Allele-Specific Methylation Links Non-Coding Variant of rs2280906 to MYOM2 Regulation in Schizophrenia

Allele-Specific Methylation Links Non-Coding Variant of rs2280906 to MYOM2 Regulation in Schizophrenia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Allele-Specific Methylation Links Non-Coding Variant of rs2280906 to MYOM2 Regulation in Schizophrenia Qiyang Li, Yuanyuan Gai, Zhongwei Li, Zhongju Wang, Xingjian Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6970984/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Molecular Neurobiology → Version 1 posted 9 You are reading this latest preprint version Abstract Schizophrenia (SCZ) is a complex polygenic disorder influenced by genetic, epigenetic, and environmental factors. While numerous risk loci disease-associated methylation variants have been identified, their functional impact and contribution to disease risk remain largely unclear. This study addresses a fundamental yet underexplored question: how do non-coding allele-specific methylation (ASM) sites influence disease risk via gene regulation? We employed the Mendelian Randomization (MR) method to integrate ASM data from monozygotic twins discordant for psychiatric disorders with brain eQTL and GWAS summary statistics to identify potential risk genes. The regulatory of the rs2280906 locus was investigated using dual-luciferase reporter assays, gene expression quantification, gene editing, methylation editing, and electrophoretic mobility shift assays. We used MR to prioritize these ASM locus associated with schizophrenia risk and demonstrated that the affected genes are enriched in energy metabolism pathways—suggesting that targeting energy dysregulation may represent a promising therapeutic avenue. We further elucidated the allele-specific, methylation-dependent mechanism by which ASM site rs2280906 regulates risk gene MYOM2 . In healthy individuals, hypomethylation of the reference C allele permits MYOM2 expression. In contrast, affected individuals exhibit hypermethylation of this allele, leading to biallelic methylation, increased recruitment of repressive transcription factors, and MYOM2 downregulation. Our study uncovers new risk genes regulated by ASM and provide mechanistic insight into the rs2280906– MYOM2 axis in schizophrenia. Our work advances understanding of how epigenetic regulation contributes to disease susceptibility and inter-individual variability, and offers new avenues for the identification of causal variants and therapeutic targets. allele-specific methylation psychiatric disorders Mendelian randomization phenotypic variations epigenetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Schizophrenia (SCZ) is a complex polygenic disorder shaped by genetic, epigenetic, and environmental factors. Major psychiatric disorders, including schizophrenia (SCZ) and bipolar disorder (BPD), are highly complex polygenetic mental diseases[ 1 , 2 ]. Their complexity arises from pronounced phenotypic heterogeneity and the intricate interplay among genetic, developmental, and environmental factors. Recent genome-wide association studies (GWAS) have identified numerous risk loci associated with these disorders, providing valuable insights into their genetic architecture and underlying biological mechanisms[ 3 – 10 ]. However, these loci explain less than a quarter of the observed phenotypic variance, highlighting the important role of non-genetic contributors, particularly epigenetic modifications, in disease etiology[ 11 – 13 ]. Among epigenetic modifications, DNA methylation play a key role in mediating the interaction between genetic predisposition and environmental influences[ 14 – 16 ]. DNA methylation regulates gene expression by modulating transcription factor (TF) binding and recruiting histone modification complexes. Aberrant DNA methylation patterns have been implicated in the pathophysiology of complex diseases, including SCZ and BPD[ 17 – 19 ]. Traditionally, DNA methylation was assumed to occur symmetrically across both allele of a gene. However, recent studies have uncovered widespread allele-specific methylation (ASM) sites, in which one allele is highly methylated while the other remains lowly methylated or unmethylated[ 20 , 21 ]. ASM sites are responsive to environmental cues and can function as epigenetic switches that regulate gene expression dosage through alterations in TF binding and chromatin structure[ 22 – 24 ]. Dysregulation of these ASM-mediated methylation switches can lead to aberrant expression of neuronal genes, disrupt neurodevelopment and processes, and modulate individual responses to environmental stressors, thereby contributing to the onset and progression of psychiatric disorders[ 20 , 24 , 25 ]. Importantly, ASM provides a plausible mechanism by which non-coding variants identified in GWAS influence disease risk via gene–environment interactions. In our previous work, we employed methylated DNA immunoprecipitation sequencing (MeDIP-seq) and whole-genome sequencing (WGS) in monozygotic (MZ) twin pairs discordant for psychiatric disorders to identify numerous psychiatry-associated ASM (psyASM) sites [ 2 ], implicating them in psychopathology. Similar to many GWAS-identified single nucleotide polymorphisms (SNPs), these ASM sites are predominantly located in non-coding regions with complex regulatory functions[ 26 , 27 , 12 ]. However, the specific mechanisms through which they contribute to psychiatric disorders, particularly by regulation of gene expression, remain largely unexplored. To address this, we leveraged Mendelian randomization (MR), a statistical approach that infers causal relationships between risk factors and disease traits. MR has proven effective in the post-GWAS era for integrating genetic association data with multi-omics layers, such as transcriptomics and proteomics, to uncover the target genes underlying non-coding risk loci[ 12 , 26 , 28 ]. This integrative approach holds promise for identifying the genes regulated by psyASM sites in psychiatric disorders. In this study, we integrated psyASM data, expression quantitative trait loci (eQTL), and GWAS data using MR to identify a set of risk genes regulated by ASM loci in psychiatric disorders. Our findings revealed that these genes are primarily involved in biological pathways such as energy metabolism. Through functional validation, we further demonstrated that the psyASM locus rs2280906 modulates the expression of the SCZ risk gene MYOM2 ( Myomesin 2 ). Methylation changes at this locus influence TF binding and regional methylation architecture. Our results provide new insights into the causal roles of psyASM loci in SCZ and BPD, uncover the epigenetic mechanisms of the rs2280906- MYOM2 axis, and offer potential implications for understanding disease etiology and developing target interventions. Method GWAS summary statistics of SCZ GWAS summary statistics for SCZ were obtained from the large-scale meta-analysis conducted by Vassily Trubetskoy and colleagues on behalf of the Psychiatric Genomics Consortium (PGC) in 2022 [ 29 ]. This study included 76,755 schizophrenia cases and 243,649 controls, primarily of East Asian and European ancestry. The GWAS summary data used in this analysis were downloaded from the official PGC website ( https://pgc.unc.edu/ ). The large sample size substantially increased statistical power, enabling the detection of genetic variants with modest effect sizes and enhancing the resolution of association signals relevant to disease etiology. eQTL of Brain tissue In this study, expression quantitative trait loci (eQTLs) were used as instrumental variables to infer regulatory relationships between genetic variants and gene expression. eQTL data were obtained from the Genotype-Tissue Expression (GTEx) Project ( https://gtexportal.org/ )[ 30 ], which includes 15,201 ribonucleic acid-sequencing (RNA-seq) samples from 49 different tissues collected from 838 postmortem donors. Specifically, we extracted cis-eQTLs from brain regions relevant to psychiatric disorders, including the Hippocampus, Frontal Cortex, and Cortex, using GTEx version 8. Mendelian Randomization to Identify ASM-Regulated Risk Genes To identify putative risk genes regulated by allele-specific methylation (ASM) loci, we employed the Summary-data-based Mendelian Randomization (SMR) method[ 31 ]. SMR enables the estimation and testing of pleiotropic associations between molecular traits and disease phenotypes by integrating quantitative trait loci (QTL) and GWAS summary statistics, facilitating the inference of potential causal genes. ASM loci identified in previous work[ 2 ] were annotated to their corresponding or nearest genes. For each ASM-gene pair, the relevant cis-eQTL subsets were extracted using the --extract-probe parameter in SMR. Linkage disequilibrium (LD) and heterogeneity in dependent instruments (HEIDI) tests were conducted using PLINK binary format genotype reference data from the 1000 Genomes Project Phase 3. PLINK is an open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. HEIDI tests help differentiate true pleiotropic effects from associations due to linkage; loci with a P HEIDI < 0.05 were considered heterogeneous and excluded from further analysis. The HEIDI test was applied using the top 20 cis-eQTL SNPs within a 2,000 Kb window around the gene of interest. SNPs showing P SMR < 0.05 were considered nominally significant, and the corresponding genes were regarded as potential ASM-regulated schizophrenia risk genes. All MR analyses, including SMR and HEIDI testing, were performed using SMR software (version 1.3.1; https://yanglab.westlake.edu.cn/software/smr/#Overview ) via command line [ 31 ]. To explore biological relevance, we conducted Gene Ontology (GO) enrichment analysis of ASM-prioritized genes using the ToppGene Suite (WebGestalt: http://www.webgestalt.org/option.php ), and constructed protein–protein interaction (PPI) networks using GeneMANIA ( https://genemania.org/ ). Chromatin interaction data were visualized using circular chromosome capture (4C) plots via the Genome Browser ( https://3dgenome.fsm.northwestern.edu/ ). Functional validation and phenotypic relevance were further assessed using gene–phenotype associations from the International Mouse Phenotyping Consortium (IMPC) [ 32 ]. Cell Culture HEK293T and SK-N-SH cells were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM; Gibco) supplemented with 10% fetal bovine serum (FBS; ExCell Bio). Cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂. Luciferase Reporter Assay To evaluate the regulatory potential of ASM loci, DNA fragments (~ 500 bp) flanking each candidate ASM site were PCR-amplified and cloned into the pGL4.23 [luc2/minP] vector (Promega), which contains a minimal TATA-box promoter upstream of the luciferase gene ( Supplementary Table 1 ). Site-directed mutagenesis was used to introduce the alternative alleles of the ASM site and/or tightly linked SNPs (in perfect linkage disequilibrium), replacing the reference alleles. Constructs were transiently co-transfected into HEK293T or SK-N-SH cells along with a Renilla luciferase control plasmid (pRL-TK) using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions. After 48 hours, firefly and Renilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega) on a Wallac Victor V1420 Multilabel Counter (PerkinElmer). Firefly luciferase activity was normalized to Renilla luciferase activity to control for transfection efficiency. Lentivirus Plasmid Construction, Packaging, and Transduction To investigate the role of the ASM locus rs2280906, we employed Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 (CRISPR/Cas9) technology to disrupt this genomic site. A single-guide RNA (sgRNA) targeting the rs2280906 locus was designed using resources from the Zhang Laboratory ( https://zlab.bio/guide-design-resources ). The sgRNA oligonucleotides were synthesized by Invitrogen and subsequently cloned into the lentiCRISPRv2 vector (Addgene #52961) using BsmBI digestion. Lentiviral particles were packaged by co-transfecting the lentiCRISPRv2-sgRNA plasmid with packaging plasmids into HEK293T cells. The viral supernatant was collected 48 hours post-transfection and used to transduce SK-N-SH cells via the calcium phosphate co-precipitation method[ 33 ]. Transduced cells were incubated for 48–72 hours, after which GFP-positive (GFP+) cells were identified by fluorescence microscopy. Stable cell lines were selected by applying 2 µg/ml puromycin for 7–10 days. The impact of functional SNP disruption and knockout of the rs2280906 locus on MYOM2 expression was assessed by quantitative real-time PCR (qRT-PCR) and Western blotting. Primary antibodies used include rabbit anti-MYOM2 (1:3,000, Proteintech 32262-1-AP) and rabbit anti-β-actin (1:1,000, Proteintech 20536-1-AP) as a loading control. EMSA, ChIP, Bisulfite Conversion and PCR Nuclear protein was extracted from HEK293T cells for evaluating the binding affinity of allelic variations and methylation changes at the functional loci rs2280908-rs2280909 using an Electrophoretic Mobility Shift Assay (EMSA). The Chemiluminescent Nucleic Acid Detection Module Kit (Thermo Fisher) was used to detect biotin-labeled DNA-protein complexes. The assay was visualized and quantified using chemiluminescence imaging on a Tanon 5200 system. For bisulfite conversion, genomic DNA was extracted from HEK293T cells and modified using the EpiArt™ DNA Methylation Bisulfite Kit (Vazyme) according to the manufacturer's instructions. Bisulfite-converted DNA was amplified using BS-PCR primers specific to the target region. To assess methylation status, PCR products were cloned into the pMD19-T cloning vector (Takara, China). For each construct, 10–12 individual clones were sequenced to determine the methylation level of the target CpG sites. Results Integrative Analysis of ASM Loci Reveals Risk Genes in Psychiatric Disorders In our previous study, we performed MeDIP-seq and WGS on peripheral blood DNA from 17 pairs of MZ twins[ 2 ]. These included 9 pairs discordant for psychiatric disorders (PDC; 4 with SCZ-discordant pairs [SDC] and 5 with BPD-discordant pairs [BDC]), 4 concordant pairs for psychiatric disorders (PCC; 1 SCZ-concordant pair [SCC] and 3 BPD-concordant pairs [BCC]), and 4 healthy control-concordant pairs (HCC). From this analysis, we identified 220,520 ASM sites, of which 21,166 were significantly associated with psychiatric disorders. Notably, the majority of these ASM sites were in located in non-coding regions, such as intergenic (8,969) and intronic (8,420) regions (Fig. 1 A). To identify potential disease-associated genes regulated by these ASM sites, we performed MR analysis by integrating ASM data with brain eQTL and PGC3 SCZ-GWAS datasets ( Fig. 1 B ) . This integrative approach revealed 7 disease-related ASM loci corresponding to 4 risk genes: SLC2A6 , SLC25A12 , MYOM2 , and FHIT (Table 1 ). SLC2A6 encodes solute carrier family 2 member 6 involved in neuronal signaling and synaptic function and may influence neuronal excitability and synaptic transmission[ 34 ]. SLC25A12 encodes the mitochondrial aspartate/glutamate carrier (AGC1), a key component of the malate/aspartate shuttle that supports mitochondrial energy metabolism in neurons[ 35 ]. Notably, SLC25A12 is upregulated in postmortem brain tissue of individuals with autistic, particularly in the prefrontal cortex[ 36 ], where its overexpression may disrupt neuronal network formation, suggesting a role in neurodevelopmental disorders. MYOM2 , a gene associated with cytoskeletal integrity, may influence disease through effects on neuronal morphology and function[ 37 ]. FHIT , primarily known for its role in apoptosis, also contribute to neuropathic pain via its influence on NK1R hyperexcitability mediated by GABAergic neurons[ 38 ]. Table 1 Mendelian Randomization identifies ASM sites regulating disease risk gene sets. Risk Gene ASM Site ASM Site Position Ref Allele Alt Allele Location BF P GWAS P eQTL TopSNP Position P SMR SLC2A6 rs587600128 chr9:136338820 C G intronic 249 1.12E-04 2.47E-06 chr9:136300204: C:G 2.87E-03 SLC25A12 rs145770325 chr2:172731685 C T intronic 47 1.26E-04 6.75E-06 chr2:172901330: T:C 3.56E-03 8.76E-04 6.39E-07 chr2:172873060: G:A 5.82E-03 2.61E-03 1.27E-08 chr2:172817794: G:A 7.59E-03 MYOM2 rs117109793 chr8:2055127 G C intronic 36 9.58E-03 1.16E-11 chr8:2102543: T:G 1.52E-02 rs2280906 chr8:2091232 A G intronic 11 1.39E-02 1.39E-02 chr8:2077072: A:G 2.88E-02 rs66538145 chr8:2099645 T G intergenic 22 1.39E-02 1.39E-02 chr8:2077072: A:G 2.88E-02 FHIT rs55646807 chr3:61536565 T A intergenic 17 1.06E-02 1.87E-04 chr3:60809239: C:T 3.56E-02 rs58580279 chr3:61536564 A G intergenic 15 1.06E-02 1.87E-04 chr3:60809239: C:T 3.56E-02 Risk gene indicates genes located nearest to the identified significant ASM Site; ASM site indicates Allele-specific methylation site; BF indicates Bayes Factors which are derived from the Bayesian generalized additive linear mixed model. GWAS indicates genome-wide association study. eQTL indicates expression quantitative trait loci. TopSNP indicates the single nucleotide polymorphism with the smallest P-value in a genomic region in GWAS. SMR indicates Summary-data-based Mendelian Randomization. Altered methylation at these ASM loci may regulate the expression of these genes, and their dysregulation could contribute causally to the development of psychiatric disorders. Despite identifying a limited number of risk genes, functional enrichment analysis revealed significant involvement in energy metabolism pathways, particularly purine nucleotide metabolism (Fig. 1 C). Furthermore, protein-protein interaction (PPI) network analysis indicated that these genes and their interacting proteins are involved in biological processes such as carbohydrate biosynthesis, hexose biosynthesis, and the respiratory electron transport chain (Fig. 1 D). The 4 risk genes were located at core nodes, with SLC25A12 at a key node, implicated in functions enriched in those energy-metabolism pathways (Fig. 1 E). These findings highlight the potential of targeting energy metabolism as therapeutic strategy for psychiatric disorders. Functional Dissection and Regulatory Mechanism of the ASM Locus rs2280906 Among the seven disease risk loci identified in our study, all seven contained ASM sites whose aberrant methylation shifting patterns were implicated in disease pathogenesis. Notably, five of these ASM loci–rs145770325, rs117109793, rs2280906, rs55646807, and rs58580279–exhibited a methylation shift from reference-allele hypermethylation in the unaffected individuals to biallelic methylation in the affected individuals ( Supplementary Fig. 1 ). To validate the regulatory potential of these causal ASM loci identified through MR approach, we conducted dual-luciferase reporter assays. Approximately 300 base pairs of genomic sequence flanking each ASM locus was cloned upstream of a minimal promoter in the pGL4.23 luciferase reporter vector (Fig. 2 A). Due to their proximity-rs55646807 and rs58580279 are located at chr3:61536565 and chr3:61536564, respectively།both were inserted into the same reporter construct. The reporter vectors were transfected into HEK293T (Fig. 2 B) and SK-N-SH (Fig. 2 C) cell lines to assess allele-specific promoter activity. Among the loci tested, rs2280906 exhibited the most significant difference in luciferase activity between its two alleles, indicating a strong regulatory effect on gene expression and highlighting its potential functional relevance in disease mechanisms. Given the presence of numerous loci in strong linkage disequilibrium (LD) near ASM sites, which can obscure the identification of the true functional variant, we investigated potential regulatory sites surrounding rs2280906 using the HalopReg database[ 39 ]. This analysis revealed that rs2280906 is in perfect LD (LD = 1) with 10 linked loci ( Supplementary Table 1 ), underscoring its potential functional importance. To pinpoint the functional site, we divided the 10 linked loci into four genomic regions and generated dual-luciferase reporter constructs carrying revertant mutations for each region (Fig. 3 A). Upon transfection into HEK293T and SK-N-SH cell lines, all four regions showed varying levels of regulatory activity, with Regions 3 and 4 displaying the most pronounced effects (Fig. 3 B), suggesting a complex regulatory landscape centered around the rs2280906 locus. Focusing on Region 3, which includes rs2280907, rs2280908, and rs2280909, we constructed individual revertant mutation vectors (Fig. 3 C) and assessed their activity in both HEK293T and SK-N-SH cells. Among these, the revertant mutation of rs2280909 demonstrated the strongest translational repression (Fig. 3 D). Since rs2280908 and rs2280909 reside within the same regulatory motif, we additionally tested a construct with combined revertant mutations. This dual mutation led to the most significant translational repression, identifying rs2280908 and rs2280909 as key functional sites. To further investigate the regulatory mechanism of this ASM locus, we conducted electrophoretic mobility shift assays (EMSAs) using a hot probe containing the Ref C allele at both rs2280908(C/T) and rs2280909(C/T) and a cold probe containing the Ref C allele or the Alt T allele. These assays demonstrated that the Alt T allele of the motif bound TFs more strongly than the Ref C allele (Fig. 4 A, Supplementary Fig. 2A ). Moreover, competition experiments showed that methylation of the Ref C allele (5mC) further enhanced TF binding (Fig. 4 B, Supplementary Fig. 2B ). When compared directly, the Alt T allele exhibited even stronger binding affinity than the methylated Ref C allele (5mC) (Fig. 4 C, Supplementary Fig. 2C ), supporting the functional significance of allele-specific binding affinity and methylation status. We used position weight matrix to predict transcription factors that may bind to the rs2280908 and rs2280909 loci (Fig. 4 D, Supplementary Table 2 ) and found that the NFY transcription factor may bind to this loci. We further validated the binding of NFY to these loci through an EMSA super-shift assay (Fig. 4 E, Supplementary Fig. 2D ). Bisulfite PCR (BS-PCR) confirmed that rs2280908 and rs2280909, adjacent to rs2280906, were highly methylated in their native genomic context (Fig. 4 F-I). To determine whether these sites influence regional methylation, we used CRISPR/Cas9 to delete rs2280908 and rs2280909 in the HEK293T genome (Fig. 4 F). This deletion led to complete demethylation of the surrounding region (Fig. 4 G-I), suggesting that these functional sites recruit DNA methyltransferases to establish or maintain local methylation. Regulatory Role of rs2280906 in Modulating MYOM2 Expression The disease-associated ASM locus rs2280906, located in intron 36 of the MYOM2 gene (~ 98 kb upstream of the promoter), was evaluated for long-range regulatory activity using circular chromosome capture (4C) data using the 3D Genome Browser. These data revealed prominent chromatin interaction peaks between rs2280906 and the MYOM2 promoter (Fig. 5 A), suggesting spatial proximity and potential regulatory influence. To confirm this hypothesis, we delete a ~ 500 bp region surrounding rs2280906 using CRISPR/Cas9 in HEK293T cells (Fig. 5 B & C, Supplementary Fig. 2E ). This deletion led to a tenfold reduction in MYOM2 RNA (Fig. 5 D) and protein (Fig. 5 E, Supplementary Fig. 2F ) levels, supporting a long-range enhancer-like function of rs2280906. To verify the regulatory relationship between the methylation level of this region and MYOM2 expression, we used a de-methylation - editing technique to demethylate this region (Fig. 5 F). Although the methylation level decreased slightly (Fig. 5 G), MYOM2 expression increased significantly after de-methylation (Fig. 5 H), indicating that the demethylation of this region can enhance the transcriptional level of MYOM2 . The Myom2 knockout mice are reduced in forelimb and hindlimb grip strength from the public IMPC database (Fig. 5 I)[ 32 ].Transcriptomic analysis of SCZ case–control samples from the PsychENCODE brain RNA-seq dataset[ 40 ] revealed significant reduced differential transcript usage (DTU) of MYOM2 in patients (Fig. 5 J), further implicating the rs2280906– MYOM2 regulatory axis in disease pathogenesis. Mechanistically, the Alt T allele of rs2280906 exhibits stronger binding to repressive TFs than the Ref C allele, particularly, when the latter is hypermethylated. In unaffected individuals, hypomethylation of the Ref C allele weakens TF binding, supporting gene expression. In contrast, in affected individuals, hypermethylation of the Ref allele leads to biallelic methylation, increased repressive TF binding, and downregulation of MYOM2 expression (Fig. 5 K). These data provide strong evidence that rs2280906 mediates transcriptional repression through a methylation-dependent, allele-specific mechanism. Discussion SCZ is a complex polygenic disorder influenced by genetic, developmental, and environmental factors. Although numerous disease-associated methylation variants have been identified, their causal relationship with SCZ remains unclear, particularly in terms of how these non-coding variants contribute to disease risk. While most studies focus on identifying disease-related epigenetic modifications, few have investigated the specific risk genes regulated by these variants. In our previous work, we identified 21,166 ASM sites in monozygotic twins discordant for psychiatric disorders [ 2 ]. Notably, the majority of these sites, like many GWAS loci, are located in non-coding regions[ 26 , 12 , 27 ]. In this study, we sought to elucidate how ASM sites influence disease risk. We employed MR to integrate ASM data with brain eQTLs and GWAS summary statistics and identified seven disease-associated ASM loci corresponding to four risk genes: SLC2A6 , SLC25A12 , MYOM2 , and FHIT . Although only four risk genes were identified, they were significantly enriched in energy metabolism pathways–an observation consistent with prior studies. For instance, metabolic pathways such as insulin signaling, glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and oxidative phosphorylation are involved in the central dysfunction of SCZ [ 41 ]. Therapeutic strategies targeting energy metabolism–such as pioglitazone and the ketogenic diet–have shown promise in alleviating SCZ symptoms [ 42 – 47 ]. Moreover, energy metabolism defects are also observed in patients with other neurological disorders, including bipolar disorder [ 48 , 49 ], major depression[ 50 – 52 ], autism spectrum disorder [ 53 , 54 ], and Alzheimer’s disease[ 55 , 56 ]. These findings suggest that enhancing energy supply may offer a viable therapeutic and preventive strategy for SCZ. A key contribution of this study is the detailed mechanistic dissecting of the rs2280906 ASM locus. We demonstrated that deleting a small genomic region surround rs2280906 led to a ~ 10-fold downregulation of MYOM2 expression, indicating strong transcriptional regulatory capacity. Located in intron 36 of MYOM2 (~ 98 kb upstream of the promoter), rs2280906 exhibited significant spatial interaction with the MYOM2 promoter in 4C-seq experiments, suggesting long-range regulation via chromatin looping. EMSA further revealed allele-specific binding of repressive TFs: the Alt T allele showed stronger binding compared to the Ref C allele, particularly when the C allele was hypermethylated. In unaffected individuals, hypomethylation of the Ref C allele weakens TF binding and supports MYOM2 expression. Conversely, in affected individuals, hypermethylation of the Ref allele results in biallelic methylation, enhanced recruitment of repressive TF binding, and MYOM2 downregulation. These findings strongly support a methylation-dependent, allele-specific mechanism of transcriptional repression mediated by rs2280906. Interestingly, several loci in perfect LD (LD = 1) with rs2280906 highlight its potential functional significance. To pinpoint causal variants within this cluster, we performed regional and single-site mutagenesis using dual-luciferase reporter assays, revealing distinct and synergistic effects among tightly linked variants. This supports the multiple causal variants hypothesis, which posits that GWAS signals often arise from several functional variants acting within the same locus [ 57 , 58 ]. While this hypothesis typically spans large genomic regions (~ 5 Mb)[ 58 ], our results demonstrate that multiple regulatory variants can co-exist within a 500 bp segment, underscoring the need for finer resolution in functional annotation. Given that ASM is influenced by both genotype and methylation status, high-depth whole-genome methylation sequencing could enhance the identification of functional epigenetic variants. Furthermore, integrating multi-omics datasets–including ATAC-seq, ChIP-seq, and Hi-C–will be critical to fully delineate the molecular mechanism underlying SCZ-associated ASM loci. Beyond identifying risk genes, this study also proposes novel therapeutic targets. The downregulation of MYOM2 in SCZ patients suggests that restoring its expression could have therapeutic benefits. Epigenetic interventions targeting rs2280906, such as DNA methyltransferase inhibitors or CRISPR-dCas9-based epigenome editing, may represent promising avenues for future research. In summary, by integrating ASM, eQTL, and GWAS data through MR, this study identifies novel risk genes regulated by ASM and uncovers the epigenetic mechanisms underlying the rs2280906– MYOM2 axis. These findings advance our understanding of how epigenetic regulation contributes to disease susceptibility and individual phenotypic differences in SCZ, while also opening up new paths for therapeutic intervention. Declarations Supplementary information Supplemental Information includes three supplementary tables can be found with this article online. Competing Interests statement The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China [grant number 82201655, 81671333, 82471527], the Chinese Postdoctoral Science Foundation [grant number 2022M721506, 2024T170381], and the College Students' Innovative Entrepreneurial Training Plan Program [grant number 202312121016]. Author Contribution Qiyang Li, Yuanyuan Gai and Zhongwei Li contributed equally to this work. Cunyou Zhao and Wen Wu are the corresponding authors for this work. Performed the experiments: QL, YG and XL. Analyzed the data: QY, ZL, YG, ZW and CZ. Wrote the paper: QL, YG, ZL, WW and CZ. Acknowledgement FundingThis work was supported by the National Natural Science Foundation of China [grant number 82201655, 81671333, 82471527], the Chinese Postdoctoral Science Foundation [grant number 2022M721506, 2024T170381], and the College Students' Innovative Entrepreneurial Training Plan Program [grant number 202312121016]. References Smeland OB, Frei O, Dale AM, Andreassen OA (2020) The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. 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J Neurol 272 (1):2. doi:10.1007/s00415-024-12800-8 Long E, Williams J, Zhang H, Choi J (2025) An evolving understanding of multiple causal variants underlying genetic association signals. Am J Hum Genet 112 (4):741-750. doi:10.1016/j.ajhg.2025.01.018 Arthur TD, Nguyen JP, Henson BA, D'Antonio-Chronowska A, Jaureguy J, Silva N, i PC, Panopoulos AD, Izpisua Belmonte JC, D'Antonio M, McVicker G, Frazer KA (2025) Multiomic QTL mapping reveals phenotypic complexity of GWAS loci and prioritizes putative causal variants. Cell Genom 5 (3):100775. doi:10.1016/j.xgen.2025.100775 Additional Declarations No competing interests reported. Supplementary Files SupplementTables.xlsx SupplementaryFigure1.tif Supplementary Figure S1. Alterations in allelic DNA methylation levels at seven identified ASM sites in monozygotic twin pairs discordant for psychiatric disorders. ASM patterns at seven loci identified through MR analysis are shown with the methylation level at the reference (Refer) and alternative (Alter) alleles for affected (Ctrl) versus unaffected (Case) co-twins. SupplementaryFigure2.tif Supplementary Figure S2. Full uncropped images of gels and blots for EMSA, gel electrophoresis, and Western blot. (A-C) EMSA competition assay using nuclear extracts from HEK293T cells for comparison of binding affinity between the reference (Ref) C allele and the alternative (Alt) T allele (A), the unmethylated versus methylated Ref C allele (B), and the methylated Ref C allele versus Alt T allele (C). (D) Validation of NFY binding to rs2280908 and rs2280909 loci by EMSA supershift assay. (E) Gel electrophoresis confirming successful CRISPR/Cas9-mediated deletion of ~500 bp region near rs2280906. (F) Western blot analysis with varying exposure times demonstrates a corresponding decrease in MYOM2 protein levels following the deletion. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Molecular Neurobiology → Version 1 posted Editorial decision: Revision requested 30 Jul, 2025 Reviews received at journal 29 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 25 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6970984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":487698518,"identity":"3f3f5eff-aa53-418c-81d5-50eb397f9030","order_by":0,"name":"Qiyang Li","email":"","orcid":"","institution":"Zhujiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiyang","middleName":"","lastName":"Li","suffix":""},{"id":487698519,"identity":"9add8079-e906-4835-9e32-895e9781a563","order_by":1,"name":"Yuanyuan Gai","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Gai","suffix":""},{"id":487698520,"identity":"fdff5f66-b4c5-43ca-b2fe-b6d703572542","order_by":2,"name":"Zhongwei Li","email":"","orcid":"","institution":"Jiangmen Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongwei","middleName":"","lastName":"Li","suffix":""},{"id":487698521,"identity":"dede68ef-b043-4b25-8f5c-224b4311f4a0","order_by":3,"name":"Zhongju Wang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhongju","middleName":"","lastName":"Wang","suffix":""},{"id":487698522,"identity":"6acdb41a-da2a-4477-9385-696ea72b344a","order_by":4,"name":"Xingjian Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingjian","middleName":"","lastName":"Li","suffix":""},{"id":487698523,"identity":"562af4a7-0349-4437-9c2f-082c15698450","order_by":5,"name":"Wen Wu","email":"","orcid":"","institution":"Zhujiang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Wu","suffix":""},{"id":487698525,"identity":"5606f755-6bb3-4363-a7e4-ca677e6ce25a","order_by":6,"name":"Cunyou Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYHACNiC2gTB5SNCSRrqWwyRo4Z+R/uzBj4rz9rozEhgfvG1jkDcnpEXiRkK6Yc+Z24nbbiQwG85tYzDc2UBAi4FEwjEJ3rbbCWY3EtikedsYEgwOENSS2Cb5t+2cPVAL+28itSSDDD/ACHQYGzNRWiTOPGOTljmTnLjtzMNmyTnnJAw3ENLC357+TPJNhZ292fHkgx/elNnIE7SFQSABxmJsANlKSD3IGoKGjoJRMApGwYgHAMMCPf9KFD5LAAAAAElFTkSuQmCC","orcid":"","institution":"Zhujiang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Cunyou","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-06-25 06:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6970984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6970984/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12035-025-05469-1","type":"published","date":"2025-12-01T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87175285,"identity":"5205dd5a-7f70-4b9a-9577-00867b1d26cf","added_by":"auto","created_at":"2025-07-21 08:26:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1085578,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR identifies risk gene sets regulated by ASM loci in psychiatric disorders. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Genomic distribution of disease-associated ASM loci, showing the distribution across intergenic, intronic, and other non-coding genomic regions. (\u003cstrong\u003eB\u003c/strong\u003e) Schematic of the integrative analytical combining ASM profiles, brain eQTL data, and GWAS summary statistics to identify candidate risk genes through MR analysis. (\u003cstrong\u003eC\u003c/strong\u003e) Gene Ontology-Molecular Function (MF) and Biological Processes (BP) enrichment analysis of the MR-identified risk gene sets, revealing significant enrichment in energy metabolism-related terms. (\u003cstrong\u003eD\u003c/strong\u003e) Protein–protein interaction (PPI)-based pathway enrichment analysis further supports the functional convergence of the risk genes in metabolic processes. (\u003cstrong\u003eE\u003c/strong\u003e) PPI network of the identified genes; nodes in red indicate genes directly implicated as risk genes by MR analysis.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/7fde785c24685fc877b1514f.jpg"},{"id":87175287,"identity":"b89f9c2e-48c8-430a-b0ca-0cc3a0edf25a","added_by":"auto","created_at":"2025-07-21 08:26:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":548965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of regulatory activity at causal ASM loci. (A) \u003c/strong\u003eSchematic of the dual-luciferase reporter assay used to evaluate allele-specific regulatory activity of ASM loci. Genomic fragments containing either the reference or alternative allele were cloned upstream of a minimal promoter driving luciferase expression. (\u003cstrong\u003eB, C\u003c/strong\u003e) Luciferase assay results in HEK293T (\u003cstrong\u003eB\u003c/strong\u003e) and SK-N-SH (\u003cstrong\u003eC\u003c/strong\u003e) cells demonstrating differences in transcriptional activity between alleles of the candidate ASM loci. All data represent the means ± SEMs. A two-tailed t-test was used for comparisons between the two indicated groups (*p \u0026lt; 0.05; ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/5a8cf81cedae91729f8c9b0e.jpg"},{"id":87175284,"identity":"ac98e10b-0d5d-4c8c-8c81-005040dba76e","added_by":"auto","created_at":"2025-07-21 08:26:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":720829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFine-mapping and functional validation of regulatory sites at the ASM Locus rs2280906. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic of regional mutagenesis strategy used to dissect regulatory elements surrounding rs2280906 in a dual-luciferase reporter assay. Distinct genomic fragments spanning the locus were systematically mutated to assess their contribution to transcriptional regulation. (\u003cstrong\u003eB\u003c/strong\u003e) Results of regional mutagenesis in HEK293T (left) and SK-N-SH (right) cells, identifying specific subregions within the rs2280906 locus that drive allele-specific transcriptional activity. (\u003cstrong\u003eC\u003c/strong\u003e) Schematic of targeted mutagenesis experiments within Region 3, which contains three SNPs in perfect LD with rs2280906. Individual and combined mutations were introduced to evaluate their functional contributions. (\u003cstrong\u003eD\u003c/strong\u003e) Luciferase assay results following mutagenesis of the three linked loci in Region 3, performed in HEK293T (left) and SK-N-SH (right) cells. The results reveal distinct and potentially synergistic regulatory effects among closely linked variants, supporting the presence of multiple functional sites within a confined genomic region. All data represent the means ± SEMs. Significant deviations in the indicated groups from Con were examined using the two-tailed student’s t-test: *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001；****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/403e2e113cf45326016a72e5.jpg"},{"id":87175409,"identity":"eb846839-9599-4a6d-920a-9ec9a4112389","added_by":"auto","created_at":"2025-07-21 08:34:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1374421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory mechanism of the ASM locus rs2280906.\u003c/strong\u003e (\u003cstrong\u003eA-C\u003c/strong\u003e) EMSA competition assay using nuclear extracts from HEK293T cells for comparison of binding affinity between the reference (Ref) C allele and the alternative (Alt) T allele (\u003cstrong\u003eA\u003c/strong\u003e), the unmethylated versus methylated Ref C allele (\u003cstrong\u003eB\u003c/strong\u003e), and the methylated Ref C allele versus Alt T allele (\u003cstrong\u003eC\u003c/strong\u003e). (\u003cstrong\u003eD\u003c/strong\u003e) Position Weight Matrix predicted NFY binding at rs2280908 and rs2280909 loci. (\u003cstrong\u003eE\u003c/strong\u003e) Validation of NFY binding to rs2280908 and rs2280909 loci by EMSA supershift assay. (\u003cstrong\u003eF\u003c/strong\u003e) Schematic diagram of CRISPR/Cas9 to delete rs2280908 and rs2280909. (\u003cstrong\u003eG\u003c/strong\u003e) Representative Sanger sequencing traces of bisulfite-converted PCR products showing the methylation level at CpG sites 4, 5, and 8 near the functional region in the wild-type (WT) and knockout (KO) HEK293T cells. (\u003cstrong\u003eH\u003c/strong\u003e) Bisulfite sequencing of individual clones showing the methylation status at 14 CpG sites surrounding rs2280908 and rs2280909. Each row represents a single bisulfite-treated clone shown with methylated CpGs (●) or unmethylated CpGs (○). (\u003cstrong\u003eI\u003c/strong\u003e) Quantification of average methylation levels at 12 CpG sites near the functional regions in WT versus KO cells, demonstrating widespread demethylation upon deletion.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/a42d46ad18aa78129946b192.jpg"},{"id":87175291,"identity":"f53582d7-3c27-4bf4-a33b-3374a03cd1cd","added_by":"auto","created_at":"2025-07-21 08:26:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1373394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMYOM2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Expression by the ASM Locus rs2280906. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) 4C assay demonstrating a spatial interaction between the rs2280906 locus and the \u003cem\u003eMYOM2\u003c/em\u003e promoter. (\u003cstrong\u003eB\u003c/strong\u003e) Gel electrophoresis confirming successful CRISPR/Cas9-mediated deletion of ~500 bp region near rs2280906. (\u003cstrong\u003eC\u003c/strong\u003e) Sanger sequencing validation of the target deletion at the rs2280906. (\u003cstrong\u003eD\u003c/strong\u003e) qPCR analysis showing a significant reduction in \u003cem\u003eMYOM2\u003c/em\u003e mRNA expression following deletion of ~500 bp region surround rs2280906. (\u003cstrong\u003eE\u003c/strong\u003e) Western blot analysis demonstrating a corresponding decrease in MYOM2 protein levels after deletion. (\u003cstrong\u003eF\u003c/strong\u003e) Schematic of dCas9-Tet1 de-mediated at the rs2280908 and rs2280909 regions. (\u003cstrong\u003eG\u003c/strong\u003e) The methylation level of dCas9-Tet1 de-mediated at the rs2280908 and rs2280909 regions. (\u003cstrong\u003eH\u003c/strong\u003e) Quantification of \u003cem\u003eMYOM2\u003c/em\u003eexpression after de-methylation. (\u003cstrong\u003eI\u003c/strong\u003e) Forelimb and hindlimb grip strength are significantly reduced in \u003cem\u003eMyom2\u003c/em\u003eknockout mice. (\u003cstrong\u003eJ\u003c/strong\u003e) Differential transcript usage (DTU) analysis of \u003cem\u003eMYOM2\u003c/em\u003e between healthy control (Ctrl) and individuals with schizophrenia (SCZ). (\u003cstrong\u003eK\u003c/strong\u003e) Schematic model illustrating how methylation-dependent, allele-specific regulation at rs2280906 disrupt MYOM2 expression and contributes to schizophrenia pathogenesis. All data represent the means ± SEMs. Significant deviations in the indicated groups from Con were examined using the two-tailed student’s t-test: *p \u0026lt; 0.05; **p \u0026lt; 0.01; ***p \u0026lt; 0.001；****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/97c61abf5f0e02ebb5dccb49.jpg"},{"id":97724090,"identity":"640648ff-a821-4918-9e2a-e4e01f2a195d","added_by":"auto","created_at":"2025-12-08 16:11:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6130374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/8e01c897-6e81-4b08-a776-fe70232fca3b.pdf"},{"id":87175288,"identity":"5e742478-be4e-4cd1-bb24-eadb6fd76e80","added_by":"auto","created_at":"2025-07-21 08:26:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":171750,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/c4bc6b085a2b34cf6da2c658.xlsx"},{"id":87175309,"identity":"936f1fd4-e9df-4931-bc33-510bfdac02e1","added_by":"auto","created_at":"2025-07-21 08:26:32","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24727108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. Alterations in allelic DNA methylation levels at seven identified ASM sites in monozygotic twin pairs discordant for psychiatric disorders. \u003c/strong\u003eASM patterns at seven loci identified through MR analysis are shown with the methylation level at the reference (Refer) and alternative (Alter) alleles for affected (Ctrl) versus unaffected (Case) co-twins.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/d2d0b635e3a130642322952c.tif"},{"id":87175315,"identity":"dea6b551-70e2-4f48-92b8-c3399e4a556e","added_by":"auto","created_at":"2025-07-21 08:26:34","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":46362132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2.\u003c/strong\u003e \u003cstrong\u003eFull uncropped images of gels and blots for EMSA, gel electrophoresis, and Western blot.\u003c/strong\u003e (\u003cstrong\u003eA-C\u003c/strong\u003e) EMSA competition assay using nuclear extracts from HEK293T cells for comparison of binding affinity between the reference (Ref) C allele and the alternative (Alt) T allele (\u003cstrong\u003eA\u003c/strong\u003e), the unmethylated versus methylated Ref C allele (\u003cstrong\u003eB\u003c/strong\u003e), and the methylated Ref C allele versus Alt T allele (\u003cstrong\u003eC\u003c/strong\u003e). (\u003cstrong\u003eD\u003c/strong\u003e) Validation of NFY binding to rs2280908 and rs2280909 loci by EMSA supershift assay. (\u003cstrong\u003eE\u003c/strong\u003e) Gel electrophoresis confirming successful CRISPR/Cas9-mediated deletion of ~500 bp region near rs2280906. (\u003cstrong\u003eF\u003c/strong\u003e) Western blot analysis with varying exposure times demonstrates a corresponding decrease in MYOM2 protein levels following the deletion.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6970984/v1/3b8ec0ad4618a8cfed910327.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Allele-Specific Methylation Links Non-Coding Variant of rs2280906 to MYOM2 Regulation in Schizophrenia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSchizophrenia (SCZ) is a complex polygenic disorder shaped by genetic, epigenetic, and environmental factors. Major psychiatric disorders, including schizophrenia (SCZ) and bipolar disorder (BPD), are highly complex polygenetic mental diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Their complexity arises from pronounced phenotypic heterogeneity and the intricate interplay among genetic, developmental, and environmental factors. Recent genome-wide association studies (GWAS) have identified numerous risk loci associated with these disorders, providing valuable insights into their genetic architecture and underlying biological mechanisms[\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, these loci explain less than a quarter of the observed phenotypic variance, highlighting the important role of non-genetic contributors, particularly epigenetic modifications, in disease etiology[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong epigenetic modifications, DNA methylation play a key role in mediating the interaction between genetic predisposition and environmental influences[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. DNA methylation regulates gene expression by modulating transcription factor (TF) binding and recruiting histone modification complexes. Aberrant DNA methylation patterns have been implicated in the pathophysiology of complex diseases, including SCZ and BPD[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR18\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Traditionally, DNA methylation was assumed to occur symmetrically across both allele of a gene. However, recent studies have uncovered widespread allele-specific methylation (ASM) sites, in which one allele is highly methylated while the other remains lowly methylated or unmethylated[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. ASM sites are responsive to environmental cues and can function as epigenetic switches that regulate gene expression dosage through alterations in TF binding and chromatin structure[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR23\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Dysregulation of these ASM-mediated methylation switches can lead to aberrant expression of neuronal genes, disrupt neurodevelopment and processes, and modulate individual responses to environmental stressors, thereby contributing to the onset and progression of psychiatric disorders[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Importantly, ASM provides a plausible mechanism by which non-coding variants identified in GWAS influence disease risk via gene–environment interactions.\u003c/p\u003e\u003cp\u003eIn our previous work, we employed methylated DNA immunoprecipitation sequencing (MeDIP-seq) and whole-genome sequencing (WGS) in monozygotic (MZ) twin pairs discordant for psychiatric disorders to identify numerous psychiatry-associated ASM (psyASM) sites [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], implicating them in psychopathology. Similar to many GWAS-identified single nucleotide polymorphisms (SNPs), these ASM sites are predominantly located in non-coding regions with complex regulatory functions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the specific mechanisms through which they contribute to psychiatric disorders, particularly by regulation of gene expression, remain largely unexplored. To address this, we leveraged Mendelian randomization (MR), a statistical approach that infers causal relationships between risk factors and disease traits. MR has proven effective in the post-GWAS era for integrating genetic association data with multi-omics layers, such as transcriptomics and proteomics, to uncover the target genes underlying non-coding risk loci[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This integrative approach holds promise for identifying the genes regulated by psyASM sites in psychiatric disorders.\u003c/p\u003e\u003cp\u003eIn this study, we integrated psyASM data, expression quantitative trait loci (eQTL), and GWAS data using MR to identify a set of risk genes regulated by ASM loci in psychiatric disorders. Our findings revealed that these genes are primarily involved in biological pathways such as energy metabolism. Through functional validation, we further demonstrated that the psyASM locus rs2280906 modulates the expression of the SCZ risk gene \u003cem\u003eMYOM2\u003c/em\u003e (\u003cem\u003eMyomesin 2\u003c/em\u003e). Methylation changes at this locus influence TF binding and regional methylation architecture. Our results provide new insights into the causal roles of psyASM loci in SCZ and BPD, uncover the epigenetic mechanisms of the rs2280906-\u003cem\u003eMYOM2\u003c/em\u003e axis, and offer potential implications for understanding disease etiology and developing target interventions.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cb\u003eGWAS summary statistics of SCZ\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGWAS summary statistics for SCZ were obtained from the large-scale meta-analysis conducted by Vassily Trubetskoy and colleagues on behalf of the Psychiatric Genomics Consortium (PGC) in 2022 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study included 76,755 schizophrenia cases and 243,649 controls, primarily of East Asian and European ancestry. The GWAS summary data used in this analysis were downloaded from the official PGC website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pgc.unc.edu/\u003c/span\u003e\u003cspan address=\"https://pgc.unc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The large sample size substantially increased statistical power, enabling the detection of genetic variants with modest effect sizes and enhancing the resolution of association signals relevant to disease etiology.\u003c/p\u003e\u003cp\u003e\u003cb\u003eeQTL of Brain tissue\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this study, expression quantitative trait loci (eQTLs) were used as instrumental variables to infer regulatory relationships between genetic variants and gene expression. eQTL data were obtained from the Genotype-Tissue Expression (GTEx) Project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which includes 15,201 ribonucleic acid-sequencing (RNA-seq) samples from 49 different tissues collected from 838 postmortem donors. Specifically, we extracted cis-eQTLs from brain regions relevant to psychiatric disorders, including the Hippocampus, Frontal Cortex, and Cortex, using GTEx version 8.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMendelian Randomization to Identify ASM-Regulated Risk Genes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify putative risk genes regulated by allele-specific methylation (ASM) loci, we employed the Summary-data-based Mendelian Randomization (SMR) method[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. SMR enables the estimation and testing of pleiotropic associations between molecular traits and disease phenotypes by integrating quantitative trait loci (QTL) and GWAS summary statistics, facilitating the inference of potential causal genes. ASM loci identified in previous work[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] were annotated to their corresponding or nearest genes. For each ASM-gene pair, the relevant cis-eQTL subsets were extracted using the --extract-probe parameter in SMR. Linkage disequilibrium (LD) and heterogeneity in dependent instruments (HEIDI) tests were conducted using PLINK binary format genotype reference data from the 1000 Genomes Project Phase 3. PLINK is an open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. HEIDI tests help differentiate true pleiotropic effects from associations due to linkage; loci with a \u003cem\u003eP\u003c/em\u003e\u003csub\u003eHEIDI\u003c/sub\u003e \u0026lt; 0.05 were considered heterogeneous and excluded from further analysis. The HEIDI test was applied using the top 20 cis-eQTL SNPs within a 2,000 Kb window around the gene of interest. SNPs showing \u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e \u0026lt; 0.05 were considered nominally significant, and the corresponding genes were regarded as potential ASM-regulated schizophrenia risk genes. All MR analyses, including SMR and HEIDI testing, were performed using SMR software (version 1.3.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.westlake.edu.cn/software/smr/#Overview\u003c/span\u003e\u003cspan address=\"https://yanglab.westlake.edu.cn/software/smr/#Overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via command line [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo explore biological relevance, we conducted Gene Ontology (GO) enrichment analysis of ASM-prioritized genes using the ToppGene Suite (WebGestalt: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.webgestalt.org/option.php\u003c/span\u003e\u003cspan address=\"http://www.webgestalt.org/option.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and constructed protein–protein interaction (PPI) networks using GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Chromatin interaction data were visualized using circular chromosome capture (4C) plots via the Genome Browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://3dgenome.fsm.northwestern.edu/\u003c/span\u003e\u003cspan address=\"https://3dgenome.fsm.northwestern.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Functional validation and phenotypic relevance were further assessed using gene–phenotype associations from the International Mouse Phenotyping Consortium (IMPC) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell Culture\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHEK293T and SK-N-SH cells were cultured in high-glucose Dulbecco’s Modified Eagle Medium (DMEM; Gibco) supplemented with 10% fetal bovine serum (FBS; ExCell Bio). Cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLuciferase Reporter Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the regulatory potential of ASM loci, DNA fragments (~ 500 bp) flanking each candidate ASM site were PCR-amplified and cloned into the pGL4.23 [luc2/minP] vector (Promega), which contains a minimal TATA-box promoter upstream of the luciferase gene (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Site-directed mutagenesis was used to introduce the alternative alleles of the ASM site and/or tightly linked SNPs (in perfect linkage disequilibrium), replacing the reference alleles. Constructs were transiently co-transfected into HEK293T or SK-N-SH cells along with a Renilla luciferase control plasmid (pRL-TK) using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions. After 48 hours, firefly and Renilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System (Promega) on a Wallac Victor V1420 Multilabel Counter (PerkinElmer). Firefly luciferase activity was normalized to Renilla luciferase activity to control for transfection efficiency.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLentivirus Plasmid Construction, Packaging, and Transduction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the role of the ASM locus rs2280906, we employed Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 (CRISPR/Cas9) technology to disrupt this genomic site. A single-guide RNA (sgRNA) targeting the rs2280906 locus was designed using resources from the Zhang Laboratory (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zlab.bio/guide-design-resources\u003c/span\u003e\u003cspan address=\"https://zlab.bio/guide-design-resources\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The sgRNA oligonucleotides were synthesized by Invitrogen and subsequently cloned into the lentiCRISPRv2 vector (Addgene #52961) using BsmBI digestion. Lentiviral particles were packaged by co-transfecting the lentiCRISPRv2-sgRNA plasmid with packaging plasmids into HEK293T cells. The viral supernatant was collected 48 hours post-transfection and used to transduce SK-N-SH cells via the calcium phosphate co-precipitation method[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Transduced cells were incubated for 48–72 hours, after which GFP-positive (GFP+) cells were identified by fluorescence microscopy. Stable cell lines were selected by applying 2 µg/ml puromycin for 7–10 days. The impact of functional SNP disruption and knockout of the rs2280906 locus on \u003cem\u003eMYOM2\u003c/em\u003e expression was assessed by quantitative real-time PCR (qRT-PCR) and Western blotting. Primary antibodies used include rabbit anti-MYOM2 (1:3,000, Proteintech 32262-1-AP) and rabbit anti-β-actin (1:1,000, Proteintech 20536-1-AP) as a loading control.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEMSA, ChIP, Bisulfite Conversion and PCR\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNuclear protein was extracted from HEK293T cells for evaluating the binding affinity of allelic variations and methylation changes at the functional loci rs2280908-rs2280909 using an Electrophoretic Mobility Shift Assay (EMSA). The Chemiluminescent Nucleic Acid Detection Module Kit (Thermo Fisher) was used to detect biotin-labeled DNA-protein complexes. The assay was visualized and quantified using chemiluminescence imaging on a Tanon 5200 system. For bisulfite conversion, genomic DNA was extracted from HEK293T cells and modified using the EpiArt™ DNA Methylation Bisulfite Kit (Vazyme) according to the manufacturer's instructions. Bisulfite-converted DNA was amplified using BS-PCR primers specific to the target region. To assess methylation status, PCR products were cloned into the pMD19-T cloning vector (Takara, China). For each construct, 10–12 individual clones were sequenced to determine the methylation level of the target CpG sites.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIntegrative Analysis of ASM Loci Reveals Risk Genes in Psychiatric Disorders\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn our previous study, we performed MeDIP-seq and WGS on peripheral blood DNA from 17 pairs of MZ twins[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These included 9 pairs discordant for psychiatric disorders (PDC; 4 with SCZ-discordant pairs [SDC] and 5 with BPD-discordant pairs [BDC]), 4 concordant pairs for psychiatric disorders (PCC; 1 SCZ-concordant pair [SCC] and 3 BPD-concordant pairs [BCC]), and 4 healthy control-concordant pairs (HCC). From this analysis, we identified 220,520 ASM sites, of which 21,166 were significantly associated with psychiatric disorders. Notably, the majority of these ASM sites were in located in non-coding regions, such as intergenic (8,969) and intronic (8,420) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). To identify potential disease-associated genes regulated by these ASM sites, we performed MR analysis by integrating ASM data with brain eQTL and PGC3 SCZ-GWAS datasets \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. This integrative approach revealed 7 disease-related ASM loci corresponding to 4 risk genes: \u003cem\u003eSLC2A6\u003c/em\u003e, \u003cem\u003eSLC25A12\u003c/em\u003e, \u003cem\u003eMYOM2\u003c/em\u003e, and \u003cem\u003eFHIT\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eSLC2A6\u003c/em\u003e encodes solute carrier family 2 member 6 involved in neuronal signaling and synaptic function and may influence neuronal excitability and synaptic transmission[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. \u003cem\u003eSLC25A12\u003c/em\u003e encodes the mitochondrial aspartate/glutamate carrier (AGC1), a key component of the malate/aspartate shuttle that supports mitochondrial energy metabolism in neurons[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Notably, \u003cem\u003eSLC25A12\u003c/em\u003e is upregulated in postmortem brain tissue of individuals with autistic, particularly in the prefrontal cortex[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e36\u003c/span\u003e], where its overexpression may disrupt neuronal network formation, suggesting a role in neurodevelopmental disorders. \u003cem\u003eMYOM2\u003c/em\u003e, a gene associated with cytoskeletal integrity, may influence disease through effects on neuronal morphology and function[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cem\u003eFHIT\u003c/em\u003e, primarily known for its role in apoptosis, also contribute to neuropathic pain via its influence on NK1R hyperexcitability mediated by GABAergic neurons[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMendelian Randomization identifies ASM sites regulating disease risk gene sets.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Gene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASM Site\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eASM Site Position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef Allele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlt Allele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eGWAS\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eeQTL\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTopSNP Position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eSMR\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSLC2A6\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers587600128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c9\"\u003e\u003cp\u003e6.75E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr2:172901330: T:C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.56E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8.76E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.39E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr2:172873060: G:A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.82E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.61E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.27E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr2:172817794: G:A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.59E-03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cem\u003eMYOM2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers117109793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr8:2055127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.58E-03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.16E-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr8:2102543: T:G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.52E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers2280906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr8:2091232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eintronic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.39E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr8:2077072: A:G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.88E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers66538145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr8:2099645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.39E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr8:2077072: A:G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.88E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eFHIT\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers55646807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr3:61536565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.06E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.87E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr3:60809239: C:T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.56E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ers58580279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003echr3:61536564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eintergenic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.06E-02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.87E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003echr3:60809239: C:T\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.56E-02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eRisk gene indicates genes located nearest to the identified significant ASM Site; ASM site indicates Allele-specific methylation site; BF indicates Bayes Factors which are derived from the Bayesian generalized additive linear mixed model. GWAS indicates genome-wide association study. eQTL indicates expression quantitative trait loci. TopSNP indicates the single nucleotide polymorphism with the smallest P-value in a genomic region in GWAS. SMR indicates Summary-data-based Mendelian Randomization.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAltered methylation at these ASM loci may regulate the expression of these genes, and their dysregulation could contribute causally to the development of psychiatric disorders. Despite identifying a limited number of risk genes, functional enrichment analysis revealed significant involvement in energy metabolism pathways, particularly purine nucleotide metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Furthermore, protein-protein interaction (PPI) network analysis indicated that these genes and their interacting proteins are involved in biological processes such as carbohydrate biosynthesis, hexose biosynthesis, and the respiratory electron transport chain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The 4 risk genes were located at core nodes, with SLC25A12 at a key node, implicated in functions enriched in those energy-metabolism pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These findings highlight the potential of targeting energy metabolism as therapeutic strategy for psychiatric disorders.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunctional Dissection and Regulatory Mechanism of the ASM Locus rs2280906\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the seven disease risk loci identified in our study, all seven contained ASM sites whose aberrant methylation shifting patterns were implicated in disease pathogenesis. Notably, five of these ASM loci\u0026ndash;rs145770325, rs117109793, rs2280906, rs55646807, and rs58580279\u0026ndash;exhibited a methylation shift from reference-allele hypermethylation in the unaffected individuals to biallelic methylation in the affected individuals (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). To validate the regulatory potential of these causal ASM loci identified through MR approach, we conducted dual-luciferase reporter assays. Approximately 300 base pairs of genomic sequence flanking each ASM locus was cloned upstream of a minimal promoter in the pGL4.23 luciferase reporter vector (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Due to their proximity-rs55646807 and rs58580279 are located at chr3:61536565 and chr3:61536564, respectively།both were inserted into the same reporter construct. The reporter vectors were transfected into HEK293T (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and SK-N-SH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) cell lines to assess allele-specific promoter activity. Among the loci tested, rs2280906 exhibited the most significant difference in luciferase activity between its two alleles, indicating a strong regulatory effect on gene expression and highlighting its potential functional relevance in disease mechanisms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven the presence of numerous loci in strong linkage disequilibrium (LD) near ASM sites, which can obscure the identification of the true functional variant, we investigated potential regulatory sites surrounding rs2280906 using the HalopReg database[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This analysis revealed that rs2280906 is in perfect LD (LD\u0026thinsp;=\u0026thinsp;1) with 10 linked loci (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), underscoring its potential functional importance. To pinpoint the functional site, we divided the 10 linked loci into four genomic regions and generated dual-luciferase reporter constructs carrying revertant mutations for each region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Upon transfection into HEK293T and SK-N-SH cell lines, all four regions showed varying levels of regulatory activity, with Regions 3 and 4 displaying the most pronounced effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), suggesting a complex regulatory landscape centered around the rs2280906 locus.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFocusing on Region 3, which includes rs2280907, rs2280908, and rs2280909, we constructed individual revertant mutation vectors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) and assessed their activity in both HEK293T and SK-N-SH cells. Among these, the revertant mutation of rs2280909 demonstrated the strongest translational repression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Since rs2280908 and rs2280909 reside within the same regulatory motif, we additionally tested a construct with combined revertant mutations. This dual mutation led to the most significant translational repression, identifying rs2280908 and rs2280909 as key functional sites.\u003c/p\u003e\u003cp\u003eTo further investigate the regulatory mechanism of this ASM locus, we conducted electrophoretic mobility shift assays (EMSAs) using a hot probe containing the Ref C allele at both rs2280908(C/T) and rs2280909(C/T) and a cold probe containing the Ref C allele or the Alt T allele. These assays demonstrated that the Alt T allele of the motif bound TFs more strongly than the Ref C allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). Moreover, competition experiments showed that methylation of the Ref C allele (5mC) further enhanced TF binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e). When compared directly, the Alt T allele exhibited even stronger binding affinity than the methylated Ref C allele (5mC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, \u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e), supporting the functional significance of allele-specific binding affinity and methylation status. We used position weight matrix to predict transcription factors that may bind to the rs2280908 and rs2280909 loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e) and found that the NFY transcription factor may bind to this loci. We further validated the binding of NFY to these loci through an EMSA super-shift assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, \u003cb\u003eSupplementary Fig.\u0026nbsp;2D\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBisulfite PCR (BS-PCR) confirmed that rs2280908 and rs2280909, adjacent to rs2280906, were highly methylated in their native genomic context (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-I). To determine whether these sites influence regional methylation, we used CRISPR/Cas9 to delete rs2280908 and rs2280909 in the HEK293T genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). This deletion led to complete demethylation of the surrounding region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-I), suggesting that these functional sites recruit DNA methyltransferases to establish or maintain local methylation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegulatory Role of rs2280906 in Modulating\u003c/b\u003e \u003cb\u003eMYOM2\u003c/b\u003e \u003cb\u003eExpression\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe disease-associated ASM locus rs2280906, located in intron 36 of the \u003cem\u003eMYOM2\u003c/em\u003e gene (~\u0026thinsp;98 kb upstream of the promoter), was evaluated for long-range regulatory activity using circular chromosome capture (4C) data using the 3D Genome Browser. These data revealed prominent chromatin interaction peaks between rs2280906 and the \u003cem\u003eMYOM2\u003c/em\u003e promoter (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), suggesting spatial proximity and potential regulatory influence. To confirm this hypothesis, we delete a\u0026thinsp;~\u0026thinsp;500 bp region surrounding rs2280906 using CRISPR/Cas9 in HEK293T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB \u003cb\u003e\u0026amp; C, Supplementary Fig.\u0026nbsp;2E\u003c/b\u003e). This deletion led to a tenfold reduction in \u003cem\u003eMYOM2\u003c/em\u003e RNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) and protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, \u003cb\u003eSupplementary Fig.\u0026nbsp;2F\u003c/b\u003e) levels, supporting a long-range enhancer-like function of rs2280906. To verify the regulatory relationship between the methylation level of this region and \u003cem\u003eMYOM2\u003c/em\u003e expression, we used a de-methylation - editing technique to demethylate this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Although the methylation level decreased slightly (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), \u003cem\u003eMYOM2\u003c/em\u003e expression increased significantly after de-methylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH), indicating that the demethylation of this region can enhance the transcriptional level of \u003cem\u003eMYOM2\u003c/em\u003e. The \u003cem\u003eMyom2\u003c/em\u003e knockout mice are reduced in forelimb and hindlimb grip strength from the public IMPC database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e32\u003c/span\u003e].Transcriptomic analysis of SCZ case\u0026ndash;control samples from the PsychENCODE brain RNA-seq dataset[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e40\u003c/span\u003e] revealed significant reduced differential transcript usage (DTU) of \u003cem\u003eMYOM2\u003c/em\u003e in patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ), further implicating the rs2280906\u0026ndash;\u003cem\u003eMYOM2\u003c/em\u003e regulatory axis in disease pathogenesis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMechanistically, the Alt T allele of rs2280906 exhibits stronger binding to repressive TFs than the Ref C allele, particularly, when the latter is hypermethylated. In unaffected individuals, hypomethylation of the Ref C allele weakens TF binding, supporting gene expression. In contrast, in affected individuals, hypermethylation of the Ref allele leads to biallelic methylation, increased repressive TF binding, and downregulation of \u003cem\u003eMYOM2\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). These data provide strong evidence that rs2280906 mediates transcriptional repression through a methylation-dependent, allele-specific mechanism.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSCZ is a complex polygenic disorder influenced by genetic, developmental, and environmental factors. Although numerous disease-associated methylation variants have been identified, their causal relationship with SCZ remains unclear, particularly in terms of how these non-coding variants contribute to disease risk. While most studies focus on identifying disease-related epigenetic modifications, few have investigated the specific risk genes regulated by these variants. In our previous work, we identified 21,166 ASM sites in monozygotic twins discordant for psychiatric disorders [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, the majority of these sites, like many GWAS loci, are located in non-coding regions[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, we sought to elucidate how ASM sites influence disease risk. We employed MR to integrate ASM data with brain eQTLs and GWAS summary statistics and identified seven disease-associated ASM loci corresponding to four risk genes: \u003cem\u003eSLC2A6\u003c/em\u003e, \u003cem\u003eSLC25A12\u003c/em\u003e, \u003cem\u003eMYOM2\u003c/em\u003e, and \u003cem\u003eFHIT\u003c/em\u003e. Although only four risk genes were identified, they were significantly enriched in energy metabolism pathways\u0026ndash;an observation consistent with prior studies. For instance, metabolic pathways such as insulin signaling, glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, and oxidative phosphorylation are involved in the central dysfunction of SCZ [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Therapeutic strategies targeting energy metabolism\u0026ndash;such as pioglitazone and the ketogenic diet\u0026ndash;have shown promise in alleviating SCZ symptoms [\u003cspan additionalcitationids=\"CR43 CR44 CR45 CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Moreover, energy metabolism defects are also observed in patients with other neurological disorders, including bipolar disorder [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e49\u003c/span\u003e], major depression[\u003cspan additionalcitationids=\"CR51\" citationid=\"CR53\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e52\u003c/span\u003e], autism spectrum disorder [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and Alzheimer\u0026rsquo;s disease[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These findings suggest that enhancing energy supply may offer a viable therapeutic and preventive strategy for SCZ.\u003c/p\u003e\u003cp\u003eA key contribution of this study is the detailed mechanistic dissecting of the rs2280906 ASM locus. We demonstrated that deleting a small genomic region surround rs2280906 led to a\u0026thinsp;~\u0026thinsp;10-fold downregulation of \u003cem\u003eMYOM2\u003c/em\u003e expression, indicating strong transcriptional regulatory capacity. Located in intron 36 of \u003cem\u003eMYOM2\u003c/em\u003e (~\u0026thinsp;98 kb upstream of the promoter), rs2280906 exhibited significant spatial interaction with the \u003cem\u003eMYOM2\u003c/em\u003e promoter in 4C-seq experiments, suggesting long-range regulation via chromatin looping. EMSA further revealed allele-specific binding of repressive TFs: the Alt T allele showed stronger binding compared to the Ref C allele, particularly when the C allele was hypermethylated. In unaffected individuals, hypomethylation of the Ref C allele weakens TF binding and supports \u003cem\u003eMYOM2\u003c/em\u003e expression. Conversely, in affected individuals, hypermethylation of the Ref allele results in biallelic methylation, enhanced recruitment of repressive TF binding, and \u003cem\u003eMYOM2\u003c/em\u003e downregulation. These findings strongly support a methylation-dependent, allele-specific mechanism of transcriptional repression mediated by rs2280906.\u003c/p\u003e\u003cp\u003eInterestingly, several loci in perfect LD (LD\u0026thinsp;=\u0026thinsp;1) with rs2280906 highlight its potential functional significance. To pinpoint causal variants within this cluster, we performed regional and single-site mutagenesis using dual-luciferase reporter assays, revealing distinct and synergistic effects among tightly linked variants. This supports the multiple causal variants hypothesis, which posits that GWAS signals often arise from several functional variants acting within the same locus [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. While this hypothesis typically spans large genomic regions (~\u0026thinsp;5 Mb)[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e58\u003c/span\u003e], our results demonstrate that multiple regulatory variants can co-exist within a 500 bp segment, underscoring the need for finer resolution in functional annotation. Given that ASM is influenced by both genotype and methylation status, high-depth whole-genome methylation sequencing could enhance the identification of functional epigenetic variants. Furthermore, integrating multi-omics datasets\u0026ndash;including ATAC-seq, ChIP-seq, and Hi-C\u0026ndash;will be critical to fully delineate the molecular mechanism underlying SCZ-associated ASM loci.\u003c/p\u003e\u003cp\u003eBeyond identifying risk genes, this study also proposes novel therapeutic targets. The downregulation of \u003cem\u003eMYOM2\u003c/em\u003e in SCZ patients suggests that restoring its expression could have therapeutic benefits. Epigenetic interventions targeting rs2280906, such as DNA methyltransferase inhibitors or CRISPR-dCas9-based epigenome editing, may represent promising avenues for future research.\u003c/p\u003e\u003cp\u003eIn summary, by integrating ASM, eQTL, and GWAS data through MR, this study identifies novel risk genes regulated by ASM and uncovers the epigenetic mechanisms underlying the rs2280906\u0026ndash;\u003cem\u003eMYOM2\u003c/em\u003e axis. These findings advance our understanding of how epigenetic regulation contributes to disease susceptibility and individual phenotypic differences in SCZ, while also opening up new paths for therapeutic intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eSupplementary information\u003c/h2\u003e\u003cp\u003eSupplemental Information includes three supplementary tables can be found with this article online.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests statement\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant number 82201655, 81671333, 82471527], the Chinese Postdoctoral Science Foundation [grant number 2022M721506, 2024T170381], and the College Students' Innovative Entrepreneurial Training Plan Program [grant number 202312121016].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQiyang Li, Yuanyuan Gai and Zhongwei Li contributed equally to this work. Cunyou Zhao and Wen Wu are the corresponding authors for this work. Performed the experiments: QL, YG and XL. Analyzed the data: QY, ZL, YG, ZW and CZ. Wrote the paper: QL, YG, ZL, WW and CZ.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eFundingThis work was supported by the National Natural Science Foundation of China [grant number 82201655, 81671333, 82471527], the Chinese Postdoctoral Science Foundation [grant number 2022M721506, 2024T170381], and the College Students' Innovative Entrepreneurial Training Plan Program [grant number 202312121016].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSmeland OB, Frei O, Dale AM, Andreassen OA (2020) The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. 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Cell Genom 5 (3):100775. doi:10.1016/j.xgen.2025.100775\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"allele-specific methylation, psychiatric disorders, Mendelian randomization, phenotypic variations, epigenetics","lastPublishedDoi":"10.21203/rs.3.rs-6970984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6970984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSchizophrenia (SCZ) is a complex polygenic disorder influenced by genetic, epigenetic, and environmental factors. While numerous risk loci disease-associated methylation variants have been identified, their functional impact and contribution to disease risk remain largely unclear. This study addresses a fundamental yet underexplored question: how do non-coding allele-specific methylation (ASM) sites influence disease risk via gene regulation? We employed the Mendelian Randomization (MR) method to integrate ASM data from monozygotic twins discordant for psychiatric disorders with brain eQTL and GWAS summary statistics to identify potential risk genes. The regulatory of the rs2280906 locus was investigated using dual-luciferase reporter assays, gene expression quantification, gene editing, methylation editing, and electrophoretic mobility shift assays. We used MR to prioritize these ASM locus associated with schizophrenia risk and demonstrated that the affected genes are enriched in energy metabolism pathways\u0026mdash;suggesting that targeting energy dysregulation may represent a promising therapeutic avenue. We further elucidated the allele-specific, methylation-dependent mechanism by which ASM site rs2280906 regulates risk gene \u003cem\u003eMYOM2\u003c/em\u003e. In healthy individuals, hypomethylation of the reference C allele permits \u003cem\u003eMYOM2\u003c/em\u003e expression. In contrast, affected individuals exhibit hypermethylation of this allele, leading to biallelic methylation, increased recruitment of repressive transcription factors, and \u003cem\u003eMYOM2\u003c/em\u003e downregulation. Our study uncovers new risk genes regulated by ASM and provide mechanistic insight into the rs2280906\u0026ndash;\u003cem\u003eMYOM2\u003c/em\u003e axis in schizophrenia. Our work advances understanding of how epigenetic regulation contributes to disease susceptibility and inter-individual variability, and offers new avenues for the identification of causal variants and therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Allele-Specific Methylation Links Non-Coding Variant of rs2280906 to MYOM2 Regulation in Schizophrenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 08:26:26","doi":"10.21203/rs.3.rs-6970984/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T14:33:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-29T04:20:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T14:04:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6110834202004090481793033500661027255","date":"2025-07-22T01:50:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160817501691914977421336366876219339659","date":"2025-07-18T06:59:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T00:19:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T12:15:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T12:14:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurobiology","date":"2025-06-25T05:55:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurobiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"moln","sideBox":"Learn more about [Molecular Neurobiology](https://www.springer.com/journal/12035)","snPcode":"12035","submissionUrl":"https://submission.nature.com/new-submission/12035/3","title":"Molecular Neurobiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1267b1c3-b384-445d-a635-4ab7c82768fc","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:06:32+00:00","versionOfRecord":{"articleIdentity":"rs-6970984","link":"https://doi.org/10.1007/s12035-025-05469-1","journal":{"identity":"molecular-neurobiology","isVorOnly":false,"title":"Molecular Neurobiology"},"publishedOn":"2025-12-01 15:56:57","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-07-21 08:26:26","video":"","vorDoi":"10.1007/s12035-025-05469-1","vorDoiUrl":"https://doi.org/10.1007/s12035-025-05469-1","workflowStages":[]},"version":"v1","identity":"rs-6970984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6970984","identity":"rs-6970984","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}