D et al. | — | 2024 | → |
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| Enhancing Transcriptomic Insights into Neurological Disorders Through the Comparative Analysis of Shapley Values. | Castro-Martínez JA et al. | — | 2024 | → |
| Evaluating the distinct effects of body mass index at childhood and adulthood on adult major psychiatric disorders. | Xiao P et al. | — | 2024 | → |
| Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases. | Gholipourshahraki T et al. | — | 2024 | → |
| Evidence for an Association Between a pH-Dependent Potassium Channel, TWIK-1, and the Accuracy of Smooth Pursuit Eye Movements. | Bargary G et al. | — | 2024 | → |
| Examining the role of common variants in rare neurodevelopmental conditions. | Huang QQ et al. | — | 2024 | → |
| Exploration on the potential efficacy and mechanism of methyl salicylate glycosides in the treatment of schizophrenia based on bioinformatics, molecular docking and dynamics simulation. | Wang X et al. | — | 2024 | → |
| Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features. | Gao J et al. | — | 2024 | → |
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| Genome-Wide and Exome-Wide Association Study Identifies Genetic Underpinning of Comorbidity between Myocardial Infarction and Severe Mental Disorders. | Jiang B et al. | — | 2024 | → |
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