The mechanisms by which common risk variants of small effect interact to contribute to complex genetic disorders are unclear. Here, we apply a genetic approach, using isogenic human induced pluripotent stem cells, to evaluate the effects of schizophrenia (SZ)-associated common variants predicted to function as SZ expression quantitative trait loci (eQTLs). By integrating CRISPR-mediated gene editing, activation and repression technologies to study one putative SZ eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (FURIN, SNAP91, TSNARE1 and CLCN3), our platform resolves pre- and postsynaptic neuronal deficits, recapitulates genotype-dependent gene expression differences and identifies convergence downstream of SZ eQTL gene perturbations. Our observations highlight the cell-type-specific effects of common variants and demonstrate a synergistic effect between SZ eQTL genes that converges on synaptic function. We propose that the links between rare and common variants implicated in psychiatric disease risk constitute a potentially generalizable phenomenon occurring more widely in complex genetic disorders.
Your institute does not have access to this article
Open Access articles citing this article.
Scientific Reports Open Access 13 July 2022
Targeting NMDA receptors in neuropsychiatric disorders by drug screening on human neurons derived from pluripotent stem cells
Translational Psychiatry Open Access 09 June 2022
Gene co-expression architecture in peripheral blood in a cohort of remitted first-episode schizophrenia patients
Schizophrenia Open Access 27 April 2022
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
All source donor hiPSCs have already been deposited at the Rutgers University Cell and DNA Repository (study 160; http://www.nimhstemcells.org/); CRISPR-edited hiPSCs are in the process of being submitted in advance of publication. The RNA-seq data are available at www.synapse.org/#!Synapse:syn20502314. Additionally, we make available the following resource by which neural cell-type-specific expression of any gene can be easily cross-referenced in our hiPSC datasets as well as case–control postmortem, hiPSC NPC and hiPSC-neuron RNA-seq datasets (https://schroden.shinyapps.io/BrennandLab-ExpressionApp-limited/). Owing to constraints reflecting the original consents, which are restricted to the study of neuropsychiatric disease only, the raw RNA-seq data will be made available by the authors upon reasonable request and institutional review board approval.
Code is available at https://github.com/nadschro/SZvariant-synergy.
Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).
Jaffe, A. E. et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125 (2018).
Hall, L. S. et al. A transcriptome wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia. Preprint at bioRxiv https://doi.org/10.1101/384560 (2018).
Huang, H. et al. Fine-mapping inflammatory bowel disease loci to single-variant resolution. Nature 547, 173–178 (2017).
Huckins, L. M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51, 659–674 (2019).
Dobbyn, A. et al. Landscape of conditional eQTL in dorsolateral prefrontal cortex and co-localization with schizophrenia GWAS. Am. J. Hum. Genet. 102, 1169–1184 (2018).
Hoffman, G. E. et al. Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains. Nat. Commun. 8, 2225 (2017).
Schwartzentruber, J. et al. Molecular and functional variation in iPSC-derived sensory neurons. Nat. Genet. 50, 54–61 (2018).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Forrest, M. P. et al. Open chromatin profiling in hiPSC-derived neurons prioritizes functional noncoding psychiatric risk variants and highlights neurodevelopmental loci. Cell Stem Cell 21, 305–318.e8 (2017).
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
Aguet, F. et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).
Zhang, Y. et al. Rapid single-step induction of functional neurons from human pluripotent stem cells. Neuron 78, 785–798 (2013).
Ho, S. M. et al. Rapid Ngn2-induction of excitatory neurons from hiPSC-derived neural progenitor cells. Methods 101, 113–124 (2016).
Ho, S. M. et al. Evaluating synthetic activation and repression of neuropsychiatric-related genes in hiPSC-derived NPCs, neurons, and astrocytes. Stem Cell Rep. 9, 615–628 (2017).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Paquet, D. et al. Efficient introduction of specific homozygous and heterozygous mutations using CRISPR/Cas9. Nature 533, 125–129 (2016).
Yang, N. et al. Generation of pure GABAergic neurons by transcription factor programming. Nat. Methods 14, 621–628 (2017).
Canals, I. et al. Rapid and efficient induction of functional astrocytes from human pluripotent stem cells. Nat. Methods 15, 693–696 (2018).
Bowles, K. R., Tcw, J., Qian, L., Jadow, B. M. & Goate, A. M. Reduced variability of neural progenitor cells and improved purity of neuronal cultures using magnetic activated cell sorting. PLoS ONE 14, e0213374 (2019).
Paşca, A. M. et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture. Nat. Methods 12, 671–678 (2015).
Birey, F. et al. Assembly of functionally integrated human forebrain spheroids. Nature 545, 54–59 (2017).
Hou, Y. et al. Schizophrenia-associated rs4702 G allele-specific downregulation of FURIN expression by miR-338-3p reduces BDNF production. Schizophr. Res. 199, 176–180 (2018).
Mowla, S. J. et al. Biosynthesis and post-translational processing of the precursor to brain-derived neurotrophic factor. J. Biol. Chem. 276, 12660–12666 (2001).
Campenot, R. B. Local control of neurite development by nerve growth factor. Proc. Natl Acad. Sci. USA 74, 4516–4519 (1977).
McGuire, J. L. et al. Altered serine/threonine kinase activity in schizophrenia. Brain Res. 1568, 42–54 (2014).
Petralia, R. S. et al. Reduction of AP180 and CALM produces defects in synaptic vesicle size and density. Neuromolecular Med. 15, 49–60 (2013).
Smith, J. J., Sumiyama, K. & Amemiya, C. T. A living fossil in the genome of a living fossil: Harbinger transposons in the coelacanth genome. Mol. Biol. Evol. 29, 985–993 (2012).
Chun, S. et al. Thalamic miR-338-3p mediates auditory thalamocortical disruption and its late onset in models of 22q11.2 microdeletion. Nat. Med. 23, 39–48 (2017).
Fromer, M. et al. De novo mutations in schizophrenia implicate synaptic networks. Nature 506, 179–184 (2014).
Kirov, G. et al. De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia. Mol. Psychiatry 17, 142–153 (2012).
Föcking, M. et al. Proteomic and genomic evidence implicates the postsynaptic density in schizophrenia. Mol. Psychiatry 20, 424–432 (2015).
Ripke, S., Schizophrenia Working Group & O’Donovan, M. Current status of schizophrenia GWAS. Eur. Neuropsychopharmacol. 27, S415 (2017).
Takata, A., Matsumoto, N. & Kato, T. Genome-wide identification of splicing QTLs in the human brain and their enrichment among schizophrenia-associated loci. Nat. Commun. 8, 14519 (2017).
Bardy, C. et al. Neuronal medium that supports basic synaptic functions and activity of human neurons in vitro. Proc. Natl Acad. Sci. USA 112, E2725–E2734 (2015).
Kwon, H. B. et al. Neuroligin-1-dependent competition regulates cortical synaptogenesis and synapse number. Nat. Neurosci. 15, 1667–1674 (2012).
Dityatev, A., Dityateva, G. & Schachner, M. Synaptic strength as a function of post- versus presynaptic expression of the neural cell adhesion molecule NCAM. Neuron 26, 207–217 (2000).
Burrone, J., O’Byrne, M. & Murthy, V. N. Multiple forms of synaptic plasticity triggered by selective suppression of activity in individual neurons. Nature 420, 414–418 (2002).
Suk, H. J. et al. Closed-loop real-time imaging enables fully automated cell-targeted patch-clamp neural recording in vivo. Neuron 96, 244–245 (2017).
Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).
Port, F. & Bullock, S. L. Augmenting CRISPR applications in Drosophila with tRNA-flanked sgRNAs. Nat. Methods 13, 852–854 (2016).
Hoffman, G. E., Schrode, N., Flaherty, E. & Brennand, K. J. New considerations for hiPSC-based models of neuropsychiatric disorders. Mol. Psychiatry 24, 49–66 (2019).
Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012).
Purcell, S. M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).
Sanders, S. J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).
O'Dushlaine, C. et al. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways. Nat. Neurosci. 18, 199–209 (2015).
Ballouz, S. & Gillis, J. Strength of functional signature correlates with effect size in autism. Genome Med. 9, 64 (2017).
Jia, P., Chen, X., Fanous, A. H. & Zhao, Z. Convergent roles of de novo mutations and common variants in schizophrenia in tissue-specific and spatiotemporal co-expression network. Transl. Psychiatry 8, 105 (2018).
Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
Wray, N. R., Wijmenga, C., Sullivan, P. F., Yang, J. & Visscher, P. M. Common disease is more complex than implied by the core gene omnigenic model. Cell 173, 1573–1580 (2018).
Wainschtein, P. et al. Recovery of trait heritability from whole genome sequence data. Preprint at bioRxiv https://doi.org/10.1101/588020 (2019).
Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012).
Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034.e6 (2019).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376.e17 (2019).
Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019).
Weiner, D. J. et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nat. Genet. 49, 978–985 (2017).
Tansey, K. E. et al. Common alleles contribute to schizophrenia in CNV carriers. Mol. Psychiatry 21, 1153 (2016).
McMahon, F. J. & Insel, T. R. Pharmacogenomics and personalized medicine in neuropsychiatry. Neuron 74, 773–776 (2012).
He, X. et al. Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am. J. Hum. Genet. 92, 667–680 (2013).
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
Brennand, K. J. et al. Modelling schizophrenia using human induced pluripotent stem cells. Nature 473, 221–225 (2011).
Szklarczyk, D. et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).
Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Wu, D. & Smyth, G. K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).
This manuscript is dedicated to Pamela Sklar, a scientist, mentor and friend who advanced the field of psychiatric genetics. This work was partially supported by NIH grants (nos. R56 MH101454 to K.J.B., E.S. and H.M., R01 MH106056 to K.J.B., U19 MH104172 and U19 MH107367 to B.A., and R01 MH109897 to P.S. and K.J.B.), the New York Stem Cell Foundation (to K.J.B.) and project ALS (grant no. 2017-03 to J.G. and H.P.). We thank the Neuroscience and Stem Cell Cores at ISMMS. This work was supported in part through the computational resources and staff expertise provided by the Scientific Computing unit at ISMMS. P. O’Reilly provided thoughtful feedback on the manuscript. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH and by National Cancer Institute, National Human Genome Research Institute, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Institute of Mental Health and National Institute of Neurological Disorders and Stroke. The data used for the analyses described in this manuscript were obtained from the GTEx portal on 4 January 2019.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
A-C. Endogenous expression profiling of five SZ-eQTL genes (SNAP91, TSNARE1, CLCN3, FURIN and CNTN4) in (A) brain tissue from the Genotype-Tissue Expression (GTEx) project, (B) single-cell RNA-Seq of post-mortem DLPFC from Psychencode and (C) hiPSC-derived neural cells (NPCs, forebrain (FB)-neurons, NGN2-excitatory neurons) and primary astrocytes. FPKM (Fragments Per Kilobase of transcript per Million mapped reads) of each gene were quantified from RNA-Seq. Three biological replicates represent data from three individuals. All boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range). D-F. Relative fold changes of mRNA levels for SNAP91, TSNARE1, CLCN3 and FURIN altered by CRISPRa (purple) or CRISPRi (orange) in (D) NPCs, n=52 independent samples per gene, (E) D8 NGN2-excitatory neurons, n=36 independent samples per gene, and (F) D21 NGN2-excitatory neurons, n=36 independent samples per gene. Numbers under data points indicate their fold changes. Fold changes were calculated by normalization of targeted CRISPRa/i altered gene mRNA level to that of CRISPRa/i scramble controls. C1 (circle), C2 (rectangle) and C3 (triangle) indicate hiPSC-NPC lines from three independent male controls (see Supplementary Table 1 for more information). Data are presented as means ± SEM. P-values were calculated using two-tailed unpaired T-test. G. Neuronal (blue) and non-neuronal (green) active chromatin mark H3K27ac ChIP-seq peaks over five SZ-eQTL genes (FURIN, SNAP91, TSNARE1, CLCN3 and CNTN4) (modified from UCSC Genome browser tracks68).
A. Results of FURIN rs4702 G allele enrichment through two rounds of Taqman ddPCR screening. B. Sanger sequencing confirming seamless allelic conversion of AA to GG at FURIN rs4702. ddPCR experiments were performed three times with similar results. C. Off-target validation of FURIN rs4702 G allele knock-in. The top three predicted off-target regions of SNP editing gRNA-rs4702 were amplified and Sanger sequenced. Traces for all clones show no indels or mismatches in a 700-1000 bp region around the off-target region in two separate experiments. D. Log2 (relative expression) of four genes predicted to be rs4702 eQTL genes, in rs4702 AA and GG D7 NGN2-excitatory neurons (n=2 cell culture samples ea). P-values were calculated using two-sided t-tests. E. Average radial migration distance of rs4702 AA and GG neurospheres, derived from NPCs, after 48h in culture (n=28, left) and representative images (right). Scale bar = 200 μm. P-values were calculated using two-sided t-tests. F. Representative confocal images of day 43 rs4702 AA (left panel) and GG (right panel) human cortical spheroids (hCS, top) and human subpallium spheroids (hSS, bottom). hCS were immunostained against cortical markers CTIP2 (green) and SATB2 (red) along with the neuronal marker MAP2 (cyan). hSS were immunostained against inhibitory markers GABA (red) and GAD67 (green), along with MAP2. White squares on 10x images represent close-up area shown in 40x images. Scale bar = 150 μm in 10x images and 50 μm in 40x images. 3 separate organoids each showed similar results. G. Log2 (FURIN expression) in rs4702 AA and GG D43 and D77 hiPSC-derived human cortical spheroids (hCS, left) and subpallium spheroids (hSS, right) through qPCR H. Altered kinase activity. Waterfall plots showing changes in degree of phosphorylation at reporter peptides for rs4702 AA vs GG neurons. Peptides with increased (FC > 1.30) or decreased phosphorylation (FC < 0.70) are highlighted in black and red, respectively (left panel). A kinase network was obtained by growing the kinome array hits with kinase interacting partners as identified using STRING. Circle size corresponds to the number of interactions, with larger circles having more interactions. Thick lines represent interactions with a kinome array direct hit, while dashed lines represent interactions made between kinome array indirect hits (right panel). All boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range).
A. Mean difference (MA) plots of SNAP91 and TSNARE1 CRISPRa/i differential expression in NGN2-excitatory neurons, with significantly differentially expressed genes highlighted (empirical Bayes method + multiple testing correction, FDR <5%). B. Expression variance is partitioned into fractions attributable to each experimental variable. Violin plots show the percentage of variance explained by each variable over all genes (n=19,255) and samples (n=28), with violin width indicating density estimate. Overlaid boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range). C. Hierarchical clustering of RNA-Seq samples from (A) with 2699 RNA-Seq samples, including brain tissue, hiPSC-derived neural cells, pluripotent cells as well as fibroblasts and blood. The current study’s NGN2-excitatory neurons show clustering by cell type. D. Principle component analysis of 2699 samples as in (C). E. Detailed schematic of assessing synaptic effect of increased SNAP91 expression in NGN2-excitatory neurons using a fluorescent labeling strategy (Fig. 3d). Lawns of either dCas9-VPR + scramble gRNA or dCas9-VPR + SNAP91 gRNA NGN2-excitatory neurons were prepared. Rare dCas9-VPR NGN2-excitatory neurons labeled by either dTomato (scramble gRNA) or eGFP (SNAP91 gRNA) were both seeded as ~1% of each neuronal lawn. The effect of increasing SNAP91 expression on postsynaptic function is measured by comparing synaptic activity between differently colored neurons within the same neuronal lawn. The effect of increasing SNAP91 expression on presynaptic function is measured by comparing electrical activity between same-colored neurons on different lawns. F. Representative traces of sEPSCs (spontaneous Excitatory Postsynaptic Currents) from day 28-31 NGN2-excitatory neurons with each condition described in the figure. G. Representative summary heatmap and clustering of mean values of synapse-associated features as measured in 612 samples (2 batches, two groups (CRISPRa/i), three gRNA treatments (scramble, SNAP91, TSNARE1), each of which were imaged using 3 channels as stained for nuclear, axon/dendritic, and synaptic markers using 5 Z levels images as obtained from confocal immunofluorescence imaging. H. Average burst duration (top) and mean firing rate (bottom) in SNAP91 and TSNARE1 CRISPRa/i D23 NGN2-excitatory neurons (left, n=24 independent cell culture wells ea) and at various time points from 15 to 35 days in vitro (DIV) (right, n=48 ea across 4 time points). Line fitted through locally weighted smoothing (loess). Shaded areas represent 95% confidence interval. All boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range); overlaid scatter plots show means of replicates from individual lines. P-values were calculated using a two-sided t-test. I. Analysis of concordance between differential expression results of SNAP91 CRISPRi and TSNARE1 CRISPRa in NGN2-excitatory neurons (n=4 independent samples). All genes are plotted. Shared significant DEGs are marked in red. Concordance of red genes was evaluated based on Spearman correlation between t-statistics from the two data sets (adj. R2 = 0.7, p= 7.3e−23). P-values were computed through a one-sided hypothesis test for the Spearman correlation coefficients being greater than zero.
A. Mean difference (MA) plots of differential expression in SNAP91, TSNARE1, CLCN3 CRISPRa, FURIN RNAi, the additive model of these perturbations, the combinatorial perturbation and the resulting synergistic effect in NGN2-excitatory neurons, with significantly differentially expressed genes highlighted (empirical Bayes method + multiple testing correction, FDR <5%). B. Heat map detailing the contrasts applied to the SZ-eQTL gene modified samples and controls to produce differential expression models as in (A). C. Expression variance is partitioned into fractions attributable to each experimental variable. Violin plots show the percentage of variance explained by each variable over all genes (n=19,469) and samples (n=34), with violin width indicating density estimate. Overlaid boxplots show first to third quartile and the median. Whiskers indicate largest/smallest observation (no larger/smaller than hinge +/- 1.5 * inter-quartile range). D. Pie chart showing percentages of genes that fall into different synergistic differential expression categories (n=19,469 genes total). E. Hierarchical clustering of the differential expression log2 (fold changes) of categories in (D), in the additive model vs. the combinatorial perturbation. CRMP1 and DLX1, as seen in Fig. 5b, are part of the ‘less up’ and ‘same up’ categories, respectively. F. Over-representation analysis (ORA)-derived significant module enrichment (FDR<5%) for ranked genes in the ‘more up’ and ‘more down’ synergistic effect categories.
Supplementary Figs. 1–4, Tables 1–7 and Note
Differentially expressed genes following gene modifications in neurons (FDR < 10%). Empirical Bayes moderation was applied to obtain precise estimates of gene-wise variability. P-values were adjusted for multiple hypotheses testing using FDR estimation.
Curated neural gene sets and categories.
Synergistic effect categories.
About this article
Cite this article
Schrode, N., Ho, SM., Yamamuro, K. et al. Synergistic effects of common schizophrenia risk variants. Nat Genet 51, 1475–1485 (2019). https://doi.org/10.1038/s41588-019-0497-5
Schizophrenia is defined by cell-specific neuropathology and multiple neurodevelopmental mechanisms in patient-derived cerebral organoids
Molecular Psychiatry (2022)
Targeting NMDA receptors in neuropsychiatric disorders by drug screening on human neurons derived from pluripotent stem cells
Translational Psychiatry (2022)
Analysis of recent shared ancestry in a familial cohort identifies coding and noncoding autism spectrum disorder variants
npj Genomic Medicine (2022)
Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits
Nature Genetics (2022)
Scientific Reports (2022)