The Dynamic Nature of Genetic Risk for Schizophrenia Within Genes Regulated by FOXP1 During Neurodevelopment

The Dynamic Nature of Genetic Risk for Schizophrenia Within Genes Regulated by FOXP1 During Neurodevelopment | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results The Dynamic Nature of Genetic Risk for Schizophrenia Within Genes Regulated by FOXP1 During Neurodevelopment Deema Ali , Gary Donohoe , View ORCID Profile Derek W. Morris doi: https://doi.org/10.1101/2025.05.12.653444 Deema Ali 1 Centre for Neuroimaging, Cognition and Genomics (NICOG), University of Galway , Ireland 2 School of Biological and Chemical Sciences, University of Galway , Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gary Donohoe 1 Centre for Neuroimaging, Cognition and Genomics (NICOG), University of Galway , Ireland 3 School of Psychology, University of Galway , Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Derek W. Morris 1 Centre for Neuroimaging, Cognition and Genomics (NICOG), University of Galway , Ireland 2 School of Biological and Chemical Sciences, University of Galway , Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Derek W. Morris For correspondence: derek.morris{at}universityofgalway.ie Abstract Full Text Info/History Metrics Preview PDF Abstract FOXP1 (Forkhead-box protein P1) is a crucial transcription factor in neural development and is associated with schizophrenia (SCZ). FOXP1 -regulated genes may contribute to genetic risk of SCZ and this may vary across different stages of neurodevelopment. We analyzed transcriptomic data from mouse and human models of FOXP1 loss-of-function across prenatal and postnatal developmental stages, including neural stem cells from embryonic mice (E14.5) and human brain organoids (equivalent to second trimester), and cortical tissues from different mouse postnatal stages P0, P7, and P47. P0 in mice corresponds to the third trimester in humans, while P7 and P47 represent early childhood and adolescence, respectively. The effect of FOXP1 disruption on gene expression in cortical tissues/cells was assayed using RNA-seq, including time-course and pairwise gene expression analysis. Linkage disequilibrium score regression assessed if FOXP1 -regulated genes were enriched for SCZ heritability. Gene-set enrichment analysis investigated if FOXP1 -regulated genes were enriched for SCZ-associated genes reported as differentially expressed in single cortical cell studies. SynGO analysis mapped FOXP1 -regulated genes to synaptic locations and functions. FOXP1 -regulated genes are enriched for SCZ heritability, with significant results for E14.5, P7 and P47 but not P0. The P7 gene-set showed the strongest enrichment for SCZ-associated genes from single cortical cell studies. FOXP1 -regulated genes at both P7 and P47 were involved in multiple synaptic functions and were mainly enriched within glutamatergic excitatory neurons, with P47 also showing enrichment within GABAergic inhibitory neurons across regions of the postnatal cortex. Prenatal FOXP1 -regulated genes were enriched in progenitor cells and also mapped to the synapse. Genetic risk for SCZ within FOXP1 -regulated genes follows a dynamic trajectory across developmental stages, showing stronger effects at timepoints that map to early childhood, followed by adolescence and second trimester. Author Summary Schizophrenia is a complex disorder caused by many genes. While existing treatments can help manage some positive symptoms, such as hallucinations and delusions, they are not effective in treating other life-limiting symptom areas. As a result, individuals with schizophrenia continue to face significant challenges, including disability, unemployment, homelessness, and social isolation. Genome-wide association studies of schizophrenia have been effective at identifying individual SNPs and genes that contribute to these phenotypes but have struggled to immediately uncover the bigger picture of the underlying biology of the disorder. Here we take an individual gene associated with schizophrenia risk, FOXP1 , which is an important regulator of brain development. Using functional genomics data from models where FOXP1 has been disrupted, we identified sets of genes regulated by FOXP1 across different developmental stages, focusing on the cortical region of the brain. Our findings reveal that FOXP1 -regulated genes are most strongly associated with schizophrenia during early childhood and adolescence. These genes are associated with synaptic components, including presynaptic and postsynaptic structures. By integrating developmental timing, cell type specificity, and functional pathways, this study provides valuable insights into the molecular mechanisms underlying schizophrenia. Introduction Forkhead Box P1 ( FOXP1 ) belongs to the FOX family of transcription factors that coordinate essential developmental processes, including within the nervous system [ 1 – 3 ]. FOXP1 is associated with a rare neurodevelopmental disorder (FOXP1 syndrome) [ 4 – 7 ]. where different types of mutations have been identified as causal [ 5 , 8 – 10 ]. Additionally, common variants in FOXP1 have been associated with schizophrenia (SCZ) [ 11 , 12 ], general cognitive ability [ 13 ] and autistic spectrum disorder (ASD) [ 14 ]. FOXP1 is expressed in both the developing and adult brain [ 3 , 15 – 17 ] and is a key regulatory gene in neural development, contributing to transcriptional mechanisms involved in neurogenesis, neuronal migration, morphogenesis, and synaptic plasticity [ 18 – 21 ]. Several studies have generated FOXP1 knockout (KO) and knockdown (KD) models to investigate the functional role of FOXP1 in the brain and to explore the molecular pathways underlying human phenotypes associated with FOXP1 . In the early stages of development, high FOXP1 levels in apical radical glial cells (aRGCs) are associated with early neurogenesis in human cortical development, while lower levels are linked to later stages [ 21 ]. FOXP1 is also involved in basal radial glial cell (bRGC) formation, where its dysregulation impairs their proliferation and differentiation, causing a long-term reduction in the number of excitatory cortical neurons [ 21 , 22 ]. In later stages of development, FOXP1 expression is predominantly confined to the pyramidal neurons of the neocortex. Deletion of foxp1 using a conditional knockout (cKO) approach (Emx1.Cre; Foxp-1 flox/flox) has been reported to cause abnormalities in vocal communication as well as neocortical cytoarchitectonic alterations via neuronal positioning and migration at early postnatal stages [ 19 ]. Apart from cortical tissue, FOXP1 is also expressed in the medium spiny neurons of the striatum as well as in the CA1/CA2 hippocampal subfields [ 3 , 23 , 24 ]. It plays a role in the differentiation of dopamine neurons in the midbrain and medium spiny neurons in the striatum [ 25 , 26 ]. Adult mice with brain specific homozygous deletion of foxp1 demonstrated developmental abnormalities in the striatum and hippocampus, dispersed neuronal organization in hippocampal CA1, reduced excitability, larger excitatory postsynaptic current amplitudes in CA1 neurons, impaired short-term memory, and ASD-like behaviors [ 16 ]. Expression analysis in a rat SCZ model identified FOXP1 as a novel SCZ candidate gene [ 27 ]. In a recent single-nuclei RNA sequencing (snRNA-seq) study of postmortem prefrontal cortical tissue, FOXP1 was prioritized as one of the key TFs targeting SCZ-associated differentially expressed genes (DEGs) in neuronal populations [ 28 ]. Additionally, Levchenko et al ., (2021) suggested that FOXP1 may contribute to immune system alterations in SCZ through interactions with immune-related genes involved in NFκB-mediated inflammatory responses, which are upregulated in SCZ [ 29 ]. Here, we explore FOXP1 ’s contribution to SCZ using RNA-seq data from FOXP1 loss-of-function models, focusing on its involvement in cortical development. Given that FOXP1 is expressed in both developing and mature brains, and that SCZ is a disorder that is linked to brain development from in utero through to adulthood [ 30 , 31 ], we utilized data generated from prenatal and postanal stages, including cortical neural stem cells (NSCs) and bRGCs from timepoints that reflect the second trimester in humans, as well as data from mouse neocortical tissues at various developmental stages - P0 (birth; equivalent to third trimester in humans), P7 (7 days after birth; equivalent to early childhood in humans), and P47 (47 days after birth; equivalent to adolescence in humans) - to investigate the involvement of FOXP1 -regulated genes in SCZ risk. These timepoints represent critical stages for the pathophysiology of neurodevelopmental disorders such as SCZ. We investigated whether FOXP1- regulated genes at various developmental stages are enriched for genes containing common SNPs associated with SCZ from GWAS and for genes differentially expressed in single cell types from the prefrontal cortical brain region of SCZ patients and control samples. Additionally, we investigated the impact of FOXP1 disruption on synaptic processes and specific cell types to better understand how FOXP1 -regulated genes contribute to the molecular mechanisms underlying SCZ. Finally, we aimed to identify trans-expression quantitative trait loci (trans-eQTL) at FOXP1 that are associated with altered expression of FOXP1 downstream target genes. Materials and Methods Ethics statement Data were directly downloaded from published studies and no further ethics approval was required. Each study is referenced, and information about ethics approval is provided in the original references. Sourcing Transcriptomic Data for FOXP1 Loss-of-Function Models We analyzed transcriptomic data derived from studies that generated FOXP1 loss-of-function models in both mouse and human across different developmental stages, including prenatal and postanal periods. For the prenatal stages, three data resources corresponding to the second trimester of human fetal development were used. The first included NSCs at embryonic day 14.5 (E14.5) of mouse development. In this study, RNA-seq was performed on isolated embryonic NSCs that were transduced in vitro with either a short hairpin RNA (shRNA) targeting foxp1 for knockdown or a scrambled shRNA as a control (CTL), with two samples analyzed for each condition [ 18 ]. The second data source included bRGCs derived from human cerebral organoids [ 22 ]. CRISPR-Cas9 technology was used to induce FOXP1 -KO in human induced pluripotent stem cells (hiPSCs), which were subsequently differentiated into cerebral organoids. snRNA-seq was then conducted on the cortical regions of the organoids to compare gene expression profiles of KO and CTL phenotypes, with three samples analyzed for each condition [ 22 ].The third source included mouse neocortical tissues at P0 (birth), which, although postnatal in mice, corresponds to the third trimester in humans and is therefore considered a prenatal stage for this study [ 32 ]. For postnatal stages, mouse neocortical tissues at P7, and P47 were analyzed [ 32 , 19 ], corresponding to early childhood, and adolescence stages of brain development in humans respectively. foxp1 -KO mice were generated using a brain-specific conditional KO of foxp1 via the Cre-Lox system. RNA-seq was subsequently performed on extracted RNA from the neocortical tissues of both foxp1 -KO and wild-type (WT) controls mice at P0, P7, and P47, with four samples analyzed for each condition and developmental stage [ 19 , 32 ]. Transcriptomic Analysis of FOXP1 at Various Developmental Stage RNA-seq raw data were downloaded directly from the Gene Expression Omnibus (GEO) database, with the following accession numbers corresponding to each dataset: GSE101633 for the embryonic NSCs, GSE98913 for neocortical tissues at P0 and P7, and GSE97181 for neocortical tissues at P47. FastQC (v0.12.1) ( http://bioinformatics.babraham.ac.uk/projects/fastqc/ ) was used for quality assessment of reads. Raw reads were trimmed for adapters using Trimmomatic (v0.39) [ 33 ]. Filtered reads were then aligned to the mouse genome mm10 ( https://genome.ucsc.edu ) using HISAT2 (v2.2.1) [ 34 ]. The BAM alignment files were then subjected to featureCounts (v2.0.3) for read counting [ 35 ]. The list of DEGs from bRGCs was directly extracted from the original paper without further analysis [ 22 ]. Time-Course Gene Expression Analysis Time-course gene expression analysis was conducted to examine the differential effects of foxp1 -KO across various developmental stages by applying a likelihood ratio test (LRT) using the DESeq2 R package [ 36 , 37 ]. This test compared the likelihood of the data under a full model (Stage + Condition + Stage:Condition) with that under a reduced model (Stage + Condition). Only data from the P0, P7, and P47 timepoints were included in the analysis. The earliest developmental stage, P0, was set as a reference stage and WT as a reference condition. The significant genes were identified at FDR < 0.05. Clustering analysis was conducted on the genes identified in LRT analysis to identify gene groups exhibiting specific expression patterns across KO and WT samples separately. The gene count data generated from pair-wise gene expression analysis (described below) was first subjected to regularized log (rlog) transformation. Clustering was then performed using the divisive hierarchical clustering method, implemented in the degPatterns function from the DEGreport R package [ 38 ]. Only genes identified from the time-course gene expression analysis with FDR of less than 0.05 were included in the clustering process. Pair-Wise Gene Expression Analysis Pair-wise differential gene expressions between foxp1 -KO and WT controls for each development stage were performed separately for each timepoint. Counts per million (CPM) values were calculated, and genes with values of 1.0 or higher in at least two replicates for either the KO or WT conditions were considered. DESeq2 (version 1.44.0) was used to detect the DEGs [ 36 , 37 ]. The significant DEGs were identified at false discovery rate (FDR) < 0.05. Gene annotation, the conversion of mouse genes to their human orthologs, was conducted on the transcripts using the BiomaRt package (version 2.60.1) [ 39 , 40 ]. Stratified Linkage Disequilibrium Score Regression Analysis Stratified linkage disequilibrium score regression (sLDSC) ( https://github.com/bulik/ldsc ) [ 41 ] was used to investigate if the FOXP1 gene-sets across the different developmental stages were enriched for heritability contributing to SCZ. GWAS summary statistics for schizophrenia (SCZ; 76,755 cases and 243,649 controls) [ 12 ] was obtained from publicly available databases (the Psychiatric Genomics Consortium website; www.med.unc.edu/pgc ). For control purposes, we carried out sLDSC analysis using GWAS summary statistics for an additional four brain-related disorders, including major depressive disorder (MDD) [ 42 ], obsessive–compulsive disorder (OCD) [ 43 ], Alzheimer’s disease (AD) [ 44 ] and stroke [ 45 ]. Linkage disequilibrium (LD) scores between SNPs were estimated using the 1000 Genomes Phase 3 European reference panel. SNPs present in HapMap 3 with an allele frequency > 0.05 were included. Heritability was stratified in a joint analysis between 53 previous function genomic annotations and each FOXP1 gene-set. Enrichment for heritability was compared to the baseline model using the Z-score to derive a (one-tailed) P-value. A Bonferroni correction was applied to determine significant enrichments, which set the corrected P value threshold at < 1.11E-03. Competitive Gene-Set Analysis of FOXP1 in SCZ Competitive gene-set enrichment analysis (GSEA) using the R package, fgsea (Fast Gene Set Enrichment Analysis)[ 46 ] was used to test if the FOXP1 gene-sets were enriched for cell-type specific DEGs for 29 different cell-types (S1 Table) derived from single-nuclei RNA sequencing (snRNA-seq) of the prefrontal cortical brain region comparing SCZ and control samples [ 28 ]. fgsea was conducted pre-ranked mode, where input gene-sets from DeSeq2 were ranked by the Wald statistic. A competitive comparison was then performed to determine whether genes that feature in a set are highly ranked in terms of differential expression compared to genes that are not in the set. Gene-sets with an FDR corrected p-value < 0.05 were considered significantly enriched. Cell-Type Enrichment Analysis of FOXP1 Gene Sets The Expression Weighted Cell-type Enrichment (EWCE) R package ( https://github.com/NathanSkene/EWCE ) was used to assess if the FOXP1 gene-sets had higher expression in a particular cell type than expected by chance [ 47 ]. This method generates random gene sets ( n = 100,000) of equal length from background genes to estimate the probability distribution. We performed enrichment analysis in a prenatal human dataset and in an adolescent mouse dataset [ 48 , 49 ]. The prenatal human dataset includes snRNA-seq data from three second-trimester fetuses and encompasses different brain regions. However, the analysis was restricted to 17 distinct clusters of nuclei from the frontal cortex (FC) [ 48 ]. The adolescent mouse dataset includes data from 19 regions across the central and peripheral nervous system of mice at post-natal days 12-30 and at 6- to 8-weeks [ 49 ]. The significance of the enriched expression of the FOXP1 gene-sets relative to the background genes in each cell type was assessed by calculating the difference in standard deviations between the two expression profiles. The significant cell types were identified at FDR < 0.05. Functional Enrichment Analysis To analyze the enrichment of synaptic gene ontologies among the FOXP1 gene-sets, we used SynGO ( https://www.syngoportal.org/ ) [ 50 ], an expert-curated resource for synaptic GO analysis. Analyses for GO terms, including biological processes and cellular components were performed. The analysis used cortex tissue-expressed genes (n=16,985) as a background gene-set (S2 Table). Ontologies with an FDR corrected p-value < 0.01 were considered significantly enriched. Results Analysis of FOXP1 in Neocortical Tissues Time-course Gene Expression Analysis We first conducted time-course gene expression analysis to determine if the effect of foxp1 -KO on downstream gene expression differed between any of the timepoints. This analysis identified 1,146 genes (FDR < 0.05; S3 Table) where the effect of foxp1 -KO on their expression differed over time. Of these mouse genes, 1,065 have human orthologs. sLDSC analysis showed that these genes were significantly enriched for genes associated with SCZ ( P =3.45E-04; S4 Table). To further explore these significant genes, their regularized log2-transformed counts were grouped based on similar expression patterns, resulting in the formation of 20 distinct clusters (S5 Table). The expression patterns across the developmental stages for each cluster, along with the scaled expression levels of individual genes, are illustrated in Fig 1 . Among the identified genes, 35 are located with genome-wide significant loci for SCZ, and 7 of these are among the 120 genes prioritized using fine-mapping and functional genomic data (S6 Table). Additionally, 2 of the identified genes, XPO7 and DNM3 , are located within coding variants with FDR < 5% in the SCHEMA study [ 51 ]. A number of clusters (clusters 12-15 and 17-20) exhibited distinct expression patterns between the WT and KO groups across one or more developmental stages ( Fig 1 ). Among the genes in these clusters, three ( PLK2 , CACNA1I , and NEK1 ) are located with genome-wide significant loci for SCZ. Overall, this analysis indicates that there is a dynamic effect where foxp1 -KO can have different levels of impact on the expression of genes under its influence at different stages of development. It is therefore warranted to investigate the genes expressed at each timepoint for their contribution to SCZ. Fig 2 provides an illustration of the stepwise methods used from here to study FOXP1 -regulated genes over time. Download figure Open in new tab Fig 1. Clusters of Significant Genes Identified by Time-Course Gene Expression Analysis Across Different Developmental Stages in Mouse Neocortical Tissue. Divisive hierarchical clustering was performed for 1,146 genes (FDR < 0.05) according to log2 normalized read counts. The cluster number and the corresponding number of genes are provided for each cluster. Genes are plotted on the y-axis according to the scaled expression value (zscore). Lines visualize the expression pattern across development, connecting the average expression level at each stage for genes within each cluster. KO: Knockout; CTL: Control. Download figure Open in new tab Fig 2. Schematic of Pairwise Gene Expression Analysis and Functional Enrichment Methodology for FOXP1 Loss-of-Function Models. E: embryonic; P: Postnatal day; KO: Knockout; KD: Knockdown; CTL: Control; DEGs: Differentially expressed genes; SCZ: Schizophrenia; GWAS: Genome-wide association study; snRNAseq: Single nucleus RNA sequencing. Pair-wise Gene Expression Analysis We identified 423 DEGs (186 up-regulated and 237 down-regulated) at P0, 394 DEGs (139 up-regulated and 255 down-regulated) at P7 and 1,527 DEGs (712 up-regulated and 815 down-regulated) at P47 ( Fig 3 and S7 Table). In total, there are 32 DEGs common to all three timepoints, with the vast majority showing concordant effects (i.e., 14 genes are down-regulated at each timepoint, and 15 genes are up-regulated at each timepoint). These data show that there is some overlap across stages but that the majority of DEGs are stage-specific, suggesting that FOXP1 influences the expression of different genes at different stages during development. Download figure Open in new tab Fig 3. Overlap of DEGs between FOXP1 KO-vs-WT at the postnatal developmental stages. Venn diagrams of A) all DEGs, B) down-regulated DEGs, and C) upregulated DEGs. These Venn diagrams illustrate the number of shared and unique DEGs across postnatal developmental stages. P: Postnatal day; DEGs: Differentially expressed genes. Enrichment Analysis for Schizophrenia-associated Genes from GWAS Using sLDSC regression analysis, we observed that there was no enrichment for genetic risk for SCZ among DEGs at P0 ( p =0.105) but this quickly changed for P7 ( p =4.9E-06) and P47 ( p = 5.0E-08; Fig 4A andS4 Table). There is overlap between P7 and P47 (n=224) but even when excluding these from either gene-set, both remain significantly enriched (S4 Table). No significant enrichment was detected for any of the four control phenotypes (S4 Table). These findings highlight that the contribution of genes influenced by FOXP1 to SCZ is developmental stage-specific; genes regulated by FOXP1 at P0 contribute little to genetic risk for SCZ while just a short time later (P7), variation in FOXP1 -regulated genes does contribute to genetic risk for SCZ and this is maintained at the later P47 timepoint, even though the set of DEGs changes considerably. Download figure Open in new tab Fig 4. Enrichment of FOXP1 Gene-Sets in SCZ-Associated Genes Across Different Developmental Stages in mouse neocortical tissue. (A) Results from sLDSC analysis of FOXP1 gene-sets across the different developmental stages using SCZ-GWAS data. The graph plots the enrichment values, defined as the ratio of heritability (h2) to the number SNPs, on the y-axis. The x-axis represents the different developmental stages. Two asterisks (**) indicate significance after Bonferroni correction, one asterisk (*) indicates nominal significance, and “NS” indicates not significant (p>0.05). (B) Heatmap shows the enrichment of FOXP1 gene-sets in SCZ associated gene-sets across the different developmental stages based on snRNA-seq data. The intensity of color represents the –log (FDR), with darker colors indicating more significant enrichment. Normalized Enrichment Score (NES) values are shown in the cells. A significant negative NES value indicates that members of the gene set tend to appear at the bottom of the ranked data, while a significant positive NES indicates the opposite. Cells without NES values represent non-significant enrichment (p > 0.05 after FDR correction). P: Postnatal day; SCZ: Schizophrenia. Enrichment Analysis for Schizophrenia-associated Genes from snRNA-seq Studies We next assessed if the FOXP1 gene-sets are enriched for SCZ-associated genes reported as differentially expressed in a single cell gene expression analysis of multiple cortical cell-types from SCZ patients with controls. The P7 gene-set again displayed robust enrichment for SCZ-associated genes, predominantly genes from glutamatergic excitatory neurons and, to a lesser extent, GABAergic inhibitory neurons, along with endothelial cells ( Fig 4B and S8 Table). The negative enrichment observed in all the tested SCZ-associated gene-sets suggests that the downregulated genes due to FOXP1 KO are more strongly associated with SCZ. The P47 gene-set also showed enrichment for SCZ-associated genes from almost the same cell types. However, The P7 gene-set showed stronger enrichment for SCZ-associated genes compared to P47 ( Fig 4B and S8 Table). In contrast, the P0 gene-set only exhibited enrichment for SCZ-associated genes from non-neuronal cells including endothelial, oligodendrocyte progenitor cells, and microglial gene-sets ( Fig 4B and S8 Table). This analysis complements our previous findings from the GWAS data, highlighting that the genes dysregulated by FOXP1 KO at P7 and P47 overlap with genes differentially expressed within single cell-types from SCZ postmortem samples, with a stronger enrichment observed at P7. Functional Enrichment Analysis We used SynGO to identify the role of FOXP1 in synaptic processes across the postnatal stages of development. Both the P7 and P47 gene-sets were significantly enriched within many overlapping pre- and postsynaptic cellular components and biological processes, whereas the P0 gene-set was not enriched within any of these terms (S9 Table). The significant cellular components for both P7 and P47 included postsynaptic density , integral components of pre- and post-synaptic membrane and presynaptic active zone membrane ( Fig 5 and S9 Table). The significant biological processes for both P7 and P47 included those within synapse organization (e.g. synapse assembly ), the pre-synapse (e.g., presynaptic vesicle exocytosis and regulation of presynaptic membrane potential ), the post-synapse (e.g., regulation of postsynaptic cytosolic calcium levels and regulation of postsynaptic membrane neurotransmitter receptor levels ) and synaptic signaling (e.g., chemical synaptic transmission ). We mapped genes that were both associated with SCZ and differentially expressed to enriched SynGO terms. At P7 and P47, 10 and 26 such genes mapped to these synaptic locations and functions, respectively ( Fig 5 ). Download figure Open in new tab Fig 5. SynGO cellular component (CC) and biological processes (BP) enrichment analyses of FOXP1 DEGs identified at postnatal stages of development (P7 and P47). Sunburst plot showing enriched CC OR BP terms based on the synapse-specific SynGO database annotation. The color encodes the significance of the enriched q value. Genes involved in the significant SynGO terms and located within genome-wide significant loci for SCZ are highlighted. P: Postnatal day. Cell-type Enrichment Analysis As our timepoints covered multiple stages of development, we investigated if our FOXP1 gene-sets were enriched in individual cell types from human prenatal FC [ 48 ] and from central and peripheral nervous system of adolescent mouse brain[ 49 ]. The FOXP1 gene-set from P0 was enriched for genes expressed in cycling progenitor cells and glutamatergic excitatory neurons within prenatal FC (S10 Table). In both the prenatal and postnatal cortex, P7 and P47 FOXP1 gene-sets were enriched mainly within glutamatergic excitatory neurons, with the P47 gene-set also showing enrichment in GABAergic inhibitory neurons (S10 and S11 Tables). Trans Expression Quantitative Trait Loci Analysis We hypothesized that genetic variation at FOXP1 (associated with SCZ in GWAS) could influence the expression of a downstream gene, mediated through FOXP1 ’s role as a transcription factor. This would be a trans expression quantitative trait loci (eQTL) effect and evidence of two risk genes (i.e., FOXP1 and a downstream gene) functioning within a putative risk pathway. To reduce the number of possible tests of target genes, we limited DEGs derived from gene expression analysis to only those genes among the 682 SCZ-risk genes reported in the latest GWAS (S12 Table) [ 12 ]. Out of the 1,697 DEGs identified across P7 and P47 developmental stages, 66 are located with genome-wide significant loci for SCZ (S13 Table). We took the rs60135207 SNP at FOXP1, which was associated with SCZ at genome-wide significant levels, and investigated its association with the expression levels of these 66 genes using eQTL data obtained from the Genotype-Tissue Expression (GTEx) project ( https://gtexportal.org/home/ ) [ 52 ]. We detected a trans eQTL for this SCZ risk SNP at FOXP1 with the expression of the SCZ risk gene PPIP5K1 in the cerebellum ( P -value < 4.50E-04; S14 Table). Specifically, the T allele of rs60135207 at FOXP1 is associated with both increased SCZ risk and increased expression of PPIP5K1 in the cerebellum. Analysis of FOXP1 in Cortical Progenitor Cells We also investigated the role of FOXP1 in SCZ within progenitor cells during early developmental stages by analyzing prenatal data corresponding to the second trimester of human fetal development. The datasets used in this study were from progenitor cell populations, specifically embryonic mouse cortical NSCs and human cortical bRGCs derived from 3D brain organoids. During cortical development, the RGCs serve as neural progenitor cells at the ventricular zone. While migrating toward the cortical plate, they differentiate into neurons, astrocytes, and oligodendrocytes [ 53 , 54 ]. As a result of FOXP1 loss of function, there were 1,075 DEGs in NSCs (E14.5) (611 upregulated and 464 downregulated) and 869 DEGs in bRGCs (362 upregulated and 507 downregulated). Comparative analysis revealed 85 DEGs common to both models, of which 39 displayed concordant expression changes (25 downregulated and 14 upregulated). Both FOXP1 gene-sets from the two cellular models were enriched for genetic risk for SCZ (NSCs: B = 1.35, p = 1.63E-05; bRGCs: B = 1.37, p = 4.65E-06; S4 Table). These results remained significant when we omitted genes present in either the P7 or P47 gene-sets (S4 Table). The NSC and bRGC gene-sets were both enriched in overlapping SynGO cellular compartments and biological processes, several of which were also enriched for the P7 and P47 gene-sets previously (e.g., presynaptic active zone and postsynaptic density membrane, and synapse assembly and transsynaptic signaling ; S9 Table). Genes in significant SynGO terms and located within genome-wide significant loci for SCZ are detailed in Fig 6 . Download figure Open in new tab Fig 6. SynGO cellular component (CC) and biological processes (BP) enrichment analyses of FOXP1 DEGs identified at prenatal stages of development (second trimester). Sunburst plot showing enriched CC or BP terms based on the synapse-specific SynGO database annotation. The color encodes the significance of the enriched q value. Genes involved in the significant SynGO terms and located within genome-wide significant loci for SCZ are highlighted. NSCs: Neural stem cells; bRGCs: basal radial glial cells. FOXP1 gene-sets from both cellular models were tested in scRNA-seq gene expression data from human prenatal FC (S10 Table)[ 48 ]. The NSC-derived gene set was enriched within endothelial cells, oligodendrocyte precursor cells, and radial glial cells. The bRGC-derived gene set was enriched within radial glial cells (as expected), intermediate progenitors, glutamatergic excitatory neurons, and GABAergic inhibitory neurons (S10 Table). We did not detect a trans eQTL for the SCZ risk SNP at FOXP1 affecting the expression of any genes ( n =70) altered by FOXP1 loss of function and located within genome-wide significant loci for SCZ and in these two cellular models (S15 Table). Discussion To investigate the developmental trajectory of FOXP1 -regulated genes and their contribution to SCZ, we analyzed transcriptomic data derived from cortical cells of mouse and human models of FOXP1 loss-of-function across different developmental stages and integrated it with human data from genetic association studies. This analysis, encompassing models spanning key developmental stages from fetal development to adolescence, revealed the dynamic nature of FOXP1 -regulated gene sets. FOXP1 regulates distinct gene-sets at various points in development and it is those gene-sets that are expressed at timepoints that map to the second trimester, early childhood and adolescence, but not third trimester, that are enriched for genetic risk for SCZ. These genes are expressed in different cortical cell types and involved in various synaptic functions. This highlights the value of considering developmental context when investigating the role of FOXP1 in the pathophysiology of SCZ. Time-course gene expression analysis for neocortical tissues across prenatal and postnatal stages identified 1,065 significant genes, where the effect of FOXP1 -KO on their expression differed over time. Of these, 763 overlapped with the DEGs identified from the pairwise comparisons between FOXP1 -KO and WT groups at each developmental stage. Some genes demonstrated stage-specific differential expression, while others exhibited altered expression across multiple stages. Clustering analysis of these genes revealed distinct expression patterns, grouping them into 20 clusters based on their patterns of expression across the postnatal stages. Among the genes in the clusters that exhibited distinct expression patterns between the FOXP1 -KO and WT groups across one or more developmental stages, three ( PLK2 , CACNA1I , and NEK1 ) are located with genome-wide significant loci for SCZ. The protein encoded by PLK2 is a member of the polo-like kinase (PLK) family of serine/threonine protein kinases, crucial for normal cell division [ 55 ]. During brain development, PLK2 is expressed in the cortical plate of embryos and its expression is upregulated by BDNF signaling, promoting dendritic growth in immature cortical neurons [ 56 ]. It has been shown to respond to synaptic activity, playing a crucial role in spine formation and the regulation of synaptic homeostasis [ 57 , 58 ]. Similarly, NEK1, another serine/threonine kinase, is involved in cell cycle control, ciliogenesis, and the DNA damage response [ 59 – 61 ]. The CACNA1I gene encodes CaV3.3, a T-type voltage-gated calcium channel that regulates neuronal excitability and rhythmic activity within neuronal circuits [ 62 ]. Functional studies of a CACNA1I variant suggest that reduced CaV3.3 activity may confer protection against SCZ by decreasing excitability within the thalamic reticular nucleus [ 63 ]. These findings underscore the significance of FOXP1 in regulating the expression of genes critical for neuronal development and synaptic function across different stages of brain development, providing valuable insights into the potential molecular mechanisms underlying the pathophysiology of SCZ. Pairwise comparisons across P0, P7, and P47 revealed that FOXP1 regulates gene expression in a stage-dependent manner during development. While some DEGs were common across two stages of development, and a few were consistently altered across all three stages, the majority of the DEGs exhibited stage-specific changes, indicating that FOXP1 ’s transcriptional impact dynamically changes throughout development. These findings emphasize the importance of developmental timing in understanding FOXP1 ’s potential contribution to SCZ. Using sLDSC, we observed the strongest enrichment for SCZ risk within FOXP1 -regulated genes was at the P7 stage, followed by P47, with no significant enrichment detected at the earlier P0 stage. In addition to examining SCZ risk indexed by GWAS, we investigated whether FOXP1 -related gene sets are enriched for SCZ-associated genes identified by gene expression analysis of cortical cell types from SCZ patients. Like the GWAS-based analysis before, the strongest enrichment was observed at the P7 stage and specifically within glutamatergic excitatory neurons. This finding, consistent with the predominant expression of FOXP1 in these neuronal subtypes [ 24 ], suggests a link between FOXP1 -mediated gene regulation and the development of SCZ in this specific neuronal population at this stage of development. SynGO enrichment analysis was specifically performed within gene-sets exhibiting significant enrichment for genes associated with SCZ at P7 and P47. This analysis highlighted FOXP1 ’s role in postnatal synaptic processes, with P7 and P47 gene-sets significantly enriched within multiple SynGO terms, important for synaptic connectivity and function. Among these genes, HCN1, encodes a voltage-gated potassium/sodium channel that is a main contributor to hyperpolarization-activated cation current. In addition to its role in regulating neuronal excitability, HCN1 has also been found in animal studies to play a significant role in rhythmic activity, synaptic plasticity [ 64 ]. Recent studies show that HCN1 is associated with working memory impairments in SCZ patients [ 65 ]. In addition to HCN1, other genes implicated in synaptic plasticity and neuronal excitability, such as GRM1 [ 66 ] and GRM3 [ 67 ], also emerge as potential contributors to cognitive dysfunction in SCZ. GRM1 and GRM3 encode metabotropic glutamate receptors (mGluRs), members of the G-protein coupled receptor (GPCR) superfamily, which play a key role in neurotransmitter signaling within the brain [ 68 ]. FRMPD4 is a positive regulator of dendritic spine morphogenesis and density through interaction with PSD-95 [ 69 ]. It is essential for maintaining excitatory synaptic transmission through interaction with mGluR1/5 [ 70 ]. Recent studies have shown that polymorphisms in the human FRMPD4 gene are associated with sex differences in SCZ, and mutations in FRMPD4 can cause X-linked ID [ 71 , 72 ]. At P7, two genes associated with SCZ, DLGAP2 and NGEF , are linked to dendritic spine density and synaptic function. DLGAP2 is also a scaffolding protein, directly interacts with PSD-95. De novo mutations have been reported in DLGAP2 gene in SCZ patient cohorts [ 73 ], further emphasizing its importance in the pathology of the disorder. The most recent GWAS of SCZ identified 682 genes within 287 genome-wide significant loci [ 12 ]. We identified a trans eQTL effect of a SNP in FOXP1 on the expression of one of these risk loci. Disruption of FOXP1 at P47 stage resulted in reduced expression of PPIP5K1 . PPIP5K1 encodes a dual functional inositol kinase [ 74 ]. This enzyme regulates inositol phosphate metabolism, a pathway increasingly implicated in SCZ pathophysiology [ 75 , 76 ], suggesting FOXP1 ’s role in modulating SCZ-associated molecular mechanisms. We also explored FOXP1 function in SCZ pathogenesis during second trimester-equivalent prenatal development by analyzing gene expression in mouse NSCs (E14.5) and human bRGCs. Both models showed significant dysregulation of gene expression in response to FOXP1 loss, with enrichment for genes associated with SCZ. SynGO analysis showed that these genes are involved in a wide range of synaptic functions. The mapping of SCZ-associated genes to SynGO-enriched terms demonstrated both overlapping and unique gene sets between prenatal and postnatal stages, indicating stage-specific risk factors for SCZ. Among these, one gene, SLC6A9 , was common between the two cellular models analyzed in the prenatal stage. SLC6A9 encodes the GLYT1 glycine transporter, which is responsible for maintaining low levels of glycine, an N-methyl-D-aspartate receptor (NMDAR) co-agonist, in the synaptic cleft. This suggests that SLC6A9 may play a role in the development of NMDAR hypofunction, which has been implicated in SCZ [ 77 ]. ARHGAP44 , a common gene between the prenatal and postnatal stages, is a synaptic Rho-GAP that binds to the SHANK3 protein, which is involved in dendritic spine formation and synaptic plasticity [ 78 ]. The SHANK family is closely associated with ASD and SCZ, and its interaction with SHANK3 points to a possible involvement of ARHGAP44 in neuropsychiatric conditions [ 79 ]. A limitation of the study is its reliance primarily on RNA-seq data from mouse models. While this approach provides valuable insights, it may not fully capture the complexity of FOXP1 function across species. Secondly, the study focused only on specific developmental stages where data were available and that did not include a timepoint equivalent to adulthood in humans. Thirdly, the mouse transcriptomic data corresponding to the third trimester, early childhood, and adolescence in humans are generated from bulk-tissue RNA-seq and lack cell-type resolution. In contrast, the data representing the second trimester were derived from progenitor cells, generated either through snRNA-seq or by preforming FOXP1 knockdown specifically in NSCs in vitro . This reflects distinct and specific cell types from those analyzed in the other studied stages. This study leverages FOXP1 ’s association with SCZ and gene expression data from biological models of FOXP1 to demonstrate that FOXP1 plays a dynamic role in regulating gene expression across development, with a significant impact on genes associated with SCZ risk, particularly during the second trimester, early childhood and adolescence. Our findings highlight the importance of considering the dynamic nature of brain development when investigating the genetic underpinnings of SCZ. By identifying key genes and pathways impacted by FOXP1 loss, including those involved in synaptic function, this study provides insights into the molecular mechanisms underlying FOXP1 ’s contribution to SCZ susceptibility. Funding This work was funded by grants from the University of Galway, Ireland (Hardiman Research Scholarship #128936 to DA). Conflict of Interest The authors declare no conflict of interest. Suplemmentary Information S1 Table: Lists of DEGs (Ruzicka et al., 2024) used for competitive GSEA. S2 Table: Background Gene Lists Used for SynGO Analysis. S3 Table: Significant genes identified in time-course gene expression analysis S4 Table: sLDSC Analysis Results of FOXP1 Gene-Sets Using GWAS Data for SCZ and Control Phenotypes S5 Table: Gene expression clusters of genes identified in time-course gene expression analysis S6 Table: Genes Identified from Time-Course Gene Expression Analysis and Located within Genome-Wide Significant Loci of SCZ S7 Table: Significant DEGs identified in pairwise gene expression analysis S8 Table: GSEA Analysis of FOXP1 gene-sets Using Data on snRNA-seq S9 Table: SynGO Analysis for FOXP1 Gene-sets S10 Table: EWCE analysis of FOXP1 Gene-Sets in the Cameron et al., 2023 scRNA-seq Data for FC Brain Region from Prenatal Brain. S11 Table: EWCE analysis of FOXP1 Gene-Sets in the Zeisel et al. 2018 scRNA-seq Data for FC Brain Region from postnatal Brain. S12 Table: SCZ Risk Genes reported in the Latest GWAS for SCZ (Trubetskoy et al., 2022) S13 Table: List of Genes Overlapping between DEGs (S7 Table) and Genome-wide Significant Loci for SCZ Reported in the Latest SCZ GWAS (S12 Table) S14 Table: eQTL Analysis for the LD-Independent FOXP1 SNP on Genome-wide significant loci for SCZ within P7 and P47 DEGs and Cell-Type Specific Expression in Different Brain Tissues Based on the GTEx Dataset. S15 Table: eQTL Analysis for the LD-Independent FOXP1 SNP on Genome-wide significant loci for SCZ within NSCs_E14.5 and Human Brain organoid_bRGCs DEGs and Cell-Type Specific Expression in Different Brain Tissues Based on the GTEx Dataset. Acknowledgements The authors would like to thank Dr. Aodán Laighneach (University of Galway) for his valuable input on the methodology used in this study. References 1. ↵ Lalmansingh AS , Karmakar S , Jin Y , Nagaich AK . Multiple modes of chromatin remodeling by Forkhead box proteins . Biochim Biophys Acta . 2012 ; 1819 : 707 – 715 . doi: 10.1016/j.bbagrm.2012.02.018 OpenUrl CrossRef PubMed 2. Lam EW-F , Brosens JJ , Gomes AR , Koo C-Y . Forkhead box proteins: tuning forks for transcriptional harmony . Nat Rev Cancer . 2013 ; 13 : 482 – 495 . doi: 10.1038/nrc3539 OpenUrl CrossRef PubMed 3. ↵ Ferland RJ , Cherry TJ , Preware PO , Morrisey EE , Walsh CA . Characterization of Foxp2 and Foxp1 mRNA and protein in the developing and mature brain . J Comp Neurol . 2003 ; 460 : 266 – 279 . doi: 10.1002/cne.10654 OpenUrl CrossRef PubMed Web of Science 4. ↵ Bacon C , Rappold GA . The distinct and overlapping phenotypic spectra of FOXP1 and FOXP2 in cognitive disorders . Hum Genet . 2012 ; 131 : 1687 – 1698 . doi: 10.1007/s00439-012-1193-z OpenUrl CrossRef PubMed 5. ↵ Hamdan FF , Daoud H , Rochefort D , Piton A , Gauthier J , Langlois M , et al. De Novo Mutations in FOXP1 in Cases with Intellectual Disability, Autism, and Language Impairment . Am J Hum Genet . 2010 ; 87 : 671 . doi: 10.1016/j.ajhg.2010.09.017 OpenUrl CrossRef PubMed 6. Le Fevre AK , Taylor S , Malek NH , Horn D , Carr CW , Abdul-Rahman OA , et al. FOXP1 mutations cause intellectual disability and a recognizable phenotype . Am J Med Genet A . 2013 ; 161A : 3166 – 3175 . doi: 10.1002/ajmg.a.36174 OpenUrl CrossRef PubMed 7. ↵ Sollis E , Graham SA , Vino A , Froehlich H , Vreeburg M , Dimitropoulou D , et al. Identification and functional characterization of de novo FOXP1 variants provides novel insights into the etiology of neurodevelopmental disorder . Hum Mol Genet . 2016 ; 25 : 546 – 557 . doi: 10.1093/hmg/ddv495 OpenUrl CrossRef PubMed 8. ↵ Pariani MJ , Spencer A , John M Graham J , Rimoin DL . A 785 kb deletion of 3p14.1p13, including the FOXP1 gene, associated with speech delay, contractures, hypertonia and blepharophimosis . Eur J Med Genet . 2009 ; 52 : 123 . doi: 10.1016/j.ejmg.2009.03.012 OpenUrl CrossRef PubMed 9. Carr CW , Moreno-De-Luca D , Parker C , Zimmerman HH , Ledbetter N , Martin CL , et al. Chiari I malformation, delayed gross motor skills, severe speech delay, and epileptiform discharges in a child with FOXP1 haploinsufficiency . Eur J Hum Genet . 2010 ; 18 : 1216 . doi: 10.1038/ejhg.2010.96 OpenUrl CrossRef PubMed 10. ↵ Iossifov I , O’Roak BJ , Sanders SJ , Ronemus M , Krumm N , Levy D , et al. The contribution of de novo coding mutations to autism spectrum disorder . Nature . 2014 ; 515 : 216 . doi: 10.1038/nature13908 OpenUrl CrossRef PubMed Web of Science 11. ↵ Lam M , Chen C-Y , Li Z , Martin AR , Bryois J , Ma X , et al. Comparative genetic architectures of schizophrenia in East Asian and European populations . Nat Genet . 2019 ; 51 : 1670 – 1678 . doi: 10.1038/s41588-019-0512-x OpenUrl CrossRef PubMed 12. ↵ Trubetskoy V , Panagiotaropoulou G , Awasthi S , Braun A , Kraft J , Skarabis N , et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia . Nature . 2022 ; 604 : 502 . doi: 10.1038/s41586-022-04434-5 OpenUrl CrossRef PubMed 13. ↵ Lam M , Hill WD , Trampush JW , Yu J , Knowles E , Davies G , et al. Pleiotropic Meta-Analysis of Cognition, Education, and Schizophrenia Differentiates Roles of Early Neurodevelopmental and Adult Synaptic Pathways . Am J Hum Genet . 2019 ; 105 : 334 – 350 . doi: 10.1016/j.ajhg.2019.06.012 OpenUrl CrossRef PubMed 14. ↵ Deciphering Developmental Disorders Study . Prevalence and architecture of de novo mutations in developmental disorders . Nature . 2017 ; 542 : 433 – 438 . doi: 10.1038/nature21062 OpenUrl CrossRef PubMed 15. ↵ Co M , Anderson AG , Konopka G . FOXP transcription factors in vertebrate brain development, function, and disorders . Wiley Interdiscip Rev Dev Biol . 2020 ; 9 : e375 . doi: 10.1002/wdev.375 OpenUrl CrossRef PubMed 16. ↵ Bacon C , Schneider M , Magueresse CL , Froehlich H , Sticht C , Gluch C , et al. Brain-specific Foxp1 deletion impairs neuronal development and causes autistic-like behaviour . Mol Psychiatry . 2014 ; 20 : 632 . doi: 10.1038/mp.2014.116 OpenUrl CrossRef PubMed 17. ↵ Tamura S , Morikawa Y , Iwanishi H , Hisaoka T , Senba E . Expression pattern of the winged-helix/forkhead transcription factor Foxp1 in the developing central nervous system . Gene Expr Patterns . 2003 ; 3 : 193 – 197 . doi: 10.1016/S1567-133X(03)00003-6 OpenUrl CrossRef PubMed 18. ↵ Braccioli L , Vervoort SJ , Adolfs Y , Heijnen CJ , Basak O , Pasterkamp RJ , et al. FOXP1 Promotes Embryonic Neural Stem Cell Differentiation by Repressing Jagged1 Expression . Stem Cell Rep . 2017 ; 9 : 1530 . doi: 10.1016/j.stemcr.2017.10.012 OpenUrl CrossRef PubMed 19. ↵ Araujo DJ , Toriumi K , Escamilla CO , Kulkarni A , Anderson AG , Harper M , et al. Foxp1 in Forebrain Pyramidal Neurons Controls Gene Expression Required for Spatial Learning and Synaptic Plasticity . J Neurosci . 2017 ; 37 : 10917 – 10931 . doi: 10.1523/JNEUROSCI.1005-17.2017 OpenUrl Abstract / FREE Full Text 20. Li X , Xiao J , Fröhlich H , Tu X , Li L , Xu Y , et al. Foxp1 Regulates Cortical Radial Migration and Neuronal Morphogenesis in Developing Cerebral Cortex . PLoS ONE . 2015 ; 10 : e0127671 . doi: 10.1371/journal.pone.0127671 OpenUrl CrossRef PubMed 21. ↵ Pearson CA , Moore DM , Tucker HO , Dekker JD , Hu H , Miquelajáuregui A , et al. Foxp1 Regulates Neural Stem Cell Self-Renewal and Bias Toward Deep Layer Cortical Fates . Cell Rep . 2020 ; 30 : 1964 – 1981 .e3. doi: 10.1016/j.celrep.2020.01.034 OpenUrl CrossRef PubMed 22. ↵ Park SHE , Kulkarni A , Konopka G . FOXP1 orchestrates neurogenesis in human cortical basal radial glial cells . PLOS Biol . 2023 ; 21 : e3001852 . doi: 10.1371/journal.pbio.3001852 OpenUrl CrossRef PubMed 23. ↵ Tamura S , Morikawa Y , Iwanishi H , Hisaoka T , Senba E . Foxp1 gene expression in projection neurons of the mouse striatum . Neuroscience . 2004 ; 124 : 261 – 267 . doi: 10.1016/j.neuroscience.2003.11.036 OpenUrl CrossRef PubMed Web of Science 24. ↵ Hisaoka T , Nakamura Y , Senba E , Morikawa Y . The forkhead transcription factors, Foxp1 and Foxp2, identify different subpopulations of projection neurons in the mouse cerebral cortex . Neuroscience . 2010 ; 166 : 551 – 563 . doi: 10.1016/j.neuroscience.2009.12.055 OpenUrl CrossRef PubMed Web of Science 25. ↵ Konstantoulas CJ , Parmar M , Li M . FoxP1 promotes midbrain identity in embryonic stem cell-derived dopamine neurons by regulating Pitx3 . J Neurochem . 2010 ; 113 : 836 – 847 . doi: 10.1111/j.1471-4159.2010.06650.x OpenUrl CrossRef PubMed Web of Science 26. ↵ Precious SV , Kelly CM , Reddington AE , Vinh NN , Stickland RC , Pekarik V , et al. FoxP1 marks medium spiny neurons from precursors to maturity and is required for their differentiation . Exp Neurol . 2016 ; 282 : 9 . doi: 10.1016/j.expneurol.2016.05.002 OpenUrl CrossRef PubMed 27. ↵ Ingason A , Giegling I , Hartmann AM , Genius J , Konte B , Friedl M , et al. Expression analysis in a rat psychosis model identifies novel candidate genes validated in a large case–control sample of schizophrenia . Transl Psychiatry . 2015 ; 5 : e656 – e656 . doi: 10.1038/tp.2015.151 OpenUrl CrossRef PubMed 28. ↵ Ruzicka WB , Mohammadi S , Fullard JF , Davila-Velderrain J , Subburaju S , Tso DR , et al. Single-cell multi-cohort dissection of the schizophrenia transcriptome . Science . 2024 ; 384 : eadg5136 . doi: 10.1126/science.adg5136 OpenUrl CrossRef PubMed 29. ↵ Levchenko A , Kanapin A , Samsonova A , Fedorenko OYu, Kornetova EG, Nurgaliev T, et al. A genome-wide association study identifies a gene network associated with paranoid schizophrenia and antipsychotics-induced tardive dyskinesia . Prog Neuropsychopharmacol Biol Psychiatry . 2021 ; 105 : 110134 . doi: 10.1016/j.pnpbp.2020.110134 OpenUrl CrossRef PubMed 30. ↵ Stilo SA , Murray RM . Non-Genetic Factors in Schizophrenia . Curr Psychiatry Rep . 2019 ; 21 : 100 . doi: 10.1007/s11920-019-1091-3 OpenUrl CrossRef PubMed 31. ↵ Gomes FV , Grace AA . Adolescent Stress as a Driving Factor for Schizophrenia Development-A Basic Science Perspective . Schizophr Bull . 2017 ; 43 : 486 – 489 . doi: 10.1093/schbul/sbx033 OpenUrl CrossRef PubMed 32. ↵ Usui N , Araujo DJ , Kulkarni A , Co M , Ellegood J , Harper M , et al. Foxp1 regulation of neonatal vocalizations via cortical development . Genes Dev . 2017 ; 31 : 2039 – 2055 . doi: 10.1101/gad.305037.117 OpenUrl Abstract / FREE Full Text 33. ↵ Bolger AM , Lohse M , Usadel B . Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics . 2014 ; 30 : 2114 . doi: 10.1093/bioinformatics/btu170 OpenUrl CrossRef PubMed Web of Science 34. ↵ Kim D , Paggi JM , Park C , Bennett C , Salzberg SL . Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype . Nat Biotechnol . 2019 ; 37 : 907 – 915 . doi: 10.1038/s41587-019-0201-4 OpenUrl CrossRef PubMed 35. ↵ Liao Y , Smyth GK , Shi W . featureCounts: an efficient general purpose program for assigning sequence reads to genomic features . Bioinformatics . 2014 ; 30 : 923 – 930 . doi: 10.1093/bioinformatics/btt656 OpenUrl CrossRef PubMed Web of Science 36. ↵ Anders S , Huber W . Differential expression analysis for sequence count data . Genome Biol . 2010 ; 11 : R106 . doi: 10.1186/gb-2010-11-10-r106 OpenUrl CrossRef PubMed 37. ↵ Love MI , Huber W , Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol . 2014 ; 15 : 550 . doi: 10.1186/s13059-014-0550-8 OpenUrl CrossRef PubMed 38. ↵ Report of DEG analysis • DEGreport . [cited 22 Oct 2024]. Available: https://lpantano.github.io/DEGreport/ 39. ↵ Durinck S , Spellman PT , Birney E , Huber W . Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt . Nat Protoc. 2009 ; 4 : 1184 – 1191 . doi: 10.1038/nprot.2009.97 OpenUrl CrossRef PubMed Web of Science 40. ↵ Durinck S , Moreau Y , Kasprzyk A , Davis S , De Moor B , Brazma A , et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis . Bioinformatics . 2005 ; 21 : 3439 – 3440 . doi: 10.1093/bioinformatics/bti525 OpenUrl CrossRef PubMed Web of Science 41. ↵ Bulik-Sullivan BK , Loh P-R , Finucane H , Ripke S , Yang J , Consortium SWG of the PG , et al. LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies. Nat Genet . 2015 ; 47 : 291 . doi: 10.1038/ng.3211 OpenUrl CrossRef PubMed 42. ↵ Demontis D , Walters RK , Martin J , Mattheisen M , Als TD , Agerbo E , et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder . Nat Genet . 2019 ; 51 : 63 – 75 . doi: 10.1038/s41588-018-0269-7 OpenUrl CrossRef PubMed 43. ↵ Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis: International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS) . Mol Psychiatry . 2017 ; 23 : 1181 . doi: 10.1038/mp.2017.154 OpenUrl CrossRef PubMed 44. ↵ Lambert J-C , Ibrahim-Verbaas CA , Harold D , Naj AC , Sims R , Bellenguez C , et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease . Nat Genet . 2013 ; 45 : 1452 . doi: 10.1038/ng.2802 OpenUrl CrossRef PubMed 45. ↵ Traylor M , Farrall M , Holliday EG , Sudlow C , Hopewell JC , Cheng Y-C , et al. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies . Lancet Neurol . 2012 ; 11 : 951 . doi: 10.1016/S1474-4422(12)70234-X OpenUrl CrossRef PubMed 46. ↵ Korotkevich G , Sukhov V , Budin N , Shpak B , Artyomov MN , Sergushichev A . Fast gene set enrichment analysis . bioRxiv ; 2021 . p. 060012 . doi: 10.1101/060012 OpenUrl Abstract / FREE Full Text 47. ↵ Skene NG , Grant SGN . Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment . Front Neurosci . 2016 ; 10 : 16 . doi: 10.3389/fnins.2016.00016 OpenUrl CrossRef 48. ↵ Cameron D , Mi D , Vinh N-N , Webber C , Li M , Marín O , et al. Single-Nuclei RNA Sequencing of 5 Regions of the Human Prenatal Brain Implicates Developing Neuron Populations in Genetic Risk for Schizophrenia . Biol Psychiatry . 2023 ; 93 : 157 . doi: 10.1016/j.biopsych.2022.06.033 OpenUrl CrossRef PubMed 49. ↵ Zeisel A , Hochgerner H , Lönnerberg P , Johnsson A , Memic F , Zwan J van der, et al. Molecular Architecture of the Mouse Nervous System . Cell . 2018 ; 174 : 999 – 1014 .e22. doi: 10.1016/j.cell.2018.06.021 OpenUrl CrossRef PubMed 50. ↵ Koopmans F , van Nierop P , Andres-Alonso M , Byrnes A , Cijsouw T , Coba MP , et al. SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse . Neuron . 2019 ; 103 : 217 – 234 .e4. doi: 10.1016/j.neuron.2019.05.002 OpenUrl CrossRef PubMed 51. ↵ Singh T , Poterba T , Curtis D , Akil H , Al Eissa M , Barchas JD , et al. Rare coding variants in ten genes confer substantial risk for schizophrenia . Nature . 2022 ; 604 : 509 – 516 . doi: 10.1038/s41586-022-04556-w OpenUrl CrossRef 52. ↵ GTEx Consortium . The Genotype-Tissue Expression (GTEx) project . Nat Genet . 2013 ; 45 : 580 – 585 . doi: 10.1038/ng.2653 OpenUrl CrossRef PubMed 53. ↵ Götz M , Huttner WB . The cell biology of neurogenesis . Nat Rev Mol Cell Biol . 2005 ; 6 : 777 – 788 . doi: 10.1038/nrm1739 OpenUrl CrossRef PubMed Web of Science 54. ↵ Fishell G , Kriegstein AR . Neurons from radial glia: the consequences of asymmetric inheritance . Curr Opin Neurobiol . 2003 ; 13 : 34 – 41 . doi: 10.1016/S0959-4388(03)00013-8 OpenUrl CrossRef PubMed Web of Science 55. ↵ Zhang C , Ni C , Lu H . Polo-Like Kinase 2: From Principle to Practice . Front Oncol . 2022 ; 12 . doi: 10.3389/fonc.2022.956225 OpenUrl CrossRef PubMed 56. ↵ Guo S-L , Tan G-H , Li S , Cheng X-W , Zhou Y , Jia Y-F , et al. Serum inducible kinase is a positive regulator of cortical dendrite development and is required for BDNF-promoted dendritic arborization . Cell Res . 2012 ; 22 : 387 – 398 . doi: 10.1038/cr.2011.100 OpenUrl CrossRef PubMed 57. ↵ Targeted Protein Degradation and Synapse Remodeling by an Inducible Protein Kinase | Science . [cited 6 Jan 2025 ]. Available: https://www.science.org/doi/full/10.1126/science.1082475 58. ↵ Seeburg DP , Feliu-Mojer M , Gaiottino J , Pak DTS , Sheng M . Critical role of CDK5 and Polo-like kinase 2 in homeostatic synaptic plasticity during elevated activity . Neuron . 2008 ; 58 : 571 – 583 . doi: 10.1016/j.neuron.2008.03.021 OpenUrl CrossRef PubMed Web of Science 59. ↵ White MC , Quarmby LM . The NIMA-family kinase, Nek1 affects the stability of centrosomes and ciliogenesis . BMC Cell Biol . 2008 ; 9 : 29 . doi: 10.1186/1471-2121-9-29 OpenUrl CrossRef PubMed 60. Fry AM , O’Regan L , Sabir SR , Bayliss R . Cell cycle regulation by the NEK family of protein kinases . J Cell Sci . 2012 ; 125 : 4423 – 4433 . doi: 10.1242/jcs.111195 OpenUrl Abstract / FREE Full Text 61. ↵ Pelegrini AL , Moura DJ , Brenner BL , Ledur PF , Maques GP , Henriques JAP , et al. Nek1 silencing slows down DNA repair and blocks DNA damage-induced cell cycle arrest . Mutagenesis . 2010 ; 25 : 447 – 454 . doi: 10.1093/mutage/geq026 OpenUrl CrossRef PubMed Web of Science 62. ↵ Cain SM , Snutch TP . Contributions of T-type calcium channel isoforms to neuronal firing . Channels . 2010 ; 4 : 475 – 482 . doi: 10.4161/chan.4.6.14106 OpenUrl CrossRef PubMed 63. ↵ El Ghaleb Y , Schneeberger PE , Fernández-Quintero ML , Geisler SM , Pelizzari S , Polstra AM , et al. CACNA1I gain-of-function mutations differentially affect channel gating and cause neurodevelopmental disorders . Brain . 2021 ; 144 : 2092 – 2106 . doi: 10.1093/brain/awab101 OpenUrl CrossRef PubMed 64. ↵ Notomi T , Shigemoto R . Immunohistochemical localization of Ih channel subunits, HCN1–4, in the rat brain . J Comp Neurol. 2004 ; 471 : 241 – 276 . doi: 10.1002/cne.11039 OpenUrl CrossRef PubMed Web of Science 65. ↵ Chen X , Zhang Q , Su Y , Zhao W , Li Y , Du B , et al. Evidence for the contribution of HCN1 gene polymorphism (rs1501357) to working memory at both behavioral and neural levels in schizophrenia patients and healthy controls . Schizophrenia . 2022 ; 8 : 1 – 7 . doi: 10.1038/s41537-022-00271-7 OpenUrl CrossRef 66. ↵ Yousaf H , Fatima A , Ali Z , Baig SM , Toft M , Iqbal Z . A Novel Nonsense Variant in GRM1 Causes Autosomal Recessive Spinocerebellar Ataxia 13 in a Consanguineous Pakistani Family . Genes . 2022 ; 13 : 1667 . doi: 10.3390/genes13091667 OpenUrl CrossRef 67. ↵ Tan H-Y , Chen Q , Sust S , Buckholtz JW , Meyers JD , Egan MF , et al. Epistasis between catechol-O-methyltransferase and type II metabotropic glutamate receptor 3 genes on working memory brain function . Proc Natl Acad Sci U S A . 2007 ; 104 : 12536 – 12541 . doi: 10.1073/pnas.0610125104 OpenUrl Abstract / FREE Full Text 68. ↵ Xu J , Zhu Y , Xu J , Zhu Y . Metabotropic Glutamate Receptors in Anxiety Disorder . Anxiety and Anguish - Psychological Explorations and Anthropological Figures. IntechOpen ; 2023 . doi: 10.5772/intechopen.1002630 OpenUrl CrossRef 69. ↵ Lee HW , Choi J , Shin H , Kim K , Yang J , Na M , et al. Preso, a novel PSD-95-interacting FERM and PDZ domain protein that regulates dendritic spine morphogenesis . J Neurosci Off J Soc Neurosci . 2008 ; 28 : 14546 – 14556 . doi: 10.1523/JNEUROSCI.3112-08.2008 OpenUrl Abstract / FREE Full Text 70. ↵ Hu J-H , Yang L , Kammermeier PJ , Moore CG , Brakeman PR , Tu J , et al. Preso1 dynamically regulates group I metabotropic glutamate receptors . Nat Neurosci . 2012 ; 15 : 836 – 844 . doi: 10.1038/nn.3103 OpenUrl CrossRef PubMed 71. ↵ Matosin N , Green MJ , Andrews JL , Newell KA , Fernandez-Enright F . Possibility of a sex-specific role for a genetic variant in FRMPD4 in schizophrenia, but not cognitive function . NeuroReport . 2016 ; 27 : 33 . doi: 10.1097/WNR.0000000000000491 OpenUrl CrossRef PubMed 72. ↵ Piard J , Hu J-H , Campeau PM , Rzońca S , Van Esch H , Vincent E , et al. FRMPD4 mutations cause X-linked intellectual disability and disrupt dendritic spine morphogenesis . Hum Mol Genet . 2018 ; 27 : 589 – 600 . doi: 10.1093/hmg/ddx426 OpenUrl CrossRef PubMed 73. ↵ Li J-M , Lu C-L , Cheng M-C , Luu S-U , Hsu S-H , Hu T-M , et al. Role of the DLGAP2 Gene Encoding the SAP90/PSD-95-Associated Protein 2 in Schizophrenia . PLOS ONE . 2014 ; 9 : e85373 . doi: 10.1371/journal.pone.0085373 OpenUrl CrossRef PubMed 74. ↵ Gokhale NA , Zaremba A , Janoshazi AK , Weaver JD , Shears SB . PPIP5K1 modulates ligand competition between diphosphoinositol polyphosphates and PtdIns(3,4,5)P3 for polyphosphoinositide-binding domains . Biochem J. 2013 ; 453 : 413 – 426 . doi: 10.1042/BJ20121528 OpenUrl Abstract / FREE Full Text 75. ↵ Shimon H , Sobolev Y , Davidson M , Haroutunian V , Belmaker RH , Agam G . Inositol levels are decreased in postmortem brain of schizophrenic patients . Biol Psychiatry . 1998 ; 44 : 428 – 432 . doi: 10.1016/S0006-3223(98)00071-7 OpenUrl CrossRef PubMed 76. ↵ Kunii Y , Matsumoto J , Izumi R , Nagaoka A , Hino M , Shishido R , et al. Evidence for Altered Phosphoinositide Signaling-Associated Molecules in the Postmortem Prefrontal Cortex of Patients with Schizophrenia . Int J Mol Sci . 2021 ; 22 : 8280 . doi: 10.3390/ijms22158280 OpenUrl CrossRef PubMed 77. ↵ Hashimoto K . Targeting of NMDA receptors in new treatments for schizophrenia . Expert Opin Ther Targets . 2014 ; 18 : 1049 – 1063 . doi: 10.1517/14728222.2014.934225 OpenUrl CrossRef PubMed 78. ↵ Galic M , Tsai F-C , Collins SR , Matis M , Bandara S , Meyer T . Dynamic recruitment of the curvature-sensitive protein ArhGAP44 to nanoscale membrane deformations limits exploratory filopodia initiation in neurons . eLife . 2014 ; 3 : e03116 . doi: 10.7554/eLife.03116 OpenUrl CrossRef PubMed 79. ↵ Huang C , Voglewede MM , Ozsen EN , Wang H , Zhang H . SHANK3 mutations associated with autism and schizophrenia lead to shared and distinct changes in dendritic spine dynamics in the developing mouse brain . Neuroscience . 2023 ; 528 : 1 – 11 . doi: 10.1016/j.neuroscience.2023.07.024 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted May 13, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following The Dynamic Nature of Genetic Risk for Schizophrenia Within Genes Regulated by FOXP1 During Neurodevelopment Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share The Dynamic Nature of Genetic Risk for Schizophrenia Within Genes Regulated by FOXP1 During Neurodevelopment Deema Ali , Gary Donohoe , Derek W. Morris bioRxiv 2025.05.12.653444; doi: https://doi.org/10.1101/2025.05.12.653444 Share This Article: Copy Citation Tools The Dynamic Nature of Genetic Risk for Schizophrenia Within Genes Regulated by FOXP1 During Neurodevelopment Deema Ali , Gary Donohoe , Derek W. Morris bioRxiv 2025.05.12.653444; doi: https://doi.org/10.1101/2025.05.12.653444 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17635) Bioengineering (13859) Bioinformatics (41846) Biophysics (21401) Cancer Biology (18534) Cell Biology (25422) Clinical Trials (138) Developmental Biology (13352) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24285) Genetics (15582) Genomics (22463) Immunology (17700) Microbiology (40298) Molecular Biology (17141) Neuroscience (88424) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4813) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4284) Systems Biology (9808) Zoology (2267)