Functional variants at 1p36.23 confer risk of schizophrenia through modulating RERE

Domain: neuroscience
Published: 2026-01-24
Editorial outcome: major_revision

Abstract
Genome-wide association studies have identified 1p36.23 as a schizophrenia risk locus. However, the functional variants and genes driving the association remain unknown. Here, we identified two functional variants (i.e., rs159961 and rs301792) at the 1p36.23 risk locus. Both variants reside in introns of RERE and exhibit allele-specific enhancer activity. Risk alleles of rs159961 and rs301792 increase enhancer activity by altering REST and POLR2A binding, leading to RERE upregulation. Consistently, RERE was significantly elevated in brains of schizophrenia cases. Functionally, RERE-overexpression impaired neurogenesis, altered dendritic spine density and dendritic complexity, and altered genes related to dendrite development and glutamatergic synapses. Through interacting with RARB and RXRA at the Grin2a promoter, RERE regulates the well-known schizophrenia risk gene Grin2a (encodes an NMDA receptor subunit), and RERE-overexpression impairs excitatory synaptic transmission. Our study indicates that functional variants rs159961 and rs301792 confer schizophrenia risk by upregulating RERE, which affects neuronal development and synaptic function.

Introduction
Schizophrenia (SCZ) is a severe and heterogeneous psychiatric disorder that affects approximately 1% of the global population. Due to its high prevalence, mortality, and morbidity, SCZ imposes a substantial economic burden and ranks among the top ten causes of disability worldwide. Unfortunately, the etiology of SCZ remains incompletely elucidated. Accumulating evidence indicates that genetic factors play a major role in the development of the disease. The heritability of SCZ is about 81%, underscoring the major contribution of genetic factors. So far, genome-wide association studies (GWASs) have identified more than 300 SCZ risk loci. However, translating these genetic findings into insights regarding the underlying pathomechanisms remains a major challenge. Identifying potential causal variants and genes (or variants and genes with functional consequences) from risk loci is the first step toward a mechanistic understanding of disease biology. However, this step is hampered by the linkage disequilibrium (LD) among risk variants and the complex gene regulation process. As each risk locus usually contains hundreds or thousands of risk variants that show high-level LD, prioritizing causal variants from risk loci is a daunting task. Besides, even if the causal variant has been pinpointed, identifying the target gene affected by the causal variant is difficult, as many genetic variants regulate the expression of distal genes rather than genes located nearest to the risk variants. Thus, there is a pressing need to identify functional (or potential causal) variants from the reported risk loci.

To prioritize potential functional or causal variants from SCZ risk loci, we previously applied a functional genomics approach and identified 132 functional (i.e., affecting transcription factor binding) single-nucleotide polymorphisms (SNPs). Among them, two functional SNPs (i.e., rs159961 and rs301792) on chromosome 1p36.23 showed robust associations with SCZ. These two functional variants reside in the intronic regions of RERE, a gene which was prioritized as the credible causal gene underlying 1p36.23 through fine-mapping, and affect the binding of transcription factors (TFs) REST and POLR2A. Although our functional genomics study has prioritized these two functional variants as likely causal variants at the 1p36.23 risk locus, the mechanisms by which they confer SCZ risk remain unknown. Specifically, the molecular mechanisms of their regulatory effects, the identity of their target gene, and the role of this gene in SCZ pathogenesis remain to be clarified.

In this study, we first demonstrated the regulatory mechanisms of the two variants with dual-luciferase reporter gene assays and electrophoretic mobility shift assays (EMSA). We then identified RERE as the target gene regulated by these variants through expression quantitative trait loci (eQTL) analysis, CRISPR/Cas9-mediated knockout assays, and TF knockdown assays, among others. Finally, we showed that RERE plays a critical role in regulating neurogenesis, dendritic spine morphogenesis, dendritic complexity, the expression of N-methyl-D-aspartate (NMDA) receptor subunit GluN2A, and excitatory synaptic transmission. Our study identified likely causal variants and genes at the 1p36.23 risk locus and elucidated their underlying mechanisms in SCZ. These findings translate GWAS results into disease biology and provide important insights into the pathogenesis of SCZ.

Results
rs159961 and rs301792 are functional SNPs at the 1p36.23 risk locus
GWAS have shown that 1p36.23 is a robust risk locus for SCZ. To pinpoint the likely causal variants from this locus, we previously used a functional genomics approach to identify functional (i.e., TF binding-affecting) SNPs. Our functional genomics approach showed that rs159961 and rs301792 are functional variants at the 1p36.23 risk locus. To further explore whether rs159961 and rs301792 are potential causal variants, we performed fine-mapping using SuSiE, CARMA, and FINEMAP. Genetic associations from populations of European-ancestry were used as input, and the results indicated the presence of an independent causal signal at this locus. SuSiE analysis yielded a 95% credible set that includes 55 SNPs. CARMA fine-mapping identified 102 SNPs, which comprise the 99% credible set. The 92% credible set comprised 50 variants in FINEMAP analysis. Notably, both rs301792 and rs159961 were included in the credible sets in all three analyses, with low posterior inclusion probabilities (PIPs) (rs301792 (PIP of SuSiE: 0.0073, CARMA: 0.0052, FINEMAP: 0.0066) and rs159961 (PIP of SuSiE: 0.0066, CARMA: 0.0046, FINEMAP: 0.0058)). The full results of the fine-mapping are provided in supplementary material (Supplementary Data 1–4).

SNPs rs159961 and rs301792 showed strong associations with SCZ in European populations. These associations were confirmed in both East Asian populations (29,519 cases, 44,392 controls) and cross-ancestry populations (96,806 cases and 492,818 controls) in our replication study, with the same risk alleles as in Europeans. These results indicate that these two variants are credible risk variants for SCZ. Both rs159961 and rs301792 are located in the intronic regions of RERE, and the intronic sequences containing rs159961 and rs301792 show enrichment for REST and POLR2A binding motifs. To validate the functionality of these two SNPs, we examined whether these two SNPs are located in active regulatory elements. Epigenomic data indicated that rs159961 and rs301792 are located in open chromatin regions (as detected by DNase-seq) that were marked with enhancer-associated histone modifications (H3K27ac, H3K4me1, and H3K9ac) in human neuronal cells and human brain tissues. To further assess the functional consequences of rs159961 and rs301792, we conducted dual-luciferase reporter gene assays. Both risk alleles (rs159961-T and rs301792-G) exhibited increased enhancer activity in SH-SY5Y cells (a human neuroblastoma cell line) compared with protective alleles (rs159961-C and rs301792-A). In U251 cells (a human glioblastoma cell line), the rs159961-T allele also exhibited higher enhancer activity than the C allele, whereas rs301792 alleles showed no significant difference. Notably, the allele-specific enhancer effects were absent in HEK-293T cells, indicating the regulatory effects of these variants are neuronal-dependent. Collectively, these results support the regulatory effects of these two functional variants.

Molecular regulatory mechanisms of rs159961 and rs301792
Our reporter gene assays revealed the neuronal-dependent regulatory effects of rs159961 and rs301792. To dissect the molecular mechanisms underlying these neuronal-dependent regulatory effects, we conducted EMSA. The EMSA showed that different alleles of rs159961 and rs301792 affect the binding affinity of TFs. The super-shift assay indicated that rs159961 binds to REST, as a super-shift band was observed when REST antibody was added. The risk allele (T allele) of rs159961 attenuated binding to REST compared with the protective allele (C allele). Different alleles of rs301792 also exhibited differential binding to POLR2A, as the binding intensity was decreased when POLR2A antibody was added. The risk allele (G allele) of rs301792 demonstrated stronger binding ability to POLR2A compared with the A allele. Takentogether, these results indicate that functional genetic variants rs159961 and rs301792 regulate the enhancer activity by affecting the binding affinity of REST and POLR2A, respectively.

Risk alleles of rs159961 and rs301792 confer SCZ risk by upregulating RERE expression
To further identify the target gene(s) regulated by these two functional variants, we examined their associations with gene expression in the human brain. The largest brain eQTL dataset, BrainMeta (v2), which was generated using human brain tissues, was explored. Both rs159961 and rs301792 showed significant associations with the expression of RERE (P=1.39×10⁻⁴⁸ and P=1.98×10⁻⁴⁹), RERE-AS1 (P=4.56×10⁻⁸ and P=4.72×10⁻⁸), and SLC45A1 (P=1.51×10⁻⁴ and P=1.64×10⁻⁴). The strongest associations were observed for RERE, suggesting that these two functional genetic variants may confer SCZ risk by regulating RERE expression. In addition, eQTL analysis using postmortem adult brain tissues also showed that the risk alleles of rs159961 and rs301792 were associated with higher expression of RERE, consistent with the results of reporter gene assays. To test the colocalization posterior probability (PP) between the 1p36.23 locus and the eQTLs for RERE, we conducted colocalization analysis. The results showed that the colocalization PP of the 1p36.23 locus with the eQTLs for RERE was high (0.854), supporting that genetic variants in this locus confer SCZ risk by regulating RERE expression. However, we noticed that the colocalization PP for SNP rs159961 and rs301792 was relatively low, SNP.PP.H4=0.09 for rs301792, and SNP.PP.H4=0.013 for rs159961. These consistent results suggest that risk alleles of these two functional variants contribute to SCZ risk by upregulating RERE expression.

To further investigate whether rs159961 and rs301792 regulate RERE expression, we performed CRISPR/Cas9-mediated gene editing. We knocked out ~300bp genomic sequences encompassing the rs159961 or rs301792 sites, followed by quantification of RERE expression. The real-time quantitative PCR (qPCR) results showed that deletion of these genomic regions led to downregulation of RERE, indicating that the genomic elements containing rs159961 and rs301792 function as enhancers to regulate RERE expression. In addition to RERE, rs159961 and rs301792 were also associated with SLC45A1 expression (P=1.51×10⁻⁴ and P=1.64×10⁻⁴). However, these associations were much weaker than those with RERE (P=1.39×10⁻⁴⁸ and P=1.98×10⁻⁴⁹). We further examined whether knockout of rs159961 or rs301792 affected SLC45A1 expression. The results showed that the rs159961 knockout significantly downregulated SLC45A1, but the rs301792 knockout had no significant effect. Together, these results suggest that RERE (rather than SLC45A1) is the main target gene regulated by these two functional variants.

To investigate whether rs159961 and rs301792 regulate RERE expression independently or synergistically, we performed conditional eQTL analyses and haplotype analyses. Conditional eQTL modeling indicated that rs159961 and rs301792 exert non-independent effects on RERE expression. Haplotype analysis further revealed that only the AC/GT diplotype (heterozygous combinations of both risk alleles) was significantly associated with increased RERE expression, suggesting a potential synergistic regulatory effect. rs159961 and rs301792 affect the binding of REST and POLR2A, implying that these two variants interact with REST and POLR2A to regulate RERE expression. We thus knocked down REST and POLR2A in SH-SY5Y cells using short hairpin RNAs (shRNAs). REST and POLR2A knockdown resulted in significant down-regulation of RERE. Furthermore, Pearson’s correlation analysis revealed a significant positive correlation between REST and RERE expression in the human brain. Collectively, these results indicate that these two TFs interact with rs159961 and rs301792 to regulate RERE expression.

rs159961 and rs301792 are located in intronic regions of RERE, suggesting that they may regulate RERE expression through long-range interactions. Hence, we examined whether the genomic regions containing rs159961 and rs301792 interact with the promoter region of RERE, using Hi-C data. Hi-C data showed long-range interactions between the genomic regions containing these two functional variants, and the RERE promoter in human hippocampus. Together, these results indicate that the risk alleles of rs159961 and rs301792 upregulate RERE expression by affecting the binding of REST and POLR2A.

TWAS supports RERE as a SCZ risk gene
The above data suggest that RERE is the likely causal gene regulated by rs159961 and rs301792. To further validate this, we performed a transcriptome-wide association analysis (TWAS) by integrating eQTL data from postmortem adult brain tissues (from the PsychENCODE project) and SCZ GWAS. This analysis identified 234 genes that showed significant associations with SCZ risk. Notably, the TWAS supported that RERE is a SCZ risk gene whose genetically predicted upregulation is associated with SCZ risk (Z-score=6.24, P=4.46×10⁻¹⁰). The TWAS result is consistent with the reporter gene assays and eQTL data, which showed that the risk alleles of rs159961 and rs301792 were associated with higher RERE expression. The TWAS results provide robust support for the involvement of RERE in SCZ.

RERE was significantly upregulated in the brains of SCZ cases compared with controls
TWAS and eQTL analysis suggested that functional risk variants confer SCZ risk by upregulating RERE. If this conjecture is true, RERE expression should be higher in SCZ cases than in controls. To further test whether RERE upregulation is associated with increased SCZ risk, we examined RERE expression in the brains of SCZ cases and controls. Consistent with eQTL and TWAS results, RERE expression was significantly upregulated in the brains of SCZ cases compared with controls (P=8.45×10⁻⁹). The significant upregulation of RERE in SCZ cases compared with controls provided further evidence supporting that functional risk alleles confer SCZ risk by upregulating RERE expression.

RERE affects the proliferation and differentiation of mNSCs
Our consistent and convergent results suggest that risk alleles of rs159961 and rs301792 upregulate RERE expression, which in turn contributes to SCZ susceptibility. To explore the potential role of RERE in SCZ pathophysiology, we examined its function in neurodevelopment using the NSC model, which is widely used in delineating SCZ pathogenesis. We first examined RERE expression in major cell types in the human and mouse brain. The results showed that RERE was broadly expressed, with relatively higher expression levels in neurons and astrocytes, implying that this gene may have a role in neurodevelopment. The proliferation and differentiation of NSCs are crucial for brain development, and previous studies have shown that SCZ risk genes regulate these processes. We obtained mNSCs from the subventricular zone (SVZ) of mouse brains at E14.5 and validated their identity using three well-characterized markers, SOX2, PAX6, and NESTIN. To mimic the effect of RERE upregulation on neurodevelopment, we overexpressed RERE in mNSCs. Proliferation assays showed that RERE overexpression inhibited the proliferation capacity of mNSCs. Furthermore, cell-cycle analysis revealed a significant increase in the proportion of cells in the G2/M-phase in the RERE-overexpression group compared with the controls, indicating that RERE-overexpression induced cell-cycle arrest at the G2/M-phase.

We also investigated the effect of RERE-overexpression on the differentiation of mNSCs. We used markers for astrocytes (GFAP), mature neurons (MAP2), oligodendrocytes (CLDN11), and immature neurons (TUBB3). After three days of spontaneous differentiation, the protein levels of GFAP and MAP2 were measured using IF, and the expression levels of Gfap, Map2, Cldn11, and Tubb3 were assessed using qPCR. The proportion of GFAP-positive cells did not differ significantly between the RERE-overexpression and control groups, indicating that overexpression of RERE did not affect astrocytic differentiation of mNSCs. However, the proportion of MAP2-positive cells was significantly reduced in the RERE-overexpression group compared with controls, indicating that RERE-overexpression inhibited neurogenesis. Consistent with IF results, qPCR analysis also revealed that Map2 and Tubb3 expression were significantly downregulated in the RERE-overexpression group compared with controls. By contrast, the expression of Gfap and Cldn11 was unchanged. Western blot results further confirmed that MAP2 protein levels were significantly reduced, whereas GFAP levels did not differ between the RERE-overexpression and control groups. Takentogether, these results indicate that RERE-overexpression impairs neural development by inhibiting the proliferation and differentiation of mNSCs.

RERE regulates pathways related to dendrite development and glutamatergic synapse
It has been reported that RERE acts as a nuclear receptor modulator to regulate the expression of specific genes. Moreover, RERE also regulates gene expression by acting as a coregulator of the retinoic acid (RA) signaling pathway. To elucidate the biological processes and signaling pathways regulated by RERE, we conducted transcriptome sequencing using RERE-overexpressing and control mNSCs. A total of 213 genes showed differential expression (|log2(FoldChange)|>1 and P-adj<0.05) in RERE-overexpressing and control mNSCs. Among them, 118 genes were downregulated, and 95 were upregulated. Several differentially expressed genes are related to neurodevelopment. For instance, Tnn is associated with neuronal development, Sema5a is involved in neurodevelopment and axonal projection, and has been linked to autism risk, and Erbb4, an important SCZ risk gene, is involved in central nervous system development and synaptic transmission. We validated the RNA-seq results with qPCR. The qPCR results of four selected genes (Erbb4, Sema5a, Fbln2, and Mest were selected as they exhibited highly significant P-values, i.e., ranked among the top 20%) and were consistent with RNA-seq, indicating the reliability of the transcriptomic analysis. Intriguingly, KEGG analysis revealed that the differentially expressed genes were predominantly enriched in glutamatergic, cholinergic, and GABAergic synapses, three important neurotransmitter systems that have been reported to be involved in SCZ. GO analysis showed that the differentially expressed genes were enriched in pathways related to endomembrane system organization, vesicle organization, dendrite development, and related processes. These results suggest that RERE plays an important role in regulating pathways related to dendrite development and neurotransmitter systems.

Overexpression of RERE affects the density of dendritic spines and dendritic complexity
Dendritic spine alterations have been widely reported in SCZ. Accumulating evidence supports the dendritic spine pathology of SCZ. For example, one study showed that excessive pruning of dendritic spines in late childhood or adolescence may contribute to SCZ risk. Our previous studies also showed that dysregulation of the SCZ risk genes NAGA and NMRAL1 led to altered dendritic spine density. These findings suggest that risk genes may contribute to SCZ risk by affecting dendritic spines. Consistent with these observations, our RNA-seq analysis also suggested that RERE may have a pivotal role in dendritic spine morphogenesis. Differentially expressed genes were significantly enriched in pathways related to dendrite development, GABAergic, glutamatergic, and cholinergic synapses. We thus investigated whether RERE has a role in regulating dendritic complexity and dendritic spine density. Dendritic complexity analysis showed that RERE-overexpression resulted in a significant increase in the number of neurites in rat cortical neurons. Dendritic spine density analysis further revealed that the densities of thin spines and the total spines were significantly reduced in RERE-overexpressing neurons compared with controls. In addition, compared with controls, the proportion of stubby spines was significantly increased in RERE-overexpressing neurons, while the proportions of other spine types (including long thin, filopodia, and branched spines) were significantly reduced. These results demonstrate the important role of RERE in regulating and maintaining dendritic spine density and complexity.

RERE regulates GluN2A
Transcriptome analysis revealed that the differentially expressed genes between RERE-overexpressing and control mNSCs were significantly enriched in glutamatergic and GABAergic synapses. Additionally, dysfunction of glutamatergic receptors has been widely reported in SCZ. To further explore whether RERE contributes to SCZ by affecting the glutamatergic system, we conducted western blots and qPCR analyses. The expression of NMDA receptors (NMDARs) and AMPA receptors (AMPARs) was quantified. The results revealed a significant decrease in the expression of NMDAR subunits (Grin1, Grin2a, and Grin2b) and AMPAR subunits (Gria2). We further examined the expression levels of GABAergic subunits (Gaba-br2 and Gabrg2), the dopaminergic receptor Drd2, and Synaptophysin. The results showed that the RERE-overexpression significantly upregulated Gaba-br2 and Gabrg2, while Synaptophysin expression was significantly downregulated. In contrast, Drd2 expression remained unchanged. Western blot further revealed that GluN2A (encoded by Grin2a) was significantly reduced. GluN2B expression also showed a trend toward a decrease; however, this change did not reach statistical significance. Moreover, the levels of GluA4, GluK1, and PSD95 did not show significant variations, but synaptophysin expression was increased. Taken together, these results indicate that RERE regulates GluN2A, an important subunit of NMDARs, suggesting that RERE may confer SCZ risk by modulating the glutamatergic system.

RERE modulates NMDAR-mediated miniature excitatory postsynaptic currents (mEPSCs)
Given the significant downregulation of GluN2A observed upon RERE-overexpression, we performed whole-cell recordings to examine NMDAR-mediated mEPSCs in primary rat frontal cortical neurons. mEPSCs offer a refined approach to study synaptic mechanisms, as each event reflects the quantal release of neurotransmitters and activation of receptors at individual synapses. At a holding potential of -70 mV, mEPSCs are mainly mediated by AMPARs because NMDARs are blocked by Mg²⁺. To relieve the voltage-dependent block of NMDARs, we used an Mg²⁺-free extracellular solution. We recorded NMDAR-mediated mEPSCs from rat frontal cortex neurons at -70 mV, in the presence of tetrodotoxin (TTX, blocking action potentials), bicuculline (blocking inhibitory inputs), and 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX, blocking AMPA/kainate currents). Quantitative analysis revealed that RERE-overexpression reduced peak amplitude of NMDAR-mEPSCs by 16% (to ~84% of control values), suggesting impaired synaptic efficacy and reduced functional receptor density. Conversely, no significant alteration was observed in event frequency between groups, indicating that presynaptic glutamate release remains unaffected. Cumulative probability analysis demonstrated a pronounced leftward shift in amplitude distribution for RERE-overexpressing neurons, indicating an increased proportion of small-amplitude synaptic events. Collectively, these electrophysiological findings demonstrate that RERE-overexpression impairs NMDAR-dependent synaptic transmission, which correlates mechanistically with the observed GluN2A subunit depletion.

RERE-RXRA/RARB complex regulates Grin2a via promoter binding
To investigate how RERE-overexpression in neurons leads to the downregulation of Grin2a, we first performed TF binding predictions to explore if the promoter region (~2000 bp upstream of the transcription start site, TSS) of Grin2a contains binding sites for RERE-related TFs. This analysis revealed the presence of motifs for RARB and RXRA. Within this region, we identified two fragments, designated F1 and F2, both of which contained putative binding motifs for RARB and RXRA. RERE has been reported to function as a nuclear receptor modulator that interacts with the RAR/RXR heterodimer to regulate gene expression through retinoic acid response elements (RAREs). We therefore hypothesized that RERE represses Grin2a expression via this pathway. To test this, we first performed a dual-luciferase reporter assay and found that both F1 and F2 were transcriptionally active. However, their regulatory activity was attenuated upon RERE-overexpression, suggesting that RERE acts as a repressor. Co-immunoprecipitation (Co-IP) experiments further demonstrated that RERE interacted with RXRA and RARB in rat neurons. To confirm the direct binding of RARB and RXRA to these promoter fragments, we performed Chromatin immunoprecipitation (ChIP)-qPCR, which verified their enrichment at fragments F1 and F2. Finally, knocking down Rxra or Rarb in rat neurons resulted in a significant downregulation of Grin2a expression. Together, these findings indicate that RERE interacts with RARB and RXRA at the promoter region of Grin2a to repress Grin2a expression.

Methods
Statistical fine-mapping
To identify putative causal variants within the 1p36.23 locus, we conducted fine-mapping using three methods: SuSiE, CARMA, and FINEMAP. Summary statistics from populations of European ancestry (from the largest SCZ GWAS) were used as input. Genotype data of European individuals (i.e., reference panel) were extracted from the 1000 Genomes project, and the LD matrix was calculated using PLINK (v.1.9). For SuSiE, we performed fine-mapping using the functions susie_rss() implemented in the R package susieR (v.0.14.2). For CARMA, we calculated the Z score and conducted fine-mapping using the R package CARMA (v.1.0). For the FINEMAP method, we utilized FINEMAP software (v1.4.2) to perform fine-mapping by using the Z file, LD file, and master file.

Colocalization analysis
To explore whether the same variants drive the expression of RERE and SCZ risk at the 1p36.23 locus, we conducted a colocalization analysis. The eQTL dataset was obtained from BrainMeta, which was constructed by using RNA-seq data of 2865 brain cortex samples. The SCZ GWAS dataset was obtained from Dang et al. We extracted the cis-eQTL of RERE in the 1p36.23 locus and the GWAS summary statistics of SCZ, and performed Bayesian colocalization analysis utilizing the R package coloc (v.5.2.3) with default parameters.

Investigation of the regulatory effects of rs159961 and rs301792 on RERE
To investigate whether rs159961 and rs301792 regulate RERE independently or synergistically, we performed both conditional eQTL and haplotype analyses. For conditional eQTL analyses, we applied the linear regression model implemented in R (lm() function) using the genotype and expression data from CMC, in which RERE expression was regressed on each SNP's allele either individually or jointly, thereby evaluating whether the effect of one SNP remained significant after conditioning on the other. We then phased the genotypes of rs159961 and rs301792 using Eagle2 to infer haplotypes and construct diplotypes for each individual, which resulted in a total of seven distinct diplotypes. To assess the impact of these diplotypes on RERE expression, we conducted linear regression analyses with diplotype groups included as categorical predictors. Setting individuals carrying the non-risk alleles at both SNPs on both chromatids (AC/AC) as the reference group, and subsequently estimating the association of RERE expression for each diplotype relative to this reference.

Transcriptome-wide association study
To translate the findings of SCZ GWAS into potential causal genes, we performed a TWAS to identify genes whose genetically regulated expression levels were associated with SCZ risk. Specifically, we integrated gene expression weights from the PsychENCODE project with SCZ GWAS summary statistics using FUSION software under default settings. The expression weights in the PsychENCODE dataset (N=1321) were calculated from genotype data and gene expression data with covariates adjusted. Further details are available in the original PsychENCODE publication and on the PsychENCODE website. For TWAS results, only genes that pass the Bonferroni-corrected P threshold (0.05/12434=4.02×10⁻⁶) are considered as TWAS significant genes.

Differential expression analysis
To identify differentially expressed genes between SCZ patients and controls, we utilized differential expression results from the PsychENCODE project, which quantified gene expression in postmortem human brain tissues. In brief, PsychENCODE collected RNA-seq data from postmortem human brains of SCZ patients and non-psychiatric controls across eight studies. They conducted strict quality control on all samples, discarding those with an ambiguous diagnosis, age, sex, or fewer than 10 million total reads. Outlier samples were also removed. After rigorous quality control, a total of 1442 high-quality RNA-seq data sets were obtained (including 899 non-psychiatric controls and 543 SCZ patients), and all samples were from the frontal or temporal cortex. In differential gene expression analysis, the authors considered the impact of all potential covariates on the results (including age, sex, and sequencing platform, among others) and incorporated these covariates into the differential gene expression analysis model. Finally, they identified 4821 genes that showed differential expression (FDR < 0.05) in SCZ cases and controls. More details on sample collection, expression quantification, and quality control can be found in the original article and on Synapse (doi.org/10.7303/syn12026837.1). The normalized expression values of RERE in SCZ cases and controls were downloaded from the PsychENCODE website, and visualizations were generated using ggplot2 in R (v4.1.1).

Cell culture
HEK-293T, SH-SY5Y, and U251 cell lines were purchased from the cell bank of Kunming Institute of Zoology, Chinese Academy of Sciences. B104 cells were purchased from Procell, Wuhan, China. HEK-293T, U251, and B104 cells were cultured in Dulbecco’s Modified Eagle medium (DMEM) (Gibco, C11995500BT), supplemented with 10% fetal bovine serum (FBS) (Gibco, 10091148) and 100 units/mL penicillin-streptomycin (Gibco, 15140-122). SH-SY5Y cells were cultured in DMEM (Gibco, C12430500BT) supplemented with 10% FBS, 1mM sodium pyruvate solution (Gibco, 11360070), 1× MEM non-essential amino acids solution (Gibco, 11140050), and 100 units/mL Penicillin-Streptomycin. All the cells were cultured at 37°C in a humidified atmosphere containing 5% CO₂. PCR-based detection was conducted periodically to ensure that there was no mycoplasma contamination in the cultured cells.

Plasmid construction and generation of engineered cells
The full-length human RERE coding sequence (ENST00000377464.5) was cloned into the pCDH vector containing a 3×FLAG tag and GFP reporter. All constructs were verified by Sanger sequencing. For mNSCs, RERE overexpression was achieved by lentiviral transduction, followed by puromycin selection to establish stable overexpression prior to downstream experiments. Primary neurons used for assessing RERE overexpression efficiency and synapse-associated protein expression were transduced with lentivirus. For other neuronal experiments, RERE overexpression was achieved by transient transfection. RERE-overexpressing B104 cells were generated by lentiviral transduction and selected with puromycin to obtain stable cell populations before subsequent analyses. Other plasmids and engineered cell lines used in this study are described in detail in the corresponding Methods sections below. Plasmids were obtained from commercial sources or collaborators, where indicated.

Dual-luciferase reporter gene assays
Dual-luciferase reporter gene assays were performed as previously described. As the two functional variants are not located in the promoter region, we assessed their enhancer activity. DNA sequences (about 600 bp) containing the functional variants were cloned into the pGL3-Promoter vector (Promega, E1761). HEK-293T, SH-SY5Y, and U251 cells were seeded in 96-well plates at densities of 3.5×10⁴, 1×10⁵, and 3×10⁴ cells per well, respectively. After 24 h of plating, 100 ng of recombinant vector and 20 ng of the pRL-TK vector (internal control; Promega, E2241) were transfected into HEK-293T cells. For SH-SY5Y and U251 cells, 150 ng of recombinant vector and 30 ng pRL-TK vector were transfected using the Lipofectamine™ 3000 transfection reagent (Invitrogen, L3000-015). Twenty-four hours post-transfection, the Dual-Luciferase® Reporter 1000 Assay System (Promega, E1980) was used to quantify luciferase activity in HEK-293T cells. For SH-SY5Y and U251 cells, luciferase activity was measured 48 h post-transfection. Luminoskan Ascent chemiluminescence analyzer (Thermo Scientific, Thermo Luminoskan Ascent) was used to detect fluorescence activity. All sequences were validated by Sanger sequencing, and primer sequences are listed in Supplementary Table 4.

To assess the activity of the Grin2a promoter region, two predicted motif-containing fragments, F1 and F2 (±500 bp around the putative RXRA and RARB binding motifs), were cloned into the pGL3-Promoter vector. These vectors were transfected into B104 cells, as well as B104 cells overexpressing RERE, and luciferase activity was measured 48 h post-transfection. The sequences of the promoter region of Grin2a, F1, and F2, are shown in Supplementary Fig. 10.

EMSAs
EMSAs were carried out as previously described. The EMSA Probe Biotin Labeling Kit (Beyotime, GS008) was used to label the synthesized oligonucleotide strands. The labeled oligonucleotides were then annealed. The nuclear extracts from SH-SY5Y cells were prepared using the Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, P0028). EMSA experiments were performed using the Chemiluminescent EMSA Kit (Beyotime, GS009). Nuclear proteins were first incubated without labeled probes for 10 min, then labeled probes were added, and the mixture was incubated for 20 min before electrophoresis. For super-shift experiments, the nucleoprotein was incubated with the corresponding TF antibody and unlabeled probes for 10 min, followed by the addition of labeled probes and incubation for 20 min. The sequences of all oligonucleotides are listed in Supplementary Table 5.

eQTL analysis
In order to explore whether rs159961 and rs301792 were associated with gene expression in the human brain, we examined their association with gene expression in three brain eQTL datasets, including the Common Mind Consortium (CMC), the Lieber Institute for Brain Development (LIBD), and BrainMeta v2. Briefly, the CMC dataset contains eQTL data from brain tissues of 467 post-mortem human brain samples, the LIBD dataset includes eQTL data from brain tissues of 412 post-mortem human brain samples, and the BrainMeta v2 dataset integrates brain sample genotype and RNA-seq data from seven cohorts (N=2443). Detailed information on sample collection, data processing, and eQTL generation can be found in the original studies.

CRISPR/Cas9-mediated genome editing
CRISPR/Cas9-mediated gene editing was used to knockout the segments containing the rs159961 and rs301792. Two sgRNAs (located upstream and downstream of the target SNP) were designed using the tool developed by the Zhang lab (https://zlab.bio/guide-design-resources). The sgRNAs were ligated into the pX459 vector (provided by Mr Yan Ruiqing), and the recombinant vector (3 μg) was transfected into SH-SY5Y (cultured in a 6-well plate) using the Lipofectamine™ 3000. The transfected cells were selected using puromycin (1 μg/mL), and puromycin-resistant cells were seeded into a 10 cm dish at a very low density for monoclonal growth. After several days of culture, monoclones were selected and plated onto 48-well plates. The knockout efficiency of each single clone was determined by PCR. The sequences of sgRNAs are listed in Supplementary Table 6, and the primer information for knockout efficiency verification is listed in Supplementary Table 7.

Real-time qPCR
Total RNA was extracted using TRIzol Reagent (Invitrogen, 10296028CN). Following the manufacturer’s protocol, 1 μg total RNA was reverse transcribed into complementary DNA (cDNA) using the PrimeScript™ RT reagent Kit (Takara, RR047A). The expression of the target gene was determined using TB Green® Premix Ex Taq™ II (Tli RNaseH Plus) (Takara, RR820A). ACTB (for human) or Actb (for mouse or rat) was used as the internal control, and the results were analyzed using the 2−ΔΔCt method. All qPCR experiments were performed with three independent biological replicates, each measured in three technical replicates. qPCR was carried out using a CFX96 or CFX Opus 96 Real-Time PCR System (Bio-Rad), with data acquisition performed using Bio-Rad CFX Maestro and Bio-Rad CFX Manager software. qPCR primer sequences are listed in Supplementary Data 8.

Knockdown assays
The shRNAs were designed using the online tool at https://rnaidesigner.thermofisher.com and were cloned into the pLKO.1-EGFP-Puro vector (from XIAMEN Anti-hela Biological Technology Trade Company) or the pSicoR-Ef1a-mCh-puro vector (Addgene, 31845). The lentiviral packaging plasmids psPAX2, pMD2.G, and the constructed vector were co-transfected into HEK-293T cells (cultured in a 10 cm dish) using polyethyleneimine (PEI). Forty-eight hours post-transfection, the packaged viral particles were collected through a 0.22 μm filter and were used to infect SH-SY5Y cells or rat primary cortical neurons (cultured in a 6-well plate). qPCR was used to determine the expression levels of REST, POLR2A, RERE, Rxra, Rarb, and Grin2a. All shRNA sequences are listed in Supplementary Table 8.

Isolation and characterization of mNSCs
Isolation and characterization of mNSCs were performed as previously described in refs. 24–29. Briefly, the brains of embryonic day 14.5 (E14.5) both male and female mice (C57BL/6J) were dissected, and tissues from the subventricular zone (SVZ) were obtained. The tissues were placed into pre-cooled proliferation medium (DMEM/F-12, Gibco, C11330500BT; 1× N2, Gibco, 17502048; 1× B27, Gibco, 12587010; basic fibroblast growth factor (bFGF) (20 ng/mL), STEMCELL, 78003; epidermal growth factor (EGF) (20 ng/mL), STEMCELL, 78006; and heparin, STEMCELL, 7980) and were dissociated with a 1 mL pipette (by repeated pipetting). The resultant cells were filtered through a 70 μm cell strainer and transferred into a fresh proliferation medium. After culturing for 2–3 days, neurospheres were selected and plated onto culture dishes (pre-coated with laminin, 3 μg/mL, Sigma, L2020-1mg). The identity of the isolated mNSCs was characterized with three neural stem cell markers, including SOX2 (R&D Systems, AF2018), PAX6 (Merck, AB2237), and NESTIN (Merck, MAB353).

Fluorescent-activated cell sorting
The 1.0×10⁶ mNSCs were plated onto 6-well plates, which were pre-coated with laminin (3 μg/mL). After culturing for 48 h, the mNSCs were harvested using StemPro Accutase (Gibco, A1110501). The dissociated cells were collected into a 15 mL centrifuge tube and centrifuged at 1000×g for 3 min at 4°C. The cell precipitation was gently rinsed with precooled phosphate buffer saline (PBS). The cells were then resuspended in 1 mL precooled PBS, and 3 mL absolute ethanol was added dropwise to achieve a final concentration of 75% ethanol. The mixture was thoroughly mixed and incubated at a temperature of 4°C for 24 h. The cell precipitation was collected by centrifugation at 1000×g for 5 min at 4°C and rinsed with precooled PBS. The cells were resuspended in 500 μL of propidium iodide (Beyotime, C1052) and incubated for 30 min at 37°C, shielding the samples from light. Red fluorescence was detected using a flow cytometer (BD, LSR Fortessa™), and the cell cycle was analyzed with FlowJo software version 10.8.1.

Proliferation of mNSCs
Cell Counting Kit-8 (CCK-8) and 5-Ethynyl-2’-deoxyuridine (EdU) assays were carried out as previously described. In brief, for the CCK-8 assay, 1×10⁴ cells were plated onto 96-well plates (pre-coated with laminin, 3 μg/mL). After culturing for 0, 24, 48, and 72 h, the absorbance at 450 nm was measured to estimate the cell viability. For the EdU assay, 2×10⁵ cells were plated onto 24-well plates (pre-coated with laminin, 3 μg/mL). After culturing for 48 h, 20 μM EdU (RIBO-Bio, C00054) was added into the medium and incubated for 45 min at 37°C (to label proliferating cells). EdU assays were conducted with the Cell-Light™ EdU Apollo 567 In Vitro Kit (RIBO-Bio, C10310-1) to detect EdU-labeled cells. Images were captured with a laser scanning confocal microscope (OLYMPUS, FV1000). Image analysis was performed using ImageJ software on three independent biological replicates, with five images acquired for each replicate.

Differentiation of mNSCs
Differentiation assays were performed as previously described. Briefly, 2×10⁵ cells and 1×10⁶ cells were plated onto 24-well and 6-well plates (pre-coated with laminin, 3 μg/mL), respectively. After culturing for 24 h in proliferation medium, the medium was replaced with differentiation medium (proliferation medium without bFGF and EGF). After differentiating for 3 days, cells were fixed for immunofluorescence (IF) staining. GFAP (Sigma, G9269) and MAP2 (Merck, AB5622) antibodies were used to label the astrocytes and neurons, respectively. Images were captured with a laser scanning confocal microscope (OLYMPUS, FV1000), with a total of 15 images obtained from three biological replicates. ImageJ software was used to count the GFAP-positive and MAP2-positive cells. qPCR was performed to detect the expression levels of Gfap, Map2, Tubb3, and Cldn11.

Western blot
Western Blot was carried out to determine protein expression levels. Cells were lysed in cold RIPA Lysis Buffer (Beyotime, P0013B) supplemented with PMSF (Beyotime, ST507) for 30-60 min. Homogenates were centrifuged at 12,000×g for 10 min at 4°C, and the supernatants were collected. After the protein concentration was determined using the BCA Protein Assay Kit (Beyotime, P0010S), protein samples were mixed with loading buffer and heated in a dry bath heater at 95°C for 5-10 min. Equal amounts of protein were separated by SDS-PAGE and transferred to PVDF membranes. The membranes were blocked for 2 h at room temperature in 5% non-fat milk in Tris-buffered saline with 0.1% Tween-20 (TBST). Subsequently, the membranes were incubated overnight at 4°C in primary antibody dilution prepared with 5% bovine serum albumin (BSA) in TBST. After washing the membrane with TBST six times (5 min each), the membrane was incubated for 1 h at room temperature in HRP-conjugated secondary antibody diluent prepared with 5% BSA in TBST. ECL substrate was used to detect the immunoblot signals. Protein band intensity was quantified using ImageJ software.

IF staining
After removing the medium and rinsing with PBS, the cells were fixed with 4% paraformaldehyde (PFA) (the mNSCs and the rat primary cortical neurons were fixed with 4% PFA containing 4% sucrose) for 15 min. Following three 5-min rinsing sessions in PBS, the cells were permeabilized with PBS containing 0.3% Triton X-100 (PBST) for 20 min. Next, the cells were blocked using the Immunol Staining Blocking Buffer (Beyotime, P0102) for 1 h at room temperature. Following this, the primary antibody was diluted in PBST containing 2% BSA and incubated at 4°C overnight. After several rinses with PBS, the cells were further incubated with the fluorescent secondary antibody (diluted in PBST containing 2% BSA) for 1 h at room temperature. Subsequently, the samples were mounted and imaged under a fluorescence microscope.

Transcriptome analysis
Differential expression analysis between RERE-overexpressed mNSCs and the control group was conducted following the widely used RNA-seq analysis pipeline. RNA sequencing was performed by Novogene Co. Ltd. After adapter trimming and filtering out low-quality reads, FastQC v0.11.9 was used to further confirm the high quality of reads. Clean reads were aligned to the grcm38 reference genome (downloaded from: https://cloud.biohpc.swmed.edu/index.php/s/grcm38_tran/) using HISAT2 v2.2.1. The SAM files were then sorted and converted to BAM format using SAMtools v1.9. Subsequently, StringTie v2.1.7 and PrepDE.py scripts were utilized for gene-level reads assembly and expression quantification. After filtering low-expressed genes (only genes with >10 counts in more than four samples were retained), differential expression analysis was performed on the quantified count matrix using DESeq2 v1.42.0. Genes with |Log2 FoldChange|>1 and a Benjamini-Hochberg (BH) method adjusted P-value < 0.05 were considered to be differentially expressed.

Isolation of the rats' primary cortical neurons
The brains of embryonic day 17.5–18.5 (E17.5–18.5) Sprague Dawley rats (both males and females) were dissected, and tissues from the frontal cortex were isolated. The tissues were cut into small pieces and rinsed with pre-cooled Hank’s Balanced Salt Solution (HBSS) (Gibco, 14175079) supplemented with 1% HEPES (Solarbio, H8090-100g) and 200 units/mL of penicillin-streptomycin. They were then digested in a pre-prepared digestion solution (Neurobasal, Gibco, 21103049; 2% B27, Gibco, 17504044; 1% GlutaMAX, Gibco, 35050061; 2 mg/mL Papain, Worthington, LS003119; 5 units/mL DNase I, Sigma, D4263) at 37°C for 20 min. The digestion was quenched with 5 mL of neuronal medium containing FBS (Neurobasal; 2% B27; 1% GlutaMAX; 1% Penicillin-Streptomycin; 2.5% FBS, Gibco, 10091148). The digested tissues were transferred to a 15 mL centrifuge tube, and the supernatant was aspirated after standing for 30 s. The remaining digested tissues were resuspended with 1.5 mL FBS-containing neuronal medium and slowly pipetted with the pre-sterilized pasteurized tube 8–9 times. After pipetting, the suspension was allowed to stand for 2–3 min to permit the undigested tissue mass to settle. The supernatant cell suspension was filtered through a 70 μm strainer and plated onto pre-coated 6-well plates. After 6-8 h of plating, half of the medium was aspirated from each well, and FBS-free neuronal medium (Neurobasal, 2% B27, 1% GlutaMAX) was added. The neurons were cultured for about two weeks, and the medium was changed every three days.

Analysis of the density and morphology of dendritic spines
After culturing the isolated rat primary neurons for 14–15 days, 3 μg pCDH-RERE-3×flag vector and 1 μg Venus plasmid (expressing GFP) were co-transfected into rat cortical neurons using Lipofectamine™ 3000. Three days post-transfection, IF staining was performed. The dendritic spines of neurons were captured with a laser scanning confocal microscope (ZEISS 880). NeuronStudio and ImageJ were used to analyze the density and morphology of neuronal dendritic spines.

Analysis of the complexity of neuronal dendrites
The isolated rat primary neurons were cultured for 3–4 days, then a co-transfection was performed using Lipofectamine™ 3000 to transfect 3 μg pCDH-RERE-3× flag vector and 1 μg Venus plasmid (expressing GFP) into rat cortical neurons. Neuronal morphology was captured using a ZEISS 880 confocal microscope, and the complexity of neurons was assessed using ImageJ software by Sholl analysis.

Electrophysiological recordings
Recordings were obtained using an EPC10 patch clamp (HEKA). The data were digitized at 10 kHz. The holding potential of primary rat frontal cortex neurons was set at −70 mV, and series resistance compensation was adjusted to 60–80%. Recordings with series resistances >20 MΩ or the leak current (Ileak) > −200 pA were rejected. The cells were recorded in an extracellular solution containing 140 mM NaCl (Sigma, S3014), 5 mM KCl (Sigma, P9541), 2.5 mM CaCl2 (Sigma, C7902), 10 mM HEPES (Sigma, H4034), and 10 mM D-glucose (Sigma, D9434) at pH 7.4 and 315 Osm. Electrodes with a resistance of 3–5 MΩ were pulled from borosilicate glass capillaries (WPI). The intracellular solution contained 120 mM K-Gluconate (MCE, HY-Y0569C), 5 mM NaCl (Sigma, S3014), 1 mM MgCl2 (Sigma, M2670), 0.2 mM EGTA (Sigma, E43T8), 2 mM MgATP (Sigma, A9187), 0.1 mM Na2GTP (Sigma, G8877), 10 mM HEPES (Sigma, H4034), and 10 mM phosphocreatine disodium (Sigma, HY-D0885B), adjusted to pH 7.2. When recording NMDAR-mEPSCs, 1 µM TTX (Hellobio, HB1034), 10 mM bicuculline (MCE, HY-N0219), and 10 µM CNQX (MCE, HY-15066) were added to the extracellular solution. Primary rat frontal cortex neurons from embryonic day 17.5–18.5 (E17.5–18.5) Sprague-Dawley rats were cultured on poly-D-lysine-coated (Sigma, P6407) glass coverslips and maintained at 37°C in the presence of 5% CO2 for 16–18 days before the experiment. Cells were transfected with 3 µg of plasmid per well in six-well plates, and electrophysiological recordings were performed 48–72 h after transfection. Data were analyzed using Clampfit 9.02 (Molecular Devices), Igor Pro 6.0 (WaveMetrics), and Prism 8 (GraphPad Software). Cumulative probability plots of NMDAR-mEPSC were plotted in Origin 2021. The normality of data distribution was assessed using the D’Agostino-Pearson omnibus normality test. Statistical differences were evaluated using the two-tailed Mann–Whitney U-test, with a significance threshold set at P < 0.05.

Co-IP
We performed Co-IP using the Immunoprecipitation Kit with Protein A+G Magnetic Beads (Beyotime, P2179). Primary neurons from the rat prefrontal cortex were collected and lysed in lysis buffer supplemented with protease inhibitors. After complete lysis, the samples were centrifuged at 10,000–14,000×g for 3–5 min at 4°C, and the supernatant was collected for subsequent immunoprecipitation assays. The RERE antibody (Bioss, bs-7077R) was diluted in 1× TBS and incubated with pre-washed Protein A+G magnetic beads on a rotator at room temperature for 15 min to 1 h. After washing the beads three times with 1× TBS, the collected protein lysates were added to the antibody-conjugated Protein A+G beads and incubated overnight at 4°C on a rotator. Following incubation, the supernatant was discarded, and the beads were washed three times with 1× TBS. The bound proteins were eluted by adding 100 μL of 1× SDS-PAGE Sample Loading Buffer, followed by heating at 95°C for 5 min. Finally, the samples were separated on a magnetic rack for 10 s, and the supernatant was collected for Western blot.

Chromatin immunoprecipitation (ChIP)-qPCR
We performed ChIP experiments using the SimpleChIP® Enzymatic Chromatin IP Kit (CST, 9003) according to the manufacturer’s instructions. Primary rat prefrontal cortical neurons were cross-linked with 1% formaldehyde at room temperature for 10 min, followed by quenching with glycine solution for 5 min at room temperature. After removing the supernatant, cells were washed twice with ice-cold PBS and collected in ice-cold PBS containing protease inhibitors. The cells were pelleted by centrifugation at 2000×g for 5 min at 4°C for subsequent experiments. Chromatin was digested with micrococcal nuclease and incubated at 37°C for 20 min. Cross-linked chromatin fragments were then incubated overnight at 4°C with the indicated antibodies, including IgG, RXRA (CST, 3085S), and RARB (Abcam, ab53161). For each IP reaction, 30 µL of ChIP-grade protein G magnetic beads were added and incubated for 2 h at 4°C. Enriched chromatin complexes were recovered by magnetic separation and washing, and the binding of RXRA and RARB was assessed by RT-qPCR. qPCR primer sequences are listed in Supplementary Table 9.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an interesting investigating the mechanistic underpinnings of the SCZ GWAS locus at 1p36.23. It uses bioinformatics approaches to prioritize SNPs predicted to alter TF binding sites, finding two SNPs within introns of RERE, altering TFBS for REST and POLR2A. They use a variaty of function genomic and experimental follow up assays to show that RERE expression is unregulated and that this has effects on neurogenesis, neuron morphology and physiology. There are a large number of experiments that appear to be mostly well conducted. The overarching story is compelling and of interest to the field. I have some methodology comments and questions that would help bolster the robustness of the genomics results which are the crux of the story.\n\nMajor\n1) Fine Mapping needs to be conducted / reported for the locus being investigated. The LD block looks large and the implication from the TFBS analyses is that there are multiple causal variants in the locus. What is the estimated number of independent causal signals at this GWAS locus from fine mapping? What are the credible set sizes? Most importantly, what are the posterior inclusion probabilities for the two prioritized variants?\n2) TWAS / Colocalization. Along the lines of #1 above, the simple overlap of a GWAS significant SNP with an eQTL is not sufficient to show that the two signals reflect the same underlying causal variant. This is especially true when LD blocks are large. What is the colocalization posterior probability of the GWAS locus with the e", "reviewer_2": "This paper, \"Functional variants at 1p36.23 confer risk of schizophrenia through modulating RERE,\" investigates how specific genetic variations contribute to schizophrenia (SCZ) risk by affecting the RERE gene. The study characterizes previously identified functional variants, rs159961 and rs301792, located in the intronic regions of the RERE gene at the 1p36.23 risk locus. These variants exhibit allele-specific enhancer activity. The risk alleles of both variants lead to higher enhancer activity, which is associated with increased RERE expression in human brain tissues. The risk allele of rs159961 reduces the binding of the transcription factor REST. The risk allele of rs301792 increases the binding of POLR2A. Both mechanisms result in stronger enhancer activity that subsequently regulates RERE expression. The study concludes that the functional variants rs159961 and rs301792 contribute to schizophrenia risk by upregulating RERE. This upregulation, in turn, affects neurogenesis, dendritic spine density, and synaptic transmission, thereby increasing SCZ risk. These findings provide a framework for translating GWAS findings into an understanding of disease biology and offer insights into SCZ pathogenesis. While the paper is comprehensive, it is fairly difficult to follow- especially how new versus reanalyzed data are presented is not made clear in the results or figure legend text. The methods are also lacking and critical experiments (e.g. possible synergistic effects of the two alleles) are noticeably missing.\n\nMajor comments\n1. Very little detail provided in the TWAS section of the results. Only RERE is identified on the Manhattan plot- were other genes significant as well?\n2. No details on the case control RNAseq/ microarray data? The authors cite several studies from which postmortem brain transcriptomics was used but do not provide a description of their overall analysis schema.\n3. Are genes examined in Fig 5 from the overexpression experiment? Why are they not included in the validation figures? There also doesn’t appear to be any tables with the RERE OE transcriptomic data. This needs to be included.\n4. For that matter, I could not find any additional data tables even though genomic data was generated (more below).\n5. The variants rs159961 and rs301792 exhibit allele-specific enhancer activity. Both variants lead to higher enhancer activity, which is associated with increased RERE expression in human brain tissues. However, further investigation into whether rs159961 and rs301792 regulate RERE independently or synergistically are not performed. While the authors acknowledge this, the study is predicated on understanding both alleles so why these experiments were not performed is puzzling.\n6. Elucidation of the exact regulatory mechanisms by which RERE affects the glutamatergic system. Is this a direct or indirect interaction? For example, RERE may bind to promoters of other genes that in turn regulate the observed genes. While the authors acknowledge this limitation, there is also no data table of the affected genes in the overexpression of RERE experiment which may help resolve this issue.\n7. Can the authors speculate about the possibility of rs159961 and rs301792 regulating additional genes? There appears to be a high degree of chromatin looping in this area and other genes may be impacted.\n\nMinor comments\n8. There are many grammatical mistakes and awkward sentence transitions that will need to be addressed.\n9. Lines 409-10 are not accurate. The data show that the introns containing rs159961 and rs301792 are enriched for the motif for REST and POLR2. Not that they affect the binding.\n10. EMSA is not defined.\n11. Sentence starting in line 469-472 doesn’t make sense. May need to be edited.\n12. Lines 517-518 is not true. The data shows an effect of the SNPs on RERE expression but not specifically on SCZ.", "reviewer_3": "The study by Liu et al aims to characterise functional variants underpinning a schizophrenia genome-wide significant common variant locus at 1p36.23, and provide insights into how these variants impact schizophrenia pathophysiology. The paper presents a series of functional genomics experiments that implicate two functional variants (rs159961 and rs301792) at this locus. TWAS, eQTL and CRISPR/Cas9 knockout experiments suggest these variants are associated with increased expression of RERE. Mouse neural stem cells (mNSCs) models suggest overexpression of RERE impairs proliferation of NSCs and neurogenesis, and also abnormalities in dendrite complexity and dendritic spine density. A transcriptomic analysis of RERE overexpressing NSCs identified 213 differentially expressed genes that were enriched for those with functions related to glutamatergic, cholinergic, and GABAergic synapse. Western blot and qPCR analyses also found RERE overexpression resulted in a decreased expression of the NMDAR subunit GRIN2A, which has been consistently been implicated in schizophrenia by common and rare genetic studies.\n\nThe manuscript is well written, and although a large number of analyses and results are presented the paper is easy to follow. The findings provide novel insights into the schizophrenia pathophysiological mechanisms associated with common risk alleles at 1p36.23. Since my background is in psychiatric genetics, my review specifically focusses on the TWAS, eQTL and differential gene expression elements of the paper. I have the following comments regarding these analyses.\n\n1. The authors claim their findings support the neurodevelopmental hypothesis of schizophrenia, but I did not see any data from the eQTL or TWAS analysis implicating REME in early neuronal development. The authors show in Supplementary figure 5 that REME is expressed prenatally, but are the eQTL/TWAS analyses based on postmortem adult brain tissue? Since published schizophrenia TWAS based on human fetal brain did not find any significant associations with REME (PMID: 32366953, Supplementary Table 2), could the authors please clarify whether their eQTL/TWAS findings implicate specific development timepoints in schizophrenia, and if not comment on the conflicting TWAS findings for REME reported in Hall et al 2021 (PMID: 32366953).\n2. The transcriptomic analysis identified 213 differentially expressed genes in RERE overexpressed mNSCs, but I did not see outputs of these experiments presented anywhere other than in Figure 5b. It would be helpful if the full results for each gene were presented as supplementary data, as it is not easy to identify which specific genes are differentially expressed from the figure. For example, I could not determine whether GRIN2A was differentially expressed from the figure. This comment is also relevant for any other analyses in the paper where results are only presented in figures; the raw results should also be available in table format where applicable.\n3. In the transcriptomic analysis, genes with adjusted P-value <0.05 were considered differentially expressed genes. I could not see what method was used to adjust P values, could the authors please clarify?\n4. The discussion opens by stating the \"Neurodevelopmental hypothesis SCZ posits that SCZ is caused by a combination of environmental and genetic factors”. I think this sentence should end by saying that these factors impact brain development.\n5. In the largest schizophrenia GWAS to date, RERE was prioritised through fine-mapping as the credible causal gene underlying 1p36.23 (main text and supplementary Table 12 in Trubetskoy et al 2022, PMID: 35396580). Given its relevance to the current study, I would suggest the authors cite this finding in the introduction and discussion."}, "author_response": "Response to Reviewers\nReviewer #1 (Remarks to the Author)\nThis is an interesting investigating the mechanistic underpinnings of the SCZ GWAS locus at 1p36.23.\nIt uses bioinformatics approaches to prioritize SNPs predicted to alter TF binding sites, finding two\nSNPs within introns of RERE, altering TFBS for REST and POLR2A. They use a variety of function\ngenomic and experimental follow up assays to show that RERE expression is unregulated and that\nthis has effects on neurogenesis, neuron morphology and physiology. There are a large number of\nexperiments that appear to be mostly well conducted. The overarching story is compelling and of\ninterest to the field. I have some methodology comments and questions that would help bolster the\nrobustness of the genomics results which are the crux of the story.\nResponse: We thank the reviewer for the positive and valuable comments on our work. The\npoint-by-point responses to the reviewer’s comments are provided below.\nMajor\n1) Fine Mapping needs to be conducted / reported for the locus being investigated. The LD block\nlooks large and the implication from the TFBS analyses is that there are multiple causal variants in\nthe locus. What is the estimated number of independent causal signals at this GWAS locus from\nfine mapping? What are the credible set sizes? Most importantly, what are the posterior inclusion\nprobabilities for the two prioritized variants?\nResponse: We thank the reviewer for this helpful comment. According to the suggestions of the\nreviewer, we conducted fine-mapping for the 1p36.23 locus (chr1: 7.2M-9.2M) utilizing three\nwell-documented methods (SuSiE(1), CARMA(2), and FINEMAP(3)). Genetic associations from the\nlargest European ancestry summary GWAS(4) were used as input, and the results showed that all\napproaches consistently support a single causal signal at this locus. The 92% credible set\ncomprised 50 variants in FINEMAP analysis. Similarly, SuSiE analysis yielded a 95% credible set\nthat includes 55 SNPs. CARMA fine-mapping identified 102 SNPs, which comprise the 99%\ncredible set. Notably, both rs301792 and rs159961 were included in reside the credible sets in all\nthree analyses, with modest posterior probabilities (rs301792 (PIP of SuSiE: 0.0073, CARMA:\n0.0052, FINEMAP: 0.0066) and rs159961 (PIP of SuSiE: 0.0066, CARMA: 0.0046, FINEMAP:\n0.0058)). These results further support that rs159961 and rs301792 are potential functional\nvariants. We have incorporated fine-mapping results into the revised manuscript. Page 6, lines\n103-109; page 22, lines 534-544; Supplementary Table 1-3; Supplementary Data 1.\n2) TWAS / Colocalization. Along the lines of #1 above, the simple overlap of a GWAS significant SNP\nwith an eQTL is not sufficient to show that the two signals reflect the same underlying causal variant.\nThis is especially true when LD blocks are large. What is the colocalization posterior probability of\nthe GWAS locus with the eQTLs for RERE ? TWAS can begin to address this but is a test of association\n1\nand also often prioritizes multiple genes in large LD blocks. Fine mapping of TWAS results or\nconditional analyses would be more convincing.\nResponse: We thank the reviewer for this valuable comment. To explore the colocalization\nposterior probability of the GWAS locus with the eQTLs for RERE, we extracted all cis-eQTLs for\nRERE from the BrainMeta eQTL dataset(5). Using the Coloc R package(6) with default parameters,\nwe performed Bayesian colocalization analysis. By integrating eQTLs for RERE and schizophrenia\nGWAS from Dang et al.(4), we found that the colocalization posterior probability of the GWAS\nlocus with the eQTLs for RERE was 0.854. However, we noticed that the colocalization posterior\nprobability for SNP rs159961 and rs301792 was relatively low, SNP.PP.H4 = 0.09 for rs301792, and\nSNP.PP.H4 = 0.013 for rs159961. Although fine-mapping and colocalization analysis only provided\nmodest statistical evidence to support that rs301792 and 159961 are causal variants, our serial\nfunctional assays, including dual-luciferase reporter gene assays, electrophoretic mobility shift\nassays (EMSA), and CRISPR/Cas9-mediated genome editing assays showed that these two\ngenetic variants function as enhancers to modulate the expression of RERE, indicating that these\ntwo variants are functional. Considering fine-mapping relies on statistical inference, combining\nresults from functional genomics approach, statistical fine-mapping, and functional experiments\nare needed to validate if the identified variants are functional or causal. We have incorporated\ncolocalization results into the revised manuscript. Page 8, lines 162-167; page 22, lines 546-551;\npage 23, lines 552-553; Supplementary Data 2.\n3) Cell-Type specificity — The lack of effect of some alleles in U251 and HEK-293T cell lines suggest\ncell-type specific allelic affects and is potentially quite interesting. Several single-cell eQTL datasets\nnow exist for the human brain (eg Fujita Nature Genetics 2024 ) and would be very informative for\ncharacterizing the cell-type specificity in which these variants may be acting. Line 430 “Neuronal\ndependent regulatory effects” — this has not been definitively shown, but the single cell QTL\nanalyses would help confirm these hypotheses\nResponse: We thank the reviewer for this insightful suggestion. To explore the cell-type\nspecificity of these two variants, we downloaded two human brain single-cell eQTL datasets from\nFujita et al.(7) and Bryois et al.(8). The results showed that these two variants are significantly\nassociated with RERE expression in astrocytes. However, the associations did not reach statistical\nsignificance in other cell types (refer to the table below for details). These results suggest that\nthe effects of these two variants on RERE expression areastrocyte-specific. Page 17, lines 426-\n427; page 18, lines 428-432; Supplementary Table 6.\nAlt Fujita et al.(7) Bryois et al. (8)\nSNP Cell type Ref Alt\nAF Beta Se P-value Beta Se P-value\nAstrocyte 0.640 0.042 9.92E-41 0.336 0.051 3.24E-11\nEndothelial 0.245 0.071 6.46E-04 -0.095 0.067 1.57E-01\nExcitatory\n-0.043 0.022 5.02E-02 0.004 0.030 8.88E-01\nneurons\nInhibitory\nrs159961 C T 0.30 -0.090 0.032 5.58E-03 -0.016 0.039 6.88E-01\nneurons\nMicroglia -0.029 0.044 5.13E-01 0.005 0.041 9.08E-01\nOligodendrocytes 0.088 0.041 3.19E-02 -0.023 0.042 5.82E-01\noligodendrocyte\n0.029 0.051 5.78E-01 0.035 0.054 5.14E-01\nprogenitor cells\nAstrocyte 0.646 0.042 1.76e-41 0.336 0.051 3.24E-11\nEndothelial 0.231 0.072 1.35E-03 -0.095 0.067 1.57E-01\nExcitatory\n-0.043 0.022 5.46E-02 0.004 0.030 8.88E-01\nneurons\nInhibitory.\nrs301792 A G 0.31 -0.091 0.032 5.37E-03 -0.016 0.039 6.88E-01\nneurons\nMicroglia -0.034 0.044 4.38E-01 0.005 0.041 9.08E-01\nOligodendrocytes 0.089 0.041 3.05E-02 -0.023 0.042 5.82E-01\noligodendrocyte\n0.028 0.052 5.92E-01 0.035 0.054 5.14E-01\nprogenitor cells\nRef: Reference allele in eQTL; Alt: Alternative allele in eQTL; Alt AF: The frequency of alternative\nallele; Beta: Regression coefficient for the effect of the effect allele, which represents the effect\nsize of alternative allele on the expression of target gene; Se: Standard error of Beta; P-value: P-\nvalue from the eQTL.\nThe mouse cell-type RNAseq dataset is quite old and lacks detailed classes of neurons. The RERE\nexpression annotations in mouse brain should be repeated in the latest dataset from the Allen Brain\nInstitute which used snRNAseq to profile the entire mouse brain —https://doi.org/10.1038/s41586-\n023-06812-z\nResponse: We thank the reviewer for this helpful suggestion. According to the reviewer's\nsuggestions, we visualized the expression pattern of Rere in the mouse brain using the mouse\nwhole-brain transcriptomic cell type atlas from ALLEN BRAIN MAP(9). The results revealed that\nRere is widely expressed across the mouse brain, and its expression levels were notably high in\nastrocytes, oligodendrocytes, and oligodendrocyte precursor cells. In the mouse brain spatial\ntranscriptome atlas, Rere was not captured. Single-cell eQTL of the human brain indicated that\nrs159961 and rs301792 were significantly associated with the expression of RERE in astrocytes,\nsuggesting these two variants may have cell-specific regulatory effects on RERE expression. We\nhave revised the manuscript in Supplementary Fig. 6c; page 10,lines 239-241; page 11,lines 242.\nMinor\nLine 71: I believe there are >300 independent signals but <300 (274?) actual “loci”\nconducted a cross-ancestry GWAS meta-analysis (96,806 cases and 492,818 controls) and\nidentified a total of 308 schizophrenia risk loci. We cited the most recent GWAS of schizophrenia\nin the revised manuscript. Page 4,lines 60-61; page 17,lines 404.\nReviewer #2 (Remarks to the Author):\nThis paper, \"Functional variants at 1p36.23 confer risk of schizophrenia through modulating RERE,\"\ninvestigates how specific genetic variations contribute to schizophrenia (SCZ) risk by affecting the\nRERE gene. The study characterizes previously identified functional variants, rs159961 and\nrs301792, located in the intronic regions of the RERE gene at the 1p36.23 risk locus. These variants\nexhibit allele-specific enhancer activity. The risk alleles of both variants lead to higher enhancer\nactivity, which is associated with increased RERE expression in human brain tissues. The risk allele\nof rs159961 reduces the binding of the transcription factor REST. The risk allele of rs301792\nincreases the binding of POLR2A. Both mechanisms result in stronger enhancer activity that\nsubsequently regulates RERE expression. The study concludes that the functional variants rs159961\nand rs301792 contribute to schizophrenia risk by upregulating RERE. This upregulation, in turn,\naffects neurogenesis, dendritic spine density, and synaptic transmission, thereby increasing SCZ risk.\nThese findings provide a framework for translating GWAS findings into an understanding of disease\nbiology and offer insights into SCZ pathogenesis. While the paper is comprehensive, it is fairly\ndifficult to follow- especially how new versus reanalyzed data are presented is not made clear in\nthe results or figure legend text. The methods are also lacking and critical experiments (e.g. possible\nsynergistic effects of the two alleles) are noticeably missing.\nResponse: We are grateful to the reviewer for the thoughtful and constructive feedback on our\nmanuscript. Below, we provide detailed responses to each of the reviewer’s comments.\nMajor comments\n1. Very little detail provided in the TWAS section of the results. Only RERE is identified on the\nManhattan plot- were other genes significant as well?\nResponse: We thank the reviewer for this comment, and we are sorry for the confusion. In our\nstudy, we conducted a TWAS by integrating gene expression weights from the PsychENCODE\nproject(10) and SCZ GWAS(11) summary statistics (using FUSION software), and identified 234\ngenes that are significantly associated with schizophrenia risk (P-value was corrected by\nBonferroni method: 0.05/12434 = 4.02×10-6). Given the space limitations of the figure, we only\nhighlighted RERE in the original version of our manuscript. According to the reviewer’s comment,\nwe now list RERE and the top 20 positive (Z > 0, upregulation of this gene increases schizophrenia\nrisk) and negative (Z < 0, downregulation of this gene increases schizophrenia risk) genes whose\nexpression changes are significantly associated with schizophrenia. We have provided the full\nTWAS results, Supplementary data 3, in the revised manuscript. Page 9,lines 211-212; page 10,\nlines 213-214; Fig. 2o;Supplementary Data 3.\n4\n2. No details on the case control RNA-seq/ microarray data? The authors cite several studies from\nwhich postmortem brain transcriptomics was used but do not provide a description of their overall\nanalysis schema.\nResponse: We thank the reviewer for this comment. We have provided a detailed description of\nthe analysis schema for the case-control RNA-seq data in the revised manuscript. Page 24, lines\n586-597.\n3. Are genes examined in Fig 5 from the overexpression experiment? Why are they not included in\nthe validation figures? There also doesn’t appear to be any tables with the RERE OE transcriptomic\ndata. This needs to be included.\nResponse: We thank the reviewer for these valuable comments. We sincerely apologize for the\nlack of a clear description of the RERE OE transcriptomic data. Figure 5 shows the results of\ndifferential expression genes identified by comparing transcriptomic data from RERE-\noverexpressing and control mouse neural stem cells. Figure 5a shows the top 20 statistically\nsignificant differentially expressed genes (DEGs) (ranked by P-value and expression fold change),\nand the volcano plot of Figure 5b shows the P-value and fold-change of all genes (statistically\nsignificant genes, i.e., log |(Fold Change)|>1 and Padj<0.05, are shown in red or blue). For the\n2\nvalidation experiment, we selected four genes related to neural development for validation by\nqPCR (Erbb4, Sema5a, Fbln2, and Mest were selected as they exhibited highly significant P-\nvalues), and the results showed that our RNA-seq results were consistent with the results of\nqPCR (Supplementary Figure 7). We have provided more detailed information on transcriptome\nresults in the revised manuscript. Following the reviewer’s suggestion, we also provided the\nresults of differential expression analysis in the supplementary data. Page 12, lines 280-284,\nlines\n290-292; Supplementary Data 4.\n4. For that matter, I could not find any additional data tables even though genomic data was\ngenerated (more below).\nResponse: We are sorry for the confusion. We did not generate any genomic data in this study.\nInstead, we utilized publicly available datasets, including those from Dang et al.(4),\nthe\nCommonMind Consortium (CMC)(12), the Lieber Institute for Brain Development (LIBD)(13,14), the\nBrain Meta v2 eQTL database(5), and PsychENCODE(10), as detailed in the Data Availability section.\nThe only sequencing data generated in this work was RNA-seq data, which have been deposited\nin the Genome Sequence Archive-CNCB-NGDC under accession number CRA025203. Therefore,\nno additional genomic data tables are available beyond the resources already cited. In the\nrevised manuscript, we performed fine-mapping and colocalization analyses as suggested by the\nfirst reviewer and presented the results. In addition, we provided the results of differential\nexpression analysis, the complete TWAS results, the single-cell eQTL findings for rs159961 and\nrs301792, as well as the outcomes of haplotype analysis. Supplementary Table 1-6;\nSupplementary Data 1-4; page 34, lines 898-908; page 35, lines 909-913.\n5\n5. The variants rs159961 and rs301792 exhibit allele-specific enhancer activity. Both variants lead\nto higher enhancer activity, which is associated with increased RERE expression in human brain\ntissues. However, further investigation into whether rs159961 and rs301792 regulate RERE\nindependently or synergistically are not performed. While the authors acknowledge this, the study\nis predicated on understanding both alleles so why these experiments were not performed is\npuzzling.\nResponse: We thank the reviewer for this insightful comment. To investigate whether rs159961\n(chr1:8484228, other allele/risk allele: C/T) and rs301792 (chr1:8468278, other allele/risk allele:\nA/G) act independently or synergistically in regulating RERE expression, we undertook a series\nof analyses.\nFirst, linkage disequilibrium (LD) analysis demonstrated that the two SNPs are in very strong\nlinkage disequilibrium (r2=0.985), with high collinearity (cojo-collinear> 0.900), which precluded\nthe use of GCTA-COJO conditional analysis. We therefore applied linear regression-based\nconditional eQTL models using the genotype and expression datasets from CMC(12). Risk allele of\neach SNP individually showed significant association with RERE expression (rs159961: β=0.043,\nP=6.090×10-4; rs301792: β=0.043, P=6.610×10-4). However, when adjusting for the other SNP,\nthe associations were no longer significant (rs159961 conditioned on rs301792: β=0.027,\np=0.611; rs301792 conditioned on rs159961: β=0.017, p=0.746) (Figure R1). This suggests that their\nregulatory effects are not independent.\nFigure·R1. Conditional analyses of rs301792 and rs159961·on·RERE expression.\nTo further test the independent effects, we performed haplotype analysis using data from\nCMC(12). By phasing the genotypes using Eagle2(15), we identified a total of seven diplotypes in\nthe phased samples, with the sample sizes for each diplotype summarized in Table R2. To assess\nthe relationship between different diplotypes and RERE expression, individuals carrying the non-\nrisk alleles at both SNPs (rs301792 and rs159961) on both chromatids (i.e., diplotype AC/AC)\nwere used as the reference group in linear regression analyses (using the R lm() function). We\nfound that only samples with the diplotype AC/GT, where one chromatid carries the AC\nhaplotype and the other carries the GT haplotype, showed a significant difference in RERE\n6\nexpression compared with the reference (β = 0.081, P = 4.950×10-6) (Table R3, Figure R2). This\npattern suggests a potential non-additive, synergistic effect between the two variants, thereby\npromoting RERE expression. We have now included the results of these conditional and\nhaplotype analyses in the revised manuscript and highlighted in the Discussion section that\nfurther studies are warranted to address this question. Page 9, lines 184-191; page 20, lines 507-\n512;page 23, lines 555-568; Supplementary Fig. 3a-c; Supplementary Table 4, 5.\nTable R2: Sample size of seven haplotype combinations (diplotypes) at rs301792 and rs159961\nin the CMC dataset.\nHaplotypes(rs301792-rs159961)\nHaplotype1 (Hap1) AC AC AC AC AT GC GT\nHaplotype2 (Hap2) AC AT GC GT GT GT GT\nDiplotype(Hap1/Hap2) AC/AC AC/AT AC/GC AC/GT AT/GT GC/GT GT/GT\nSample size 243 4 2 181 1 1 45\nTable R3: Linear regression analysis of RERE expression across diplotypes at rs301792 and\nrs159961, using AC/AC as the reference group.\nDiplotype (Hap1/Hap2) Estimate (β) Std.Error t.value P.value\nAC/AT 0.050 0.090 0.553 0.580\nAC/GC -0.091 0.126 -0.724 0.470\nAC/GT 0.081 0.017 4.637 4.950×10-6\nAT/GT -0.224 0.178 -1.257 0.209\nGC/GT 0.149 0.178 0.839 0.402\nGT/GT 0.043 0.029 1.502 0.134\nrs159961\n6. Elucidation of the exact regulatory mechanisms by which RERE affects the glutamatergic system.\nIs this a direct or indirect interaction? For example, RERE may bind to promoters of other genes that\nin turn regulate the observed genes. While the authors acknowledge this limitation, there is also\nno data table of the affected genes in the overexpression of RERE experiment which may help\nresolve this issue.\nResponse: We thank the reviewer for this insightful comment. To address the regulatory\nmechanisms by which RERE affects the glutamatergic system, we performed additional serial\nexperiments in the revised manuscript. Specifically, we conducted transcription factor binding\nprediction, reporter gene assays, Co-immunoprecipitation (Co-IP), Chromatin\nimmunoprecipitation (ChIP)-qPCR, and transcription factor knockdown assays. To investigate\nwhether RERE regulates Grin2a directly or indirectly, we first performed transcription factor\nbinding predictions to explore if the promoter region ( ~2000 bp upstream of the transcription\nstart site, TSS) of Grin2a contains binding sites for RERE-related transcription factors. This\nanalysis revealed the presence of binding motifs for RARB and RXRA (Fig. 8a, b; Supplementary\nFig. 9). Within the promoter region, we identified two fragments, designated F1 and F2, both of\n8\nwhich contained putative binding motifs for RARB and RXRA (Supplementary Fig. 9). Of note,\nRERE has been reported to function as a nuclear receptor modulator that interacts with the\nRAR/RXR heterodimer to regulate gene expression through retinoic acid response elements\n(RAREs)(16). We therefore hypothesized that RERE may repress Grin2a expression via this\npathway. We thus performed a dual-luciferase reporter gene assay. The results validated the\nregulatory activity of both F1 and F2 fragments (Fig. 8c). We found that the regulatory activity of\nF1 and F2 was attenuated upon RERE-overexpression (Fig. 8d; Supplementary Fig. 10), suggesting\nthat RERE acts as a repressor. Co-immunoprecipitation experiments further demonstrated that\nRERE interacted with RXRA and RARB in rat neurons (Fig. 8e). To confirm the direct binding of\nRARB and RXRA to these promoter fragments, we performed ChIP-qPCR, which verified\nenrichment of RARB and RXRA at fragments F1 and F2 (Fig. 8f, g). Finally, Rxra or Rarb knockdown\nin rat neurons resulted in a significant downregulation of Grin2a (Fig. 8h-k). Taken together, these\nresults indicate that RERE interacts with RARB and RXRA to repress Grin2a expression directly\n(Fig. 8l). The detailed results are presented in Fig. 8; Supplementary Fig. 9-10; page 15, lines 375-\n387; page 16, lines 388-397; page 25, lines 634-638; page 26, lines 639; page 33, lines 866-879;\npage 34, lines 880-896; and page 49, lines 1223-1236.\n7. Can the authors speculate about the possibility of rs159961 and rs301792 regulating additional\ngenes? There appears to be a high degree of chromatin looping in this area and other genes may\nbe impacted.\nResponse: We thank the reviewer for this valuable suggestion. In this study, we indeed\nconsidered the possibility that rs159961 and rs301792 may regulate additional genes. We\ntherefore performed relevant experiments and analysis to investigate this. Our eQTL analysis\ndemonstrated that both variants are associated with the expression levels of RERE (P = 1.39 ×\n10⁻⁴⁸ and P = 1.98 × 10⁻⁴⁹), RERE-AS1 (P = 4.56 × 10⁻⁸ and P = 4.72 × 10⁻⁸), and SLC45A1 (P = 1.51\n× 10⁻⁴ and P = 1.64 × 10⁻⁴) (Fig. 2a), suggesting that these two variants may contribute\nschizophrenia risk by modulating expression of these three genes. Furthermore, in CRISPR/Cas9-\nmediated genome editing experiments, deletion of the genomic regions encompassing rs159961\nand rs301792 resulted in a significant downregulation of RERE expression (Fig. 2f–j). Notably,\ndeletion of the rs159961-containing region also led to a marked reduction in SLC45A1 expression,\nwhereas deletion of the rs301792-containing region did not cause significant changes in SLC45A1\nexpression (Supplementary Fig. 2a, b). RERE-AS1, a long noncoding RNA that modulates the\nexpression of nearby genes, was not assayed for expression changes. These results suggest that\nrs159961 may influence the expression of both RERE and SLC45A1, whereas rs301792\npredominantly affects RERE. Although rs301792 and rs159961 may also regulate SLC45A1,\nthe\neQTL results showed that the association significance between these two SNPs and RERE is\nseveral orders of magnitude higher than SLC45A1, indicating RERE is the major target gene of\nthese two functional SNPs. We have revised the manuscript. Page 8, lines 155-157; page 8, lines\n179-182.\nMinor comments\n8. There are many grammatical mistakes and awkward sentence transitions that will need to be\naddressed.\nto correct grammatical errors and improve sentence flow and clarity throughout the text. We\nhope that the reviewer is satisfied with the writing. However, if the reviewer deems that further\nproof is needed, we are willing to send the manuscript to a professional English editing service\nfor further improvement.\n9. Lines 409-10 are not accurate. The data show that the introns containing rs159961 and rs301792\nare enriched for the motif for REST and POLR2. Not that they affect the binding.\nResponse: We thank the reviewer for pointing this out. We agree that our results indicate that\nthe intronic regions containing rs159961 and rs301792 are enriched for the REST and POLR2A\nmotifs, rather than directly demonstrating that these variants alter binding. We have revised the\ntext in the manuscript. Page 6, lines 117–118.\n10. EMSA is not defined.\nResponse: We thank the reviewer for pointing this out. According to the reviewer’s comments,\nwe have made the corresponding corrections in the manuscript. Page 7, lines136-138.\n11. Sentence starting in line 469-472 doesn’t make sense. May need to be edited.\nResponse: We thank the reviewer for pointing this out. eQTL analysis showed that rs159961 and\nrs301792 are strongly associated with the expression of RERE (P = 1.39 × 10⁻⁴⁸ and P = 1.98 ×\n10⁻⁴⁹, respectively), RERE-AS1 (P = 4.56 × 10⁻⁸ and P = 4.72 × 10⁻⁸), and SLC45A1 (P = 1.51 × 10⁻⁴\nand P = 1.64 × 10⁻⁴). The strongest associations were observed for RERE, prompting us to focus\non this gene in subsequent studies. To validate these associations, we used CRISPR–Cas9 to knock\nout the genomic segments harboring rs159961 and rs301792, and measured the expression of\nRERE and SLC45A1. Knockout of rs159961 led to downregulation of both RERE and SLC45A1,\nwhereas knockout of rs301792 resulted in downregulation of RERE only. RERE-AS1, a long\nnoncoding RNA that modulates the expression of nearby genes, was not assayed for expression\nchanges. We have revised the sentence in the manuscript. Page 8, lines 176-183.\n12. Lines 517-518 is not true. The data shows an effect of the SNPs on RERE expression but not\nspecifically on SCZ.\nResponse: We appreciate the reviewer for pointing this out. Specifically, rs159961, rs301792, and\nRERE are located within the 1p36.23 region, an important risk locus for SCZ(4,11,17-19). These two\nfunctional variants showed associations with SCZ in East Asians, with stronger association\nsignificance observed in Europeans (Supplementary Fig. 1). Our eQTL analysis suggested that\nfunctional risk variants confer SCZ risk by upregulating RERE, as the risk alleles of rs159961 and\nrs301792 were associated with higher expression of RERE (Fig. 2a-e). This regulatory effect was\nexperimentally confirmed through dual-luciferase reporter gene assays and CRISPR-Cas9-\nmediated knockout experiments (Fig. 1g-i, Fig. 2f-j). Furthermore, our TWAS results support RERE\n10\nas a schizophrenia (SCZ) risk gene, with its genetically predicted upregulation significantly\nassociated with increased SCZ risk (Z-score=6.24, P=4.46×10-10) (Fig. 2o). Consistently, RERE was\nsignificantly upregulated in brains of SCZ cases compared with controls (Fig. 2p). Taken together,\nthese findings suggest that rs159961 and rs301792 confer genetic risk by regulating RERE\nexpression, and that RERE upregulation contributes to SCZ susceptibility. We have revised the\nsentence in the manuscript. Page 10, lines 235-236.\nReviewer #3 (Remarks to the Author)\nThe study by Liu et al aims to characterise functional variants underpinning a schizophrenia\ngenome-wide significant common variant locus at 1p36.23, and provide insights into how these\nvariants impact schizophrenia pathophysiology. The paper presents a series of functional genomics\nexperiments that implicate two functional variants (rs159961 and rs301792) at this locus. TWAS,\neQTL and CRISPR/Cas9 knockout experiments suggest these variants are associated with increased\nexpression of RERE. Mouse neural stem cells (mNSCs) models suggest overexpression of RERE\nimpairs proliferation of NSCs and neurogenesis, and also abnormalities in dendrite complexity and\ndendritic spine density. A transcriptomic analysis of RERE overexpressing NSCs identified 213\ndifferentially expressed genes that were enriched for those with functions related to glutamatergic,\ncholinergic, and GABAergic synapse. Western blot and qPCR analyses also found RERE\noverexpression resulted in a decreased expression of the NMDAR subunit GRIN2A, which has been\nconsistently been implicated in schizophrenia by common and rare genetic studies.\nThe manuscript is well written, and although a large number of analyses and results are presented\nthe paper is easy to follow. The findings provide novel insights into the schizophrenia\npathophysiological mechanisms associated with common risk alleles at 1p36.23. Since my\nbackground is in psychiatric genetics, my review specifically focusses on the TWAS, eQTL and\ndifferential gene expression elements of the paper. I have the following comments regarding these\nanalyses.\nResponse: We sincerely appreciate the reviewer’s positive comments on our work. We also\ngreatly thank the reviewer for these insightful comments and suggestions. Our point-by-point\nresponses are provided below.\n1. The authors claim their findings support the neurodevelopmental hypothesis of schizophrenia,\nbut I did not see any data from the eQTL or TWAS analysis implicating REME in early neuronal\ndevelopment. The authors show in Supplementary figure 5 that REME is expressed prenatally, but\nare the eQTL/TWAS analyses based on postmortem adult brain tissue? Since published\nschizophrenia TWAS based on human fetal brain did not find any significant associations with REME\n(PMID: 32366953, Supplementary Table 2), could the authors please clarify whether their\neQTL/TWAS findings implicate specific development timepoints in schizophrenia, and if not\ncomment on the conflicting TWAS findings for REME reported in Hall et al 2021 (PMID: 32366953)\nResponse: We thank the reviewer for this insightful comment. In this study, the eQTL/TWAS\nanalyses were based on postmortem tissues from the adult brain. To specifically address the\nreviewer’s concern, we performed additional analyses using prenatal brain data.\nexpression in prenatal brain samples from Walker et al.(20). No significant eQTL associations were\ndetected (rs159961: beta = 0.044, P = 0.596, FDR = 1; rs301792: beta = 0.134, P = 0.117, FDR = 1).\nThe lack of significant findings may be explained by the small sample size in the prenatal dataset\n(N = 201) compared with postnatal datasets (N > 400)(21,22), which limits statistical power for eQTL\ndiscovery. Moreover, gene expression during early brain development undergoes greater\ndynamic changes and may be subject to more complex genetic regulatory mechanisms(13,20),\nfurther complicating the detection of robust eQTL effects between rs159961/rs301792 and RERE\nexpression.\nWe next attempted to perform TWAS analysis using prenatal brain data. Following the same\napproach as Hall et al.(23), we applied the FUSION pipeline to generate expression weights from\nfetal brain RNA-sequencing and genotype data to predict cis-regulated transcript levels. However,\nthe cis-heritability of RERE expression in the prenatal dataset was very low (h2=-0.002, P=0.498),\nwhich precluded the training of reliable TWAS weights. By contrast, the adult brain dataset\nexhibited sufficient heritability (h2 = 0.084, P = 1.925×10-3), thereby enabling robust TWAS\nmodeling.\nWe also carefully reviewed the study by Hall et al.(23). Their fetal brain TWAS was based on\n120 prenatal samples, in which they reported only 1,351 genes and 3,985 transcripts with\nsufficient heritability for TWAS. Importantly, RERE was not included in the gene-level dataset,\nand only a single RERE transcript was analyzed at the isoform level. The limited number of genes\nand transcripts identified in this study, coupled with the small sample size and low genetic\nheritability, may explain why RERE was not significantly associated with SCZ.\nIn summary, while current prenatal brain datasets are underpowered to reliably assess RERE\nand SCZ associations, our integrative analyses in adult brain tissues, together with findings from\nlarge-scale genetic studies(4,11) and our complementary wet-lab cellular experiments, support the\nhypothesis that RERE plays a role in neurodevelopmental processes relevant to SCZ. We have\nadded this clarification and discussion of the fetal TWAS study to the revised manuscript. Page\n8, lines 159-161; page 9, lines 211-212; page 10, lines 213; page 18, lines 456; page 19, lines 457-\n467.\n2. The transcriptomic analysis identified 213 differentially expressed genes in RERE overexpressed\nmNSCs, but I did not see outputs of these experiments presented anywhere other than in Figure 5b.\nIt would be helpful if the full results for each gene were presented as supplementary data, as it is\nnot easy to identify which specific genes are differentially expressed from the figure. For example, I\ncould not determine whether GRIN2A was differentially expressed from the figure. This comment is\nalso relevant for any other analyses in the paper where results are only presented in figures; the\nraw results should also be available in table format where applicable.\nResponse: We thank the reviewer for this valuable suggestion. We have provided supplementary\ndata to present the full results of differentially expressed genes in the revised manuscript.\nBesides, we performed fine-mapping and colocalization analyses and presented the results. We\nalso provided the complete TWAS results, the single-cell eQTL findings for rs159961 and rs301792,\nas well as the outcomes of haplotype analysis. Page 6, lines 109; page 8, lines 167; page 9, lines\n191; page 10, lines 214; page 12, lines 284; page 18, lines 429-430; Supplementary Tables 1-6;\n3. In the transcriptomic analysis, genes with adjusted P-value <0.05 were considered differentially\nexpressed genes. I could not see what method was used to adjust P values, could the authors please\nclarify?\nResponse: We thank the reviewer for this helpful suggestion. In differential gene expression\nanalysis, we applied the Benjamini-Hochberg (BH) method to adjust P-values for multiple testing,\nand we have incorporated detailed descriptions of this methodology in the revised manuscript.\nPage 31, lines 799-801.\n4. The discussion opens by stating the \"Neurodevelopmental hypothesis SCZ posits that SCZ is\ncaused by a combination of environmental and genetic factors”. I think this sentence should end by\nsaying that these factors impact brain development.\nResponse: We thank the reviewer for this valuable suggestion. According to the reviewer’s\nsuggestion, we have revised the text inthe manuscript accordingly. Page 17, lines 400-402.\n5. In the largest schizophrenia GWAS to date, RERE was prioritised through fine-mapping as the\ncredible causal gene underlying 1p36.23 (main text and supplementary Table 12 in Trubetskoy et al\n2022, PMID: 35396580). Given its relevance to the current study, I would suggest the authors cite\nthis finding in the introduction and discussion.\nResponse: We thank the reviewer for this helpful suggestion. We have now cited the\nrecommended reference (Trubetskoy et al., 2022) in the revised manuscript, which provides\nadditional support for our findings. Page 4, lines 77-79; page 17, lines 414-418.\nAgain, we greatly appreciate the reviewers for their insightful and valuable comments and\nsuggestions, which help us to improve the quality of the manuscript significantly. We hope our\nrevised work is satisfactory, and we are happy to further improve it if needed.\nselection in regression, with application to genetic fine mapping. J R Stat Soc Series B\nStat Methodol82, 1273-1300, doi:10.1111/rssb.12388 (2020).\nassociation meta-analyses. Nat Genet55, 1057-1065, doi:10.1038/s41588-023-01392-0\n(2023).\ngenome-wide association studies. Bioinformatics32, 1493-1501,\ndoi:10.1093/bioinformatics/btw018 (2016).\nof schizophrenia. Nat Hum Behav9, 609-624, doi:10.1038/s41562-024-02091-4 (2025).\nNat Genet54, 1355-1363, doi:10.1038/s41588-022-01154-4 (2022).\nassociation studies using summary statistics. PLoS Genet10, e1004383,\ndoi:10.1371/journal.pgen.1004383 (2014).\ndisease brain. Nat Genet56, 605-614, doi:10.1038/s41588-024-01685-y (2024).\nrisk genes for psychiatric and neurological disorders. Nat Neurosci25, 1104-1112,\ndoi:10.1038/s41593-022-01128-z (2022).\nmouse brain. Nature624, 317-332, doi:10.1038/s41586-023-06812-z (2023).\nschizophrenia, and bipolar disorder. Science362, doi:10.1126/science.aat8127 (2018).\nschizophrenia. Nature604, 502-508, doi:10.1038/s41586-022-04434-5 (2022).\nschizophrenia. Nat Neurosci19, 1442-1453, doi:10.1038/nn.4399 (2016).\ntranscriptome illuminate schizophrenia pathogenesis. Nat Neurosci21, 1117-1125,\ndoi:10.1038/s41593-018-0197-y (2018).\nCoherence in the Frontal Cortex and Hippocampus across Development and\nSchizophrenia. Neuron103, 203-216 e208, doi:10.1016/j.neuron.2019.05.013 (2019).\npanel. Nat Genet48, 1443-1448, doi:10.1038/ng.3679 (2016).\nbiology. Genet Med20, 1022-1029, doi:10.1038/gim.2017.233 (2018).\n108 schizophrenia-associated genetic loci. Nature511, 421-427,\ndoi:10.1038/nature13595 (2014).\nschizophrenia. Nat Genet49, 1576-1583, doi:10.1038/ng.3973 (2017).\ngenes and in regions under strong background selection. Nat Genet50, 381-389,\ndoi:10.1038/s41588-018-0059-2 (2018).\nBrain Informs Disease Mechanisms. Cell179, 750-771 e722,\ndoi:10.1016/j.cell.2019.09.021 (2019).\nWinner's Curse in eQTL studies. Nucleic Acids Res46, e133, doi:10.1093/nar/gky780\n(2018).\ncausal variants for brain-related traits. Nat Genet54, 161-169, doi:10.1038/s41588-021-\n00987-9 (2022).\nwith genetic risk for neuropsychiatric disorders. Mol Psychiatry26, 2082-2088,\ndoi:10.1038/s41380-020-0743-3 (2021).\n15"}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have clearly invested a significant amount of time and effort to thoroughly address my concerns, demonstrating careful consideration and substantial additions/revisions throughout the paper.", "reviewer_3": "The authors have adequately addressed my comments in the revised manuscript, and I have no further comments."}, "author_response": "Response to reviewers\nReviewers' comments:\nReviewer #2 (Remarks to the Author):\nThe authors have clearly invested a significant amount of time and effort to thoroughly address\nmy concerns, demonstrating careful consideration and substantial additions/revisions throughout\nthe paper.\nResponse: We greatly appreciate the reviewer’s comments and are glad that the revisions have\naddressed your concerns.\nReviewer #3 (Remarks to the Author):\nThe authors have adequately addressed my comments in the revised manuscript, and I have no\nfurther comments.\nResponse: We sincerely thank the reviewer for the positive feedback and for confirming that our\nrevisions have adequately addressed all comments.\nReviewer #2 comments on Reviewer #1 (Remarks to the Author):\nReviewer #1 comment (1) Fine Mapping needs to be conducted / reported for the locus being\ninvestigated. The LD block looks large and the implication from the TFBS analyses is that there are\nmultiple causal variants in the locus. What is the estimated number of independent causal signals\nat this GWAS locus from fine mapping? What are the credible set sizes? Most importantly, what\nare the posterior inclusion probabilities for the two prioritized variants?\nReviewer #2 comments on response to Reviewer #1 comment (1): The authors implemented three\ndifferent strategies for assessing posterior probability of their SNPs for fine mapping. The identified\na 92% credible set of genes comprised of 50 variants that included rs301792 and rs159961.\nTherefore, the original reviewer’s assertion of multiple causal variants in the locus is possible and\nin fact highly likely. In addition, each SNP individually had relatively low PIP and would not be\nconsidered significant. The authors do not report these specific PIPs in the text, but it is present in\nthe supplemental data added. The authors should consider reporting the individual PIPs and at the\nvery least discuss the possibility of additional variants in the discussion.\nResponse:We appreciate the reviewer for this valuable suggestion. We acknowledge that, based\nsolely on the fine-mapping results, multiple potential causal variants may exist within this locus.\nMoreover, due to the relatively low posterior inclusion probabilities (PIP) observed at this locus, it\nis difficult to establish robust causal associations for the candidate variants. To clarify this point,\nwe have now reported in the revised manuscript the full fine-mapping results for this locus,\nincluding the credible set sizes, and the posterior inclusion probabilities of the two candidate\nfunctional variants identified through our functional genomics analyses. Page 6, lines 103-114.\nIndeed, these findings underscore the general limitation of relying purely on statistical fine-\nmapping to further prioritize genetic association signals identified by GWAS and to pinpoint the\ntruly functional variants within a locus. As demonstrated by Massively Parallel Reporter Assays\n(MPRA) studies2, statistical fine-mapping alone cannot effectively discriminate between MPRA-\npositive and MPRA-negative variants, and the fine-mapping posterior inclusion probabilities do\nnot significantly differ between these two groups. These findings indicate that PIP is not directly\nrelated to functionality. In contrast, allele-specific regulatory activity is largely driven by\ntranscription factors (TFs), and MPRA-positive variants are significantly enriched within TF\nregulatory networks. In another MPRA study1, the authors compared MPRA results with statistical\nfine-mapping algorithms (FINEMAP, SuSiE, and CAVIAR) and found that statistical fine-mapping\nperforms effectively for loci with small credible sets and simple linkage disequilibrium (LD)\npatterns, but may be less effective for loci characterized by complex LD structures and larger\ncredible sets. These findings further highlight the strength of our functional genomics approach\n(based on TF binding-affecting analyses), which enables the identification of schizophrenia-\nassociated functional variants with relatively low fine-mapping posterior inclusion probabilities\n(PIP < 0.1).\nIn addition, a series of cellular and molecular experiments presented in the main text provide\nstrong evidence that the candidate variants we identified (rs301792 and rs159961) are likely to\nrepresent causal functional variants within this locus contributing to the pathogenesis of\nschizophrenia (SCZ). Nevertheless, we recognize that, given the current scope of our analytical and\nexperimental throughput, we were unable to thoroughly investigate all potential variants within\nthis locus. Therefore, we cannot exclude the possibility that other functional causal variants\nassociated with SCZ may also exist in this region. To avoid overstatement of our findings, we have\nexplicitly acknowledged this point in the revised Discussion section as a study limitation. Page 20,\nlines 509-520; page 21, lines 521-531\nReviewer #1 comment (2) TWAS / Colocalization. Along the lines of #1 above, the simple overlap\nof a GWAS significant SNP with an eQTL is not sufficient to show that the two signals reflect the\nsame underlying causal variant. This is especially true when LD blocks are large. What is the\ncolocalization posterior probability of the GWAS locus with the eQTLs for RERE ? TWAS can begin\nto address this but is a test of association\nand also often prioritizes multiple genes in large LD blocks. Fine mapping of TWAS results or\nconditional analyses would be more convincing.\nReviewer #2 comments on response to Reviewer #1 comment (2): The authors also attempt to\nvalidate the SNPs using TWAS. Again, they found low colocalization of the SNPs with eQTLs but\nreport the high colocalization posterior probability with the locus and eQTLs. While this is an\nimprovement from their previous analysis it does support the new finemap data that there are\nadditional SNPs in the locus that should be examined in future studies.\nResponse: We thank the reviewer for this insightful comment. Consistent with our response to\nComment (1), we acknowledge that additional candidate functional causal variants for SCZ may\nexist within this locus, and that these additional SNPs warrant further in-depth investigation in\nfuture studies. Nevertheless, based on the current body of evidence, including dual-luciferase\nreporter gene assays, electrophoretic mobility shift assays (EMSA), and CRISPR/Cas9-mediated\ngenome editing experiments, we have provided strong and convergent support for the potential\nfunctional consequences of the two candidate variants we identified in relation to SCZ. Therefore,\nthis limitation does not compromise the overall contribution of our study in elucidating the role of\nthe 1p36.23 genomic region in schizophrenia. To clarify this point, we have now explicitly discussed\nthis limitation in the revised manuscript. Page 8, lines 171-173; page 20, lines 509-520; page 21,\nlines 521-531.\nReviewer #2 comments on response to Reviewer #1: This reviewer feels that the authors should\nmake it clearer that there may be additional SNPs that warrant further examination in the future\nbut that the functional genomic data do make a convincing case for the roles of these particular\nSNPs in SCZ even if they are not the only ones in the locus.\nResponse:We thank the reviewer for the positive evaluation of our revised work. As noted in our\nresponses to the previous two comments, we acknowledge that there may be additional SNPs that\nwarrant further investigation in future studies, and we have added a discussion of this limitation\nin the revised manuscript.\nReviewer #3 comments on Reviewer #1 (Remarks to the Author):\nI’ve now reviewed the response to reviewer 1, focussing on points 1 and 2 made by the reviewer.\nThe finemapping and colocalisation analyses have conflicting evidence that do not always support\nthe authors main conclusions, and I believe the authors have been too selective in what findings\nare described in the main text. I do not believe the results from these analyses refute the main\nconclusions of the paper, but I do think they need to be more clearly described and discussed in the\nmanuscript. I provide further context and suggestions for revision below.\nResponse: We thank the reviewer for the overall positive evaluation on our revised manuscript.\nWe would like to clarify that the results of the fine-mapping and colocalization analyses are not\ncontradictory. In fact, the colocalization analysis indicated a significant overlap between the GWAS\nsignal and the risk genomic region containing RERE. However, when examined at the level of\nindividual variants, neither of the candidate functional variants we identified rs301792 and\nrs159961 achieved high posterior inclusion probabilities (Fine-mapping PIP < 0.7; coloc PIP < 0.7).\nWe have now provided the specific posterior inclusion probabilities in the revised manuscript to\nenhance the transparency of our results.\nFurthermore, we did not selectively present our results. In the previously revised manuscript,\nwe have provided the full fine-mapping and colocalization results in the Supplementary Materials\n(Supplementary Table 1-3, Supplementary Data 1, 2). As the main conclusions of our study are\nderived from the identification of candidate functional variants affecting transcription factor\nbinding, integrated with comprehensive functional fine-mapping approaches (including dual-\nluciferase reporter gene assays, EMSA, and CRISPR/Cas9-mediated genome editing experiments),\nrather than relying solely on statistical fine-mapping (fine-mapping and colocalization)\nposterior\nprobabilities for variant prioritization, these findings do not contradict the main conclusions of our\nstudy. However, to avoid overstatement and potential misinterpretation, we have now clarified\nand discussed these results more explicitly in the revised manuscript. Page 6, lines 103-114; page\n8, lines 171-173; page 20, lines 509-520; page 21, lines 521-531.\nReviewer 1 asked for fine-mapping to be conducted for the 1p36.23 locus, to provide additional\nevidence for the credible causal effects of rs301792 and rs15996. The authors now present results\nfrom three fine-mapping approaches (SuSie, CARMA and FINEMAP) in the revised manuscript.\nThe authors show these variants are within the 92-99% credible sets, however, the posterior\nprobabilities for the variants of interest are in fact very low (< 0.0073), despite the authors claiming\nthey are modest. Given this conflicting evidence, I do not fully agree with the authors interpretation\nthat the fine-mapping results further support rs159961 and rs301792 as potential functional\nvariants. Fine-mapping is based on statistical inference, and therefore the low posterior\nprobabilities observed for rs159961 and rs301792 do not fully refute the results from the functional\ngenomics experiments supporting rs159961 and rs301792 as functional variants. However, I think\nthe authors should be more upfront in the revised manuscript about the lack of evidence from fine-\nmapping. For example, I don’t believe the claim on line 107 that the variants have ‘modest posterior\nprobabilities for causality’ is justified. Could the authors consider presenting the actual posterior\nprobabilities in the main text, as they do in the response letter, and more importantly add text to\nthe discussion describing the finemapping results and interpreting the findings in the context of the\nfunctional data presented?\nResponse: We thank the reviewer for this objective comment on our manuscript. We apologize\nfor our previous statement referring to “modest posterior probabilities for causality” as we\nacknowledge that this description was somewhat overstated. To avoid any potential\nmisunderstanding, we have now adopted a more objective phrasing in the revised manuscript and\nhave presented the actual posterior probabilities in the main text. These data indicate that the\ntwo TF binding-affecting candidate functional causal variants we identified are not supported by\nfine-mapping evidence.\nIn addition, we have expanded our discussion of the fine-mapping results in the revised\nmanuscript and compared them with findings from published MPRA studies (see also our response\nto Reviewer #2, Comment 1). Large-scale MPRA analyses have demonstrated that statistical fine-\nmapping alone cannot effectively distinguish MPRA-positive (i.e., functional) from MPRA-negative\n(non-funcitonal) variants, and that allele-specific regulatory activity is primarily driven by\ntranscription factors (TFs). This comparison underscores the limitations of relying solely on\nstatistical fine-mapping analyses and further highlights the importance of the functional data\npresented in our manuscript. Moreover, as we were unable to experimentally test all potential\ncausal variants and therefore focused on the two most plausible functional causal variants we\nidentified. We have also acknowledged in the revised Discussion that we cannot exclude the\npossibility that other schizophrenia-associated functional causal variants may exist within this\ngenomic region. Page 6, lines 103-114; page 20, lines 509-520; page 21, lines 521-531.\nSimilarly, in the response to point 2 by reviewer 1, the authors have been selective in what\ncolocalization data is presented in the main text to support the GWAS causal variant mapping to\nan eQTL. For example, in the response letter the authors indicate that the colocalization posterior\nprobability for SNP rs159961 and rs301792 is low, but this is not described in the main text. As\nabove, I would recommend the authors clearly present this data in the main text and interpret the\nfinding in the discussion.\nResponse: We thank the reviewer for this valuable suggestion. To enhance the transparency of\nour results presentation, we have now included in the revised main text the colocalization\nposterior probabilities for SNPs rs159961 and rs301792. In addition, considering the comments\nfrom Reviewer #2, we have provided a more detailed explanation of why the findings from\nstatistical fine-mapping (fine-mapping and colocalization) analyses are not contradictory to the\nmain conclusions of our study. Furthermore, to avoid overstatement and potential\nmisinterpretation, we have now clarified and discussed these results more explicitly in the revised\nmanuscript. Page 8, lines 171-173; page 20, lines 509-520; page 21, lines 521-531.\nReferences\npsychiatric disorders. Cell188, 1409-1424 e1421 (2025).\nassociated common variants. Cell Genom3, 100404 (2023)."}, {"round": 3, "reviewer_comments": {"reviewer_2": "1. The authors have toned down the overall claims of their PIP findings and report all of the analysis in the main paper.\n2. The authors also specifically state the TF binding is not supported by fine mapping.\n3. The overall discussion also better describes these findings.", "reviewer_3": "The authors have adequately addressed all remaining comments I had. I have no other concerns or comments on the work which I recommend for publication."}, "author_response": "Response: We greatly appreciate the reviewers for their insightful and valuable comments and\nsuggestions, which help us to improve the quality of the manuscript significantly. We hope our\nrevised work is satisfactory, and we are happy to further improve it if needed."}]

Decision letter