Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.
paper
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- Authors
- Huckins, Laura M; Dobbyn, Amanda; Ruderfer, Douglas M; Hoffman, Gabriel; Wang, Weiqing; Pardiñas, Antonio F; Rajagopal, Veera M; Als, Thomas D; T Nguyen, Hoang; Girdhar, Kiran; Boocock, James; Roussos, Panos; Fromer, Menachem; Kramer, Robin; Domenici, Enrico; Gamazon, Eric R; Purcell, Shaun; CommonMind Consortium; Schizophrenia Working Group of the Psychiatric Genomics Consortium; iPSYCH-GEMS Schizophrenia Working Group; Demontis, Ditte; Børglum, Anders D; Walters, James T R; O'Donovan, Michael C; Sullivan, Patrick; Owen, Michael J; Devlin, Bernie; Sieberts, Solveig K; Cox, Nancy J; Im, Hae Kyung; Sklar, Pamela; Stahl, Eli A
- Year
- 2019
- Journal
- Nature genetics
- PMID
- 30911161
- DOI
- 10.1038/s41588-019-0364-4
- PMCID
- PMC7034316
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.
Replication of DLPFC prediction models in independent data.Measured gene expression (ROSMAP RNA-seq) was compared to predicted genetically-regulated gene expression for CMC DLPFC and 12 GTeX predictor databases. Replication R2 values are significantly higher for the DLPFC than for the 12 GTEX brain expression models.A. Distribution of RR2 values of CMC DLPFC predictors in ROSMAP data. Mean RR2 = 0.056. 47.7% of genes have RR2 >= 0.01. Boxplots show mean, quartiles,; whiskers show full range of data.B. Distribution of RR2 values of 12 GTeX predictors in ROSMAP data.Table of sample sizes and p-val thresholds for CMC DLPFC and GTeX data. Number of samples, number of genes in the prediXcan model and number of eGenes are all significantly correlated with predictor performance in ROSMAP data (spearman correlation test).
SCZ associations resultsA) 413 genes are associated with SCZ across 12 brain tissues. Each point represents one gene-tissue pair.B) 67 genes remain significant outside the MHC after stepwise conditional analysisC) Number of genome-wide significant loci, outside the MHC region, identified in each brain region. These trends are partly driven by differences in power between brain regions.Abbreviations are as follows; CB- Cerebellum; CX- Cortex; FL- Frontal Cortex; DLPFC- Dorso-lateral pre-frontal cortex; CB HEMI- Cerebellar Hemisphere; HIP- Hippocampus; PIT- Pituitary Gland; HTH- Hypothalamus; NAB- Nucleus Accumbens (Basal Ganglia); PUT- Putamen (Basal Ganglia); CAU- Caudate (Basal Ganglia); CNG- Anterior Cingulate Cortex
SCZ-associated genes are co-expressed throughout development and across brain regionsA) Brain tissues selected for each of four brainspan regions. Brainspan includes 525 samples from 43 unique individuals. Region 1: IPC, V1C, ITC, OFC, STC, A1C; Region 2:S1C, M1C, DFC, VFC, MFC; Region 3:HIP, AMY, STR; Region 4: CBAverage clustering coefficients were calculated for all pairs of SCZ-associated genes, and compared to average clustering coefficients for 100,000 permuted gene networks to obtain empirical significance levels.
Gene expression patterns for SCZ-associated genes cluster into four groups, relating to distinct spatiotemporal expression.Brain regions are shown in figure 3a.A. 29 genes are expressed in the early-mid pre-natal period (4-24 post-conception weeks)B. 15 genes are expressed throughout development; subclusters correspond to either specific expression in region 4, or expression across the brainC. Ten genes are expressed in the late-prenatal (25-38pcw) and post-natal periodD. 12 genes are expressed in the late pre-natal period (25-39pcw)
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| 60 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Identifying independent associations | Equation 3: GREX Beta adjustment β=βCoCoxGVAR |
| 61 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Identifying independent associations | Where GVAR is the variance of the GREX predictor for each gene. |
| 62 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Gene set Analyses | Pathway analyses were carried out using an extension to MAGMA86. P-values were assigned to genes… |
| 63 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Gene set Analyses | Significance levels were adjusted across all pathways included in either test using the… |
| 64 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Coexpression of SCZ genes throughout development | We investigate spatiotemporal expression of our associated genes using publicly available… |
| 65 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Coexpression of SCZ genes throughout development | First, we tested for correlation of gene expression for all SCZ-associated genes at each… |
| 66 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Coexpression of SCZ genes throughout development | For each of the 67 SCZ-associated genes, we calculated average expression at each spatiotemporal… |
| 67 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — In-silico replication of SCZ-associated genes in mouse models | We downloaded genotype, knock-out allele information and phenotyping data for ~10,000 mouse mutant… |
| 68 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — In-silico replication of SCZ-associated genes in mouse models | We calculated the rate of phenotypic abnormalities in all mouse lines with reduced expression of… |
| 69 | Online Methods (Limit 3,000 words, at end of manuscript, currently 2,064) — Data Availability | Our CMC-derived DLPFC prediction models are publicly available at… |
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| Citation | PMID | DOI | Status |
|---|---|---|---|
| (GTex Consortium). GTEx Portal. (2015). at <http://gtexportal.org/home/documentationPage> | — | — | — |
| A. BennettD, A. SchneiderJ, ArvanitakisZ & S. WilsonR Overview and Findings from the Religious Orders Study. Curr. Alzheimer Res 9, 628–645 (2012).2247186010.2174/156720512801322573PMC3409291 | — | — | — |
| AlbertFW & KruglyakL The role of regulatory variation in complex traits and disease. Nat. Rev. Genet 16, 197–212 (2015).2570792710.1038/nrg3891 | — | — | — |
| ArdlieKG The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science (80-. ). 348, 648–660 (2015).10.1126/science.1262110PMC454748425954001 | — | — | — |
| ArnoldWN The illness of Vincent van Gogh. J. Hist. Neurosci 13, 22–43 (2004).1537033510.1080/09647040490885475 | — | — | — |
| AshburnerM Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet 25, 25–29 (2000).1080265110.1038/75556PMC3037419 | — | — | — |
| AyadiA Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project. Mamm. Genome 23, 600–610 (2012).2296125810.1007/s00335-012-9418-yPMC3463797 | — | — | — |
| BahlE, KoomarT & MichaelsonJJ cerebroViz: An R package for anatomical visu-alization of spatiotemporal brain data. Bioinformatics 33, btw726 (2016).10.1093/bioinformatics/btw726PMC587079728011779 | — | — | — |
| BarbeiraA MetaXcan: Summary Statistics Based Gene-Level Association Method Infers Accurate PrediXcan Results. bioRxiv (2016). | — | — | — |
| BarbeiraAN Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun 9, (2018).10.1038/s41467-018-03621-1PMC594082529739930 | — | — | — |
| BaumanAL Cocaine and antidepressant-sensitive biogenic amine transporters exist in regulated complexes with protein phosphatase 2A. J. Neurosci 20, 7571–8 (2000).1102721610.1523/JNEUROSCI.20-20-07571.2000PMC6772869 | — | — | — |
| BautistaO, Vázquez-CaubetJC, ZhivagoEA & Dolores SáizM From metabolism to psychiatric symptoms: psychosis as a manifestation of acute intermittent porphyria. J. Neuropsychiatry Clin. Neurosci 26, E30 (2014).10.1176/appi.neuropsych.1304008324763782 | — | — | — |
| BenjaminY & HochbergY Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society 289–300 (1995). | — | — | — |
| BennettDA, SchneiderJA, BuchmanAS, BarnesLL & WilsonPAB and R.S Overview and Findings from the Rush Memory and Aging Project. Current Alzheimer Research 9, 646–663 (2012).2247186710.2174/156720512801322663PMC3439198 | — | — | — |
| BhattacharyyaKB & RaiS The neuropsychiatric ailment of Vincent Van Gogh. Ann. Indian Acad. Neurol 18, 6–9 (2014).10.4103/0972-2327.145286PMC435021525745302 | — | — | — |
| BioCarta. MSigDB Collections. (2017). | — | — | — |
| BonnotO Diagnostic and treatment implications of psychosis secondary to treatable metabolic disorders in adults: a systematic review. Orphanet J. Rare Dis 9, 65 (2014).2477571610.1186/1750-1172-9-65PMC4043981 | — | — | — |
| BoocockJ, GiambartolomeiC & StahlEA COLOC2. (2016). | — | — | — |
| BrowningR in The Poems of Robert Browning (eds. PorterC & ClarkeHA) 257–271 (Thomas Y. Cromwell and Company, 1896). | — | — | — |
| BrownSDM & MooreMW The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mammalian genome : official journal of the International Mammalian Genome Society 23, 632–640 (2012).2294074910.1007/s00335-012-9427-xPMC3774932 | — | — | — |
| CannonM Evidence for Early-Childhood, Pan-Developmental Impairment Specific to Schizophreniform Disorder. Arch. Gen. Psychiatry 59, 449 (2002).1198244910.1001/archpsyc.59.5.449 | — | — | — |
| CastellanoE RAS signalling through PI3-Kinase controls cell migration via modulation of Reelin expression. Nat. Commun 7, 11245 (2016).2707153710.1038/ncomms11245PMC4833863 | — | — | — |
| COCHRANWG THE COMPARISON OF PERCENTAGES IN MATCHED SAMPLES. Biometrika 37, 256–266 (1950).14801052 | — | — | — |
| ColantuoniC Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478, 519–523 (2012).10.1038/nature10524PMC351067022031444 | — | — | — |
| Consortium, C.-D. G. of the P. G. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379 (2013).2345388510.1016/S0140-6736(12)62129-1PMC3714010 | — | — | — |
| CooksonW, LiangL, AbecasisG, MoffattM & LathropM Mapping complex disease traits with global gene expression. Nat. Rev. Genet 10, 184–194 (2009).1922392710.1038/nrg2537PMC4550035 | — | — | — |
| CorreaR Vincent van Gogh: A pathographic analysis. Med. Hypotheses 82, 141–144 (2014).2433256110.1016/j.mehy.2013.11.022 | — | — | — |
| CroftD The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–7 (2014).2424384010.1093/nar/gkt1102PMC3965010 | — | — | — |
| DarnellJC FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–61 (2011).2178424610.1016/j.cell.2011.06.013PMC3232425 | — | — | — |
| de LeeuwCA, MooijJM, HeskesT & PosthumaD MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol 11, e1004219 (2015).2588571010.1371/journal.pcbi.1004219PMC4401657 | — | — | — |
| DobbynA Co-localization of Conditional eQTL and GWAS Signatures in Schizophrenia. Rev. (2017). | — | — | — |
| DobbynA Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS. Am. J. Hum. Genet (2018). doi:10.1016/j.ajhg.2018.04.011PMC599351329805045 | — | — | — |
| DuboisPCA Multiple common variants for celiac disease influencing immune gene expression. Nat. Genet 42, 295–302 (2010).2019075210.1038/ng.543PMC2847618 | — | — | — |
| Enriquez-BarretoL & MoralesM The PI3K signaling pathway as a pharmacological target in Autism related disorders and Schizophrenia. Mol. Cell. Ther 4, 2 (2016).2687787810.1186/s40591-016-0047-9PMC4751644 | — | — | — |
| Exome Aggregation Consortium Analysis of protein-coding genetic variation in 60,706 humans bioRxiv (Cold Spring Harbor Labs Journals, 2015). doi:10.1101/030338 | — | — | — |
| FromerM De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).2446350710.1038/nature12929PMC4237002 | — | — | — |
| FromerM Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia bioRxiv (Cold Spring Harbor Labs Journals, 2016). doi:10.1101/052209PMC508314227668389 | — | — | — |
| FromerM Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci 19, 1442–1453 (2016).2766838910.1038/nn.4399PMC5083142 | — | — | — |
| GamazonER A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet 47, 1091–8 (2015).2625884810.1038/ng.3367PMC4552594 | — | — | — |
| GeschwindDH & FlintJ Genetics and genomics of psychiatric disease. Science (80-. ). 349, (2015).10.1126/science.aaa8954PMC469456326404826 | — | — | — |
| GiambartolomeiC Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).2483039410.1371/journal.pgen.1004383PMC4022491 | — | — | — |
| GiambartolomeiC Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. PLoS Genet. 10, e1004383 (2014).2483039410.1371/journal.pgen.1004383PMC4022491 | — | — | — |
| GiladY, RifkinSA & PritchardJK Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 24, 408–415 (2008).1859788510.1016/j.tig.2008.06.001PMC2583071 | — | — | — |
| GlassD Gene expression changes with age in skin, adipose tissue, blood and brain. Genome Biol. 14, R75 (2013).2388984310.1186/gb-2013-14-7-r75PMC4054017 | — | — | — |
| GlessnerJT Strong synaptic transmission impact by copy number variations in schizophrenia. Proc. Natl. Acad. Sci. U. S. A 107, 10584–9 (2010).2048917910.1073/pnas.1000274107PMC2890845 | — | — | — |
| GoldJM Cognitive deficits as treatment targets in schizophrenia. Schizophr. Res 72, 21–28 (2004).1553140410.1016/j.schres.2004.09.008 | — | — | — |
| GottliebA, DaneshjouR, DeGorterM, MontgomeryS & AltmanR Population-specific imputation of gene expression improves prediction of pharmacogenomic traits for African Americans. bioRxiv 115451 (2017). doi:10.1101/115451PMC570215829178968 | — | — | — |
| GururajanA & van denBuuse, M.. Is the mTOR-signalling cascade disrupted in Schizophrenia? J. Neurochem 129, 377–387 (2014).2426636610.1111/jnc.12622 | — | — | — |
| GusevA Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet 48, 245–52 (2016).2685491710.1038/ng.3506PMC4767558 | — | — | — |
| GusevA Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. bioRxiv 67355 (2016). doi:10.1101/067355PMC594289329632383 | — | — | — |
| HoweDG ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics. Nucleic Acids Res. 41, D854–D860 (2013).2307418710.1093/nar/gks938PMC3531097 | — | — | — |
| HughesJR A reappraisal of the possible seizures of Vincent van Gogh. Epilepsy Behav. 6, 504–10 (2005).1590774510.1016/j.yebeh.2005.02.014 | — | — | — |
| KabackMM & DesnickRJ Hexosaminidase A Deficiency GeneReviews(®) (University of Washington, Seattle, 1993). | — | — | — |
| KanehisaM & GotoS KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).1059217310.1093/nar/28.1.27PMC102409 | — | — | — |
| KeaneTM Mouse genomic variation and its effect on phenotypes and gene regulation. Nature 477, 289–294 (2011).2192191010.1038/nature10413PMC3276836 | — | — | — |
| KeefeRSE & FentonWS How Should DSM-V Criteria for Schizophrenia Include Cognitive Impairment? Schizophr. Bull 33, 912–920 (2007).1756762710.1093/schbul/sbm046PMC2632322 | — | — | — |
| KirovG De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–53 (2012).2208372810.1038/mp.2011.154PMC3603134 | — | — | — |
| KumarB Acute intermittent porphyria presenting solely with psychosis: a case report and discussion. Psychosomatics 53, 494–8 (2012).2290208810.1016/j.psym.2012.03.008 | — | — | — |
| LeekJT, JohnsonWE, ParkerHS, JaffeAE & StoreyJD The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–3 (2012).2225766910.1093/bioinformatics/bts034PMC3307112 | — | — | — |
| LibioulleC Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4. PLoS Genet. 3, e58 (2007).1744784210.1371/journal.pgen.0030058PMC1853118 | — | — | — |
| LinGN Spatiotemporal 16p11.2 Protein Network Implicates Cortical Late Mid-Fetal Brain Development and KCTD13- Cul3-RhoA Pathway in Psychiatric Diseases. Neuron 85, 742–754 (2015).2569526910.1016/j.neuron.2015.01.010PMC4335356 | — | — | — |
| LoftusLS & ArnoldWN Vincent van Gogh’s illness: acute intermittent porphyria? BMJ 303, 1589–9110.1136/bmj.303.6817.1589PMC16762501773180 | — | — | — |
| MalhotraD High frequencies of de novo CNVs in bipolar disorder and schizophrenia. Neuron 72, 951–963 (2011).2219633110.1016/j.neuron.2011.11.007PMC3921625 | — | — | — |
| MancusoN Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits. Am. J. Hum. Genet 100, 473–487 (2017).2823835810.1016/j.ajhg.2017.01.031PMC5339290 | — | — | — |
| MeleM The human transcriptome across tissues and individuals. Science (80-. ). 348, 660–665 (2015).10.1126/science.aaa0355PMC454747225954002 | — | — | — |
| MiH, MuruganujanA & ThomasPD PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41, D377–86 (2013).2319328910.1093/nar/gks1118PMC3531194 | — | — | — |
| MillerJA Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).2469522910.1038/nature13185PMC4105188 | — | — | — |
| MoffattMF Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448, 470–473 (2007).1761149610.1038/nature06014 | — | — | — |
| NeedA & GoldsteinDB Next generation disparities in human genomics: concerns and remedies. Trends Genet 25, 489–94 (2009).1983685310.1016/j.tig.2009.09.012 | — | — | — |
| Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).2559922310.1038/nn.3922PMC4378867 | — | — | — |
| NguyenHT Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders. doi.org 135293 (2017). doi:10.1101/135293PMC573815329262854 | — | — | — |
| NicolaeDL Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS. PLoS Genet. 6, e1000888 (2010).2036901910.1371/journal.pgen.1000888PMC2848547 | — | — | — |
| PardiñasAF Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet (2018). doi:10.1038/s41588-018-0059-2PMC591869229483656 | — | — | — |
| PardiñasAF Common schizophrenia alleles are enriched in mutation-intolerant genes and maintained by background selection. bioRxiv 68593 (2016). doi:10.1101/068593 | — | — | — |
| ParikshakNN, GandalMJ, GeschwindDH & AngelesL Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat. Rev. Genet 16, 441–458 (2015).2614971310.1038/nrg3934PMC4699316 | — | — | — |
| PetersTJ & BeveridgeA The madness of King George III: a psychiatric re-assessment. Hist. Psychiatry 21, 20–37 (2010).2187742810.1177/0957154X09343825 | — | — | — |
| PickrellJK Detection and interpretation of shared genetic influences on 42 human traits. Nat. Genet 48, 709–717 (2016).2718296510.1038/ng.3570PMC5207801 | — | — | — |
| PischikE & KauppinenR An update of clinical management of acute intermittent porphyria. Appl. Clin. Genet 8, 201–14 (2015).2636610310.2147/TACG.S48605PMC4562648 | — | — | — |
| PopejoyA & FullertonS Genomics is failing on diversity. Nature 538, 161–164 (2016).2773487710.1038/538161aPMC5089703 | — | — | — |
| ReichenbergA Static and Dynamic Cognitive Deficits in Childhood Preceding Adult Schizophrenia: A 30-Year Study. Am. J. Psychiatry 167, 160–169 (2010).2004802110.1176/appi.ajp.2009.09040574PMC3552325 | — | — | — |
| RipkeS Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).2505606110.1038/nature13595PMC4112379 | — | — | — |
| RitsnerMS Brain Protection in Schizophrenia, Mood and Cognitive Disorders. (Springer Science & Business Media, 2010, 2010). doi:10.1007/978-90-481-8553-5 | — | — | — |
| SalehO Late Onset Tay-Sachs Disease Pre senting as a Brief Psychotic Disorder with Catatonia: A Case Report and Review of Literature. | — | — | — |
| SandersSJ First glimpses of the neurobiology of autism spectrum disorder. Curr. Opin. Genet. Dev 33, 80–92 (2015).2654713010.1016/j.gde.2015.10.002 | — | — | — |
| SkaperSD in Brain Protection in Schizophrenia, Mood and Cognitive Disorders (ed. RitsnerMS) 135–165 (Springer Science & Business Media, 2010, 2010). | — | — | — |
| SmithCL, BlakeJA, KadinJA, RichardsonJE & BultCJ Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse. Nucleic Acids Res. 46, D836–D842 (2018).2909207210.1093/nar/gkx1006PMC5753350 | — | — | — |
| SpeliotesEK Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet 42, 937–948 (2010).2093563010.1038/ng.686PMC3014648 | — | — | — |
| StrikWK [The psychiatric illness of Vincent van Gogh]. Nervenarzt 68, 401–9 (1997).928085010.1007/s001150050142 | — | — | — |
| SzatkiewiczJP Copy number variation in schizophrenia in Sweden. Mol. Psychiatry 19, 762–773 (2014).2477674010.1038/mp.2014.40PMC4271733 | — | — | — |
| The Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–1056 (2014).2542836910.1093/nar/gku1179PMC4383973 | — | — | — |
| ThomasPD PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 13, 2129–2141 (2003).1295288110.1101/gr.772403PMC403709 | — | — | — |
| van der WeydenL, WhiteJK, AdamsDJ & LoganDW The mouse genetics toolkit: revealing function and mechanism. Genome Biol. 12, 224 (2011).2172235310.1186/gb-2011-12-6-224PMC3218837 | — | — | — |
| VenturaP A challenging diagnosis for potential fatal diseases: recommendations for diagnosing acute porphyrias. Eur. J. Intern. Med 25, 497–505 (2014).2480992710.1016/j.ejim.2014.03.011 | — | — | — |
| WattsDJ & StrogatzSH Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).962399810.1038/30918 | — | — | — |
| WillerCJ, LiY & AbecasisGR METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).2061638210.1093/bioinformatics/btq340PMC2922887 | — | — | — |
| YangJ, LeeSH, GoddardME & VisscherPM GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet 88, 76–82 (2011).2116746810.1016/j.ajhg.2010.11.011PMC3014363 | — | — | — |
| ZhuZ Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet 48, 481–487 (2016).2701911010.1038/ng.3538 | — | — | — |
| ZimmermannM, BonaccursoC, ValeriusC & HamannGF [Acute intermittent porphyria. A clinical chameleon: case study of a 40-year-old female patient]. Nervenarzt 77, 1501–5 (2006).1713641210.1007/s00115-006-2224-6 | — | — | — |
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| 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 | → |
| Mapping dynamic regulation of gene expression using single-cell transcriptomics and application to complex disease genetics. | Abe H et al. | — | 2025 | → |
| Mendelian Randomization-Based Discovery of Novel Protein Biomarkers and Drug Targets in Colorectal Cancer: Validation Through Prognostic Modeling, Single-Cell Analysis, and In Vitro Cell Experiments. | Bao X et al. | — | 2025 | → |
| Mendelian Randomization Reveals Causalities Between DNA Methylation and Schizophrenia. | Wang D et al. | — | 2025 | → |
| Multi-ancestral genome-wide association study of clinically defined nicotine dependence reveals strong genetic correlations with other substance use disorders and health-related traits. | Johnson EC et al. | — | 2025 | → |
| Protein arginine methyltransferase 7 linked to schizophrenia through regulation of neural progenitor cell proliferation and differentiation. | Shen T et al. | — | 2025 | → |
| The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol. | Berthold N et al. | — | 2025 | → |
| Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis. | Zhang R et al. | — | 2024 | → |
| Accurate identification of genes associated with brain disorders by integrating heterogeneous genomic data into a Bayesian framework. | He D et al. | — | 2024 | → |
| A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing. | Pergola G et al. | — | 2024 | → |
| CLOZAPINE-RELATED BRAIN<i>NRN1</i>EXPRESSION PATTERNS ARE ASSOCIATED WITH METHYLATION AND GENETIC VARIANTS IN SCHIZOPHRENIA | Almodóvar-Payá C et al. | — | 2024 | — |
| Co-expression of prepulse inhibition and Schizophrenia genes in the mouse and human brain. | Garrett L et al. | — | 2024 | → |
| Decreased CNNM2 expression in prefrontal cortex affects sensorimotor gating function, cognition, dendritic spine morphogenesis and risk of schizophrenia. | Zhou DY et al. | — | 2024 | → |
| Gene expression imputation provides clinical and biological insights into treatment-resistant schizophrenia polygenic risk. | Prohens L et al. | — | 2024 | → |
| Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. | Xu X et al. | — | 2024 | → |
| Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders. | Luo J et al. | — | 2024 | → |
| Genomic insights into the comorbidity between type 2 diabetes and schizophrenia. | Arruda AL et al. | — | 2024 | → |
| Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine. | Nam Y et al. | — | 2024 | → |
| Harnessing transcriptomic signals for amyotrophic lateral sclerosis to identify novel drugs and enhance risk prediction. | Pain O et al. | — | 2024 | → |
| Network-wide risk convergence in gene co-expression identifies reproducible genetic hubs of schizophrenia risk. | Borcuk C et al. | — | 2024 | → |
| Prioritizing susceptibility genes for the prognosis of male-pattern baldness with transcriptome-wide association study. | Choi E et al. | — | 2024 | → |
| The schizophrenia syndrome, circa 2024: What we know and how that informs its nature. | Tandon R et al. | — | 2024 | → |
| Transcriptional risk scores in Alzheimer's disease: From pathology to cognition. | Pyun JM et al. | — | 2024 | → |
| Cerebellar Functional Dysconnectivity in Drug-Naïve Patients With First-Episode Schizophrenia. | Cao H et al. | — | 2023 | → |
| Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions. | Pergola G et al. | — | 2023 | → |
| Dysfunction of cAMP-Protein Kinase A-Calcium Signaling Axis in Striatal Medium Spiny Neurons: A Role in Schizophrenia and Huntington's Disease Neuropathology. | Fjodorova M et al. | — | 2023 | → |
| Human forebrain organoid-based multi-omics analyses of PCCB as a schizophrenia associated gene linked to GABAergic pathways. | Zhang W et al. | — | 2023 | → |
| Identification and functional analysis of circulating extrachromosomal circular DNA in schizophrenia implicate its negative effect on the disorder. | Xiang X et al. | — | 2023 | → |
| Induction of dopaminergic neurons for neuronal subtype-specific modeling of psychiatric disease risk. | Powell SK et al. | — | 2023 | → |
| Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. | Hicks EM et al. | — | 2023 | → |
| Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways. | Pergola G et al. | — | 2023 | → |
| Mapping anorexia nervosa genes to clinical phenotypes. | Johnson JS et al. | — | 2023 | → |
| Mendelian Randomization Study Using Dopaminergic Neuron-Specific eQTL Identifies Novel Risk Genes for Schizophrenia. | Dang X et al. | — | 2023 | → |
| Prioritization of potential causative genes for schizophrenia in placenta. | Ursini G et al. | — | 2023 | → |
| Rediscovering tandem repeat variation in schizophrenia: challenges and opportunities. | Birnbaum R | — | 2023 | → |
| Spatiotemporal expression patterns of anxiety disorder-associated genes. | Karunakaran KB et al. | — | 2023 | → |
| Stem Cell Models for Context-Specific Modeling in Psychiatric Disorders. | Seah C et al. | — | 2023 | → |
| The genetic architecture of schizophrenia: review of large-scale genetic studies. | Kato H et al. | — | 2023 | → |
| A conditional gene-based association framework integrating isoform-level eQTL data reveals new susceptibility genes for schizophrenia. | Li X et al. | — | 2022 | → |
| An epigenetic association analysis of childhood trauma in psychosis reveals possible overlap with methylation changes associated with PTSD. | Løkhammer S et al. | — | 2022 | → |
| Bioinformatics and network-based approaches for determining pathways, signature molecules, and drug substances connected to genetic basis of schizophrenia etiology. | Khan U et al. | — | 2022 | → |
| Bioinformatics detection of modulators controlling splicing factor-dependent intron retention in the human brain. | Chen SX et al. | — | 2022 | → |
| Gene co-expression architecture in peripheral blood in a cohort of remitted first-episode schizophrenia patients. | Rodríguez N et al. | — | 2022 | → |
| Genetic control of RNA splicing and its distinct role in complex trait variation. | Qi T et al. | — | 2022 | → |
| Identification of genetic loci that overlap between schizophrenia and metabolic syndrome. | Lv H et al. | — | 2022 | → |
| Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population. | Ding K et al. | — | 2022 | → |
| Integrating human brain proteomes with genome-wide association data implicates novel proteins in post-traumatic stress disorder. | Wingo TS et al. | — | 2022 | → |
| Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. | Guan F et al. | — | 2022 | → |
| In vivo study sheds new light on the dendritic spine pathology hypothesis of schizophrenia. | Li W et al. | — | 2022 | → |
| Joint-Tissue Integrative Analysis Identified Hundreds of Schizophrenia Risk Genes. | Wu Y et al. | — | 2022 | → |
| Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization. | Jiang L et al. | — | 2022 | → |
| Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. | Highland HM et al. | — | 2022 | → |
| Shared mechanisms across the major psychiatric and neurodegenerative diseases. | Wingo TS et al. | — | 2022 | → |
| The shared genetic basis of mood instability and psychiatric disorders: A cross-trait genome-wide association analysis. | Hindley G et al. | — | 2022 | → |
| Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes. | Li L et al. | — | 2022 | → |
| Transcriptome-wide association study reveals increased neuronal FLT3 expression is associated with Tourette's syndrome. | Liao C et al. | — | 2022 | → |
| Upper cortical layer-driven network impairment in schizophrenia. | Batiuk MY et al. | — | 2022 | → |
| What genes are differentially expressed in individuals with schizophrenia? A systematic review. | Merikangas AK et al. | — | 2022 | → |
| A longitudinal study of gene expression in first-episode schizophrenia; exploring relapse mechanisms by co-expression analysis in peripheral blood. | Gassó P et al. | — | 2021 | → |
| Applying stem cells and CRISPR engineering to uncover the etiology of schizophrenia. | Michael Deans PJ et al. | — | 2021 | → |
| A robust two-sample transcriptome-wide Mendelian randomization method integrating GWAS with multi-tissue eQTL summary statistics. | Gleason KJ et al. | — | 2021 | → |
| Brain-immune interactions in neuropsychiatric disorders: Lessons from transcriptome studies for molecular targeting. | Afridi R et al. | — | 2021 | → |
| Brain proteome-wide association study implicates novel proteins in depression pathogenesis. | Wingo TS et al. | — | 2021 | → |
| D-cysteine is an endogenous regulator of neural progenitor cell dynamics in the mammalian brain. | Semenza ER et al. | — | 2021 | → |
| DECO: a framework for jointly analyzing de novo and rare case/control variants, and biological pathways. | Nguyen TH et al. | — | 2021 | → |
| Delineating the Genetic Component of Gene Expression in Major Depression. | Dall'Aglio L et al. | — | 2021 | → |
| Dream: powerful differential expression analysis for repeated measures designs. | Hoffman GE et al. | — | 2021 | → |
| Engineering <i>in vitro</i> human neural tissue analogs by 3D bioprinting and electrostimulation. | Warren D et al. | — | 2021 | → |
| Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study. | Tavares V et al. | — | 2021 | → |
| Evidence of an interaction between <i>FXR1</i> and <i>GSK3β</i> polymorphisms on levels of Negative Symptoms of Schizophrenia and their response to antipsychotics. | Rampino A et al. | — | 2021 | → |
| Gene Expression Analysis in Three Posttraumatic Stress Disorder Cohorts Implicates Inflammation and Innate Immunity Pathways and Uncovers Shared Genetic Risk With Major Depressive Disorder. | Garrett ME et al. | — | 2021 | → |
| Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders. | Pei G et al. | — | 2021 | → |
| Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci. | Chen HH et al. | — | 2021 | → |
| Genome-Wide Association Studies of Schizophrenia and Bipolar Disorder in a Diverse Cohort of US Veterans. | Bigdeli TB et al. | — | 2021 | → |
| Genome-wide association study followed by trans-ancestry meta-analysis identify 17 new risk loci for schizophrenia. | Liu J et al. | — | 2021 | → |
| Glyoxalase 1 Confers Susceptibility to Schizophrenia: From Genetic Variants to Phenotypes of Neural Function. | Yin J et al. | — | 2021 | → |
| Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning. | Chang M et al. | — | 2021 | → |
| Insilico Functional Analysis of Genome-Wide Dataset From 17,000 Individuals Identifies Candidate Malaria Resistance Genes Enriched in Malaria Pathogenic Pathways. | Damena D et al. | — | 2021 | → |
| Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer's disease pathogenesis. | Wingo AP et al. | — | 2021 | → |
| Integrative Analyses Followed by Functional Characterization Reveal TMEM180 as a Schizophrenia Risk Gene. | Wang JY et al. | — | 2021 | → |
| kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes. | Cao C et al. | — | 2021 | → |
| Low-Level Brain Somatic Mutations Are Implicated in Schizophrenia. | Kim MH et al. | — | 2021 | → |
| Multi-omics integration analysis identifies novel genes for alcoholism with potential overlap with neurodegenerative diseases. | Kapoor M et al. | — | 2021 | → |
| Neurodevelopmental Disorders in Patients With Complex Phenotypes and Potential Complex Genetic Basis Involving Non-Coding Genes, and Double CNVs. | Servetti M et al. | — | 2021 | → |
| Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia. | Dobrindt K et al. | — | 2021 | → |
| Shaping the Trans-Scale Properties of Schizophrenia <i>via</i> Cerebral Alterations on Magnetic Resonance Imaging and Single-Nucleotide Polymorphisms of Coding and Non-Coding Regions. | Zhao SW et al. | — | 2021 | → |
| SNX29, a new susceptibility gene shared with major mental disorders in Han Chinese population. | Chen JH et al. | — | 2021 | → |
| Transcriptome-wide association study reveals two genes that influence mismatch negativity. | Bhat A et al. | — | 2021 | → |
| Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders. | Hernandez LM et al. | — | 2021 | → |
| Analysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts. | Huckins LM et al. | — | 2020 | → |
| Analysis of GWAS-Derived Schizophrenia Genes for Links to Ischemia-Hypoxia Response of the Brain. | Schmidt-Kastner R et al. | — | 2020 | → |
| An update on the role of common genetic variation underlying substance use disorders. | Johnson EC et al. | — | 2020 | → |
| A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits. | Dahl A et al. | — | 2020 | → |
| A role for TGFβ signalling in medium spiny neuron differentiation of human pluripotent stem cells. | Fjodorova M et al. | — | 2020 | → |
| A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. | Hall LS et al. | — | 2020 | → |
| Blood-based multi-tissue gene expression inference with Bayesian ridge regression. | Xu W et al. | — | 2020 | → |
| Common genetic risk variants identified in the SPARK cohort support DDHD2 as a candidate risk gene for autism. | Matoba N et al. | — | 2020 | → |
| Complexities of Understanding Function from CKD-Associated DNA Variants. | Lin J et al. | — | 2020 | → |
| Evaluating the cardiovascular safety of sclerostin inhibition using evidence from meta-analysis of clinical trials and human genetics. | Bovijn J et al. | — | 2020 | → |
| Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification. | Barbeira AN et al. | — | 2020 | → |
| Gene expression profiles complement the analysis of genomic modifiers of the clinical onset of Huntington disease. | Wright GEB et al. | — | 2020 | → |
| Genetic Influences on Disease Subtypes. | Dahl A et al. | — | 2020 | → |
| Identification of ALK in Thinness. | Orthofer M et al. | — | 2020 | → |
| Identification of relevant hub genes for early intervention at gene coexpression modules with altered predicted expression in schizophrenia. | Rodriguez-López J et al. | — | 2020 | → |
| Integrative analyses prioritize GNL3 as a risk gene for bipolar disorder. | Meng Q et al. | — | 2020 | → |
| Investigation of Schizophrenia with Human Induced Pluripotent Stem Cells. | Powell SK et al. | — | 2020 | → |
| Massively parallel techniques for cataloguing the regulome of the human brain. | Townsley KG et al. | — | 2020 | → |
| Meta-Analysis of Transcriptomic Data of Dorsolateral Prefrontal Cortex and of Peripheral Blood Mononuclear Cells Identifies Altered Pathways in Schizophrenia. | Petralia MC et al. | — | 2020 | → |
| Mining GWAS and eQTL data for CF lung disease modifiers by gene expression imputation. | Dang H et al. | — | 2020 | → |
| Modeling the complex genetic architectures of brain disease. | Fernando MB et al. | — | 2020 | → |
| Molecular mechanisms of psychiatric diseases. | Blokhin IO et al. | — | 2020 | → |
| mTADA is a framework for identifying risk genes from de novo mutations in multiple traits. | Nguyen TH et al. | — | 2020 | → |
| Peripheral Blood Leukocyte RNA-Seq Identifies a Set of Genes Related to Abnormal Psychomotor Behavior Characteristics in Patients with Schizophrenia. | Zhang Y et al. | — | 2020 | → |
| Prefrontal cortex eQTLs/mQTLs enriched in genetic variants associated with alcohol use disorder and other diseases. | Lin H et al. | — | 2020 | → |
| Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk. | Jaffe AE et al. | — | 2020 | → |
| Quantifying genetic effects on disease mediated by assayed gene expression levels. | Yao DW et al. | — | 2020 | → |
| Recent advances in genetic studies of alcohol use disorders. | Gupta I et al. | — | 2020 | → |
| SZDB2.0: an updated comprehensive resource for schizophrenia research. | Wu Y et al. | — | 2020 | → |
| The genome-wide risk alleles for psychiatric disorders at 3p21.1 show convergent effects on mRNA expression, cognitive function, and mushroom dendritic spine. | Yang Z et al. | — | 2020 | → |
| The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. | Smeland OB et al. | — | 2020 | → |
| Translating insights from neuropsychiatric genetics and genomics for precision psychiatry. | Rees E et al. | — | 2020 | → |
| Genome-wide association study implicates CHRNA2 in cannabis use disorder. | Demontis D et al. | — | 2019 | → |
| Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits. | Gamazon ER et al. | — | 2019 | → |
| Synergistic effects of common schizophrenia risk variants. | Schrode N et al. | — | 2019 | → |
| Transcriptome association studies of neuropsychiatric traits in African Americans implicate <i>PRMT7</i> in schizophrenia. | Fiorica PN et al. | — | 2019 | → |
| Translational research identifies a metabolism pathway involved in first-episode of schizophrenia: Towards precision medicine. | Simonneau M | — | 2019 | → |
| Use of the epigenetic toolbox to contextualize common variants associated with schizophrenia risk . | Rajarajan P et al. | — | 2019 | → |