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. 2026 Apr 7;67(9):1460–1475. doi: 10.1111/jcpp.70151

Convergent genetic pathways linking neuropsychiatric and ocular disorders in children

Meng Pan 1,#, Wentao Zhou 1,#, Hui‐Qi Qu 2, Zhanjie Xiu 3,4, Rui Sun 1, Hakon Hakonarson 2,5,6,✉, Jin Li 3,4,✉, Xuefeng Shi 1,✉
PMCID: PMC13450179  PMID: 41947537

Abstract

Background

Clinical and epidemiological studies conducted in children have suggested a potential link between neuropsychiatric and ocular disorders. However, the existence and directionality of this relationship remain inconsistent, likely due to the complex interplay between genetic predisposition and environmental factors that may influence both conditions.

Methods

We investigated the overall and local genetic correlations and causal relationship between eight neuropsychiatric disorders and five ocular disorders based on large‐scale genome‐wide association study cohorts from Psychiatric Genomics Consortium and UK Biobank. An in‐house independent cohort (2,726 attention deficit hyperactivity disorder (ADHD) cases and 16,299 controls) was also assessed to confirm the disease relationships. We further performed joint analyses to pinpoint the shared genetic loci. Finally, we explored their underlying biological pathways by SNP‐ and Gene‐based enrichment analyses.

Results

We discovered significant positive genetic correlations between autism spectrum disorder (ASD) and myopia/astigmatism; inverse genetic correlation and potential causal relationship between ADHD and myopia/astigmatism. The aggregated protective effect of ADHD on myopia and astigmatism was demonstrated using Mendelian randomization, and was validated in the independent in‐house cohort. In total, 124 loci were found to be shared between psychiatric diseases and refractive errors. Enrichment analyses highlighted early neurodevelopmental processes as key shared genetic mechanisms that may play a critical role in the development of childhood‐onset neuropsychiatric disorders and refractive errors.

Conclusions

Our findings indicate a shared genetic architecture underlying the development of childhood‐onset neuropsychiatric disorders and refractive errors. These results help clarify the clinically observed associations between ADHD, ASD, and refractive errors and provide evidence that shared early neurodevelopmental processes contribute to both conditions. Together, the findings offer a novel perspective on the neurogenetic basis of refractive error and its connection to early brain development.

Keywords: Pediatric neuropsychiatric disorders, refractive error, genetic correlation, pleiotropy, genome‐wide association study, attention deficit/hyperactivity disorder

Introduction

Neuropsychiatric disorders represent a diverse group of common and complex conditions marked by substantial deficits in cognition, emotional regulation, and behavior (Kessler & Wang, 2008; Walker, McGee, & Druss, 2015). In parallel, visual impairment, most often caused by refractive error, strabismus, or amblyopia, affects at least 19 million children worldwide and has extensive consequences for psychological well‐being and educational attainment, posing a major global public health challenge (Holden et al., 2016; Li et al., 2022; Solebo & Rahi, 2014). The early‐onset manifestations of both neuropsychiatric and ocular disorders are of particular concern given their long‐term neurodevelopmental impacts and associated societal burden (Kieling et al., 2011; Li et al., 2022; Morgan et al., 2018).

An increasing number of epidemiological and clinical studies have examined the potential bidirectional associations between neuropsychiatric and ocular disorders in childhood (Bellato et al., 2022; Chang et al., 2021; Lee et al., 2022), particularly focusing on links between attention deficit/hyperactivity disorder (ADHD) or autism spectrum disorder (ASD) and various ophthalmologic conditions (Bellato et al., 2022; Chang, Gandhi, & O'Hara, 2019; Choi et al., 2022; Chou, Chen, Hsiao, Lai, & Yen, 2023). However, these reported associations remain inconsistent. For example, a bidirectional association between ADHD and myopia was observed in an East Asian cohort (Chou et al., 2023), but not in a German cohort (Reimelt et al., 2021). Likewise, ASD has been linked to increased risks of amblyopia and strabismus in U.S. studies (Chang et al., 2019, 2021), whereas no such association was found among Korean children with amblyopia (Kim et al., 2022). Table S1 provides a summary of clinical and epidemiological studies documenting phenotypic associations and comorbidity between the related psychiatric and ocular disorders. The directionality and biological basis of these associations remain unclear, likely reflecting differences in study design, population characteristics, diagnostic criteria, and the genetic‐environmental interplay influencing both neurodevelopment and vision.

Genetic factors contribute substantially to the etiology of neuropsychiatric and ocular disorders, especially in early‐onset forms (Demontis et al., 2019; Smoller et al., 2019; Tedja et al., 2019). Emerging evidence indicates shared molecular mechanisms between these domains; for instance, CTNND2 variants have been implicated in both myopia and several neuropsychiatric disorders (Lu, Aguilar, Li, Jiang, & Chen, 2016). Genome‐wide association studies (GWAS) reduce the influence of environmental confounders and enable quantitative assessment of shared heritability and genetic correlations across disorders. Their application has revealed broad genetic overlap and pleiotropy across traditionally separate disease domains (Anttila et al., 2018).

To decipher the genetic architecture underlying the observed comorbidities between neuropsychiatric and ocular disorders, especially for the early‐onset forms, we conducted the first comprehensive GWAS‐based analysis assessing genetic correlations across these disease domains. We adopted a developmental psychopathology perspective, allowing investigation of shared genetic mechanisms linking neurodevelopmental and ocular phenotypes across disorders that vary in age of onset but share overlapping etiological architecture. Specifically, we sought to: (1) identify their shared genetic structures; (2) evaluate whether these genetic correlations correspond to known clinical comorbidities and directionality; (3) Explore whether these genetic correlations are specific to early‐onset disorders; (4) Detect pleiotropic loci contributing to risk across both domains; and (5) Characterize the biological pathways underlying these shared genetic effects in silico.

Methods and materials

Research design overview

Our study specifically focuses on the genetic association between psychiatric diseases with neurodevelopmental origins and ocular diseases that are highly prevalent in children or adolescence. To systematically investigate their cross‐disease genetic architecture, we implemented a four‐stage analytical framework (Figure 1) comprising: (1) quantification of genetic overlap; (2) causal inference analysis; (3) identification of shared genetic loci; and (4) characterization of the implicated biological mechanisms. Subsequently, validation of the significant associations was carried out using the genetic risk score (GRS) approach in the independent ADHD cohort from Children's Hospital of Philadelphia (CHOP).

Figure 1.

Figure 1

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The schematic diagram of study design and workflow. ADHD, attention deficit hyperactivity disorder; AN, anorexia nervosa; ASD, autism spectrum disorder; BIP, bipolar disorder; MDD, major depressive disorder; OCD, obsessive compulsive disorder; SCZ, schizophrenia; SNV, single nucleotide variation; TS, Tourette's syndrome

Study cohorts

Selection of neuropsychiatric disorders was guided by four criteria: (1) evidence supporting neurodevelopmental origins; (2) presence of behavioral and/or cognitive dysfunction; (3) typical onset or high prevalence during childhood or adolescence; and (4) availability of large‐scale GWAS summary statistics with >2,000 cases and genome‐wide SNP coverage to ensure sufficient statistical power and reliability.

Accordingly, we included ADHD, ASD, and Tourette syndrome (TS), which are classical childhood‐onset neurodevelopmental disorders, as well as obsessive compulsive disorder (OCD) and anorexia nervosa (AN), which commonly emerge in later childhood or adolescence. In addition, we included major depressive disorder (MDD), bipolar disorder (BIP), and schizophrenia (SCZ). Although these disorders typically have age of onset in late adolescence or early adulthood (Kessler et al., 2007; Solmi et al., 2022; Thapar, Collishaw, Pine, & Thapar, 2012), they were included due to their increasingly recognized neurodevelopmental components (Anttila et al., 2018; Morris‐Rosendahl & Crocq, 2020; Thapar, Cooper, & Rutter, 2017), overlapping clinical features and comorbidities, and shared genetic architecture and signaling pathways (Anttila et al., 2018; Cross‐Disorder Group of the Psychiatric Genomics Consortium, 2019; Gandal et al., 2018).

We excluded neurological disorders, such as epilepsy, cerebellar ataxia, and other conditions characterized by primary structural, physiological, or vascular abnormalities of the nervous system that predominantly affect motor, sensory, coordination, or autonomic functions, rather than mood, cognition, or behavior.

The ocular phenotypes were selected based on their predominant onset during childhood and documented clinical, epidemiological associations with these psychiatric conditions (Table S1). Diseases of refractive error (myopia, hypermetropia, and astigmatism) as well as amblyopia and strabismus were therefore included.

GWAS summary statistics for the eight psychiatric diseases were obtained from large consortia and cohort resources, including the Psychiatric Genomics Consortium (PGC), iPSYCH, and national biobanks (Table S2). The datasets comprised the following: ADHD (Demontis et al., 2019; 19,099 cases/34,194 controls), ASD (Grove et al., 2019; 18,381/27,969), OCD (IOCDF‐GC and OCGAS, 2018; 2,688/7,037), AN (Watson et al., 2019; 16,992/55,525), TS (Yu et al., 2019; 4,819/9,488), MDD (Howard et al., 2019; 170,756/329,443), BIP (Mullins et al., 2021; 41,917/371,549), and SCZ (Lam et al., 2019; 33,640/43,456). Case and control status in each original study were determined via structured clinical assessments or medical‐record review following DSM‐III‐R, DSM‐IV, DSM‐5, or ICD‐8‐10 diagnostic criteria. In the UK Biobank subsets, psychiatric diagnoses were primarily self‐reported (Table S2).

GWAS summary statistics for ocular traits were obtained from the UK Biobank (Jiang, Zheng, Fang, & Yang, 2021) and included myopia (36,623 cases/419,031 controls), hypermetropia (18,730/436,924), strabismus (1,936/453,718), astigmatism (12,182/443,472), and amblyopia (3,694/451,960). Case definitions were based on participant self‐reports corroborated by ophthalmic health‐record data when available (Table S2).

All neuropsychiatric disorder datasets were downloaded from the Psychiatric Genomics Consortium (PGC; https://pgc.unc.edu/), and ocular trait data were retrieved from the GWAS Catalog (NHGRI‐EBI; https://www.ebi.ac.uk/gwas). Only GWAS summary statistics derived from participants of European ancestry were included to minimize population stratification. This genome‐wide pleiotropic association study adhered to the Strengthening the Reporting of Genetic Association Studies (STREGA) guidelines (Little et al., 2009).

The in‐house ADHD cohort for replication

The in‐house ADHD cohort was derived from the Center for Applied Genomics (CAG) at CHOP with detailed description in our previous publication (Yao et al., 2021). Briefly, inclusion criteria encompassed diagnostic codes for hyperkinetic syndrome of childhood, attention deficit disorders in the International Classification of Diseases (ICD‐9 314.x and ICD‐10 F90.x) and individuals with records of clinically recognized ADHD medications, including amphetamine derivatives, methylphenidate formulations, and non‐stimulant agents or adjunctive psychotropic medications.

Exclusion criteria of this ADHD cohort were designed to remove individuals with neurological, developmental, or medical conditions that might confound ADHD diagnosis. Cases were excluded if they exhibited ICD codes for intellectual disability (ICD‐9 317–319; ICD‐10 F70–F79), dementia or other mental disorders (ICD‐9 290.x, 294; ICD‐10 F01–F04, F06.0, F06.8), structural brain anomalies, traumatic brain injury, or encephalopathy (ICD‐9 348.x, 742.x, 800–804, 959.01; ICD‐10 G93.x, Q01–Q07, S02–S09), CNS infections, malignancies, neurofibromatosis, or neurocutaneous disorders (ICD‐9 006.5, 013.2, 191–192, 237.7; ICD‐10 A06.6, A17.81, C71–C72, Q85.0), or perinatal brain injury and birth trauma (ICD‐9 764‐767; ICD‐10 P05, P10‐P15). Additional exclusions included somatoform, stereotypic movement, and factitious disorders (ICD‐9 300.8x, 307.3x, 301.51; ICD‐10 F45.x, F68.12, F98.4).

Based on the above inclusion and exclusion criteria, ADHD cases were identified from the clinical and genotypic database of CAG at CHOP. Control participants were selected from the same biobank population and were required to have no documented diagnosis of ADHD or any other major neuropsychiatric disorder. In total, 2,726 individuals with ADHD and 16,299 controls aged 3–21 years (mean = 6 years) were included in the present analysis.

Analysis

We conducted our study in the following steps to characterize genetic overlap and causal mechanisms between neuropsychiatric and ocular traits:

Quality control filtering of the GWAS data

For the GWAS summary statistics of each disease, we removed the palindromic SNPs, variants that were not SNPs or were strand‐ambiguous, SNPs with duplicated rs numbers or missing values, and SNPs with minor allele frequency (MAF) < =0.01. The harmonization of the effect direction was performed to ensure that SNPs' effect on the ocular and neuropsychiatric phenotypes corresponded to the same allele.

Quantification of genetic correlation

Genome‐wide SNP‐based heritability and cross‐trait genetic correlations were estimated using linkage disequilibrium score regression (LDSC) (Bulik‐Sullivan et al., 2015) to quantify the overall shared genetic architecture. Local genetic correlations were assessed using ρ‐HESS (Shi, Mancuso, Spendlove, & Pasaniuc, 2017), which partitions the genome into 1,704 independent LD blocks to identify specific regions showing shared effects. Significance was determined after Bonferroni correction (p < .05/1,704).

Causality testing via mendelian randomization (MR)

Given the conceptual distinction between genetic correlation and directional causal inference (Appendix S1), we further evaluated potential causal relationships between trait pairs using a complementary approach, bidirectional two‐sample MR (Hemani et al., 2018). Independent SNPs (LD r 2 < .001, 10 Mb window) with GWAS significance (p < 5 × 10−8) served as instrumental variables; thresholds were relaxed when fewer than five instruments were available. The inverse‐variance weighted approach was the primary method for causal relationship estimation between diseases, with MR‐Egger regression and leave‐one‐out tests to assess horizontal pleiotropy and heterogeneity (Burgess & Thompson, 2017).

In‐house validation study for genetic correlation

To validate the reverse genetic correlation between ADHD and myopia, as well as astigmatism, we conducted a genetic risk score (GRS) analysis using an independent ADHD cohort from CHOP. For each participant, we computed GRSs representing genetic susceptibility to myopia and astigmatism.

The myopia GRS was derived from 19 index SNPs reaching genome‐wide significance in GWAS analyses. Because only two genome‐wide significant loci were available for astigmatism, the selection threshold was relaxed to include loci with p < 1 × 10−6, yielding a total of six index SNPs to ensure adequate statistical power. GRSs were calculated as the weighted sum of risk alleles carried by each individual, with weights corresponding to the SNP effect sizes (β values) obtained from UK Biobank GWAS summary statistics.

Two‐sided t‐tests compared mean GRS between ADHD cases and controls to examine whether ADHD status was associated with lower genetic load for myopia or astigmatism.

Identification of shared and novel loci

For disorder pairs showing significant genetic correlations, we conducted cross‐trait joint analyses using Multi‐Trait Analysis of GWAS (MTAG) (Turley et al., 2018). The analyses generated trait‐specific effect estimates for each SNP while preserving the effects across phenotypes. Genome‐wide significant signals (p < 5 × 10−8) were clustered into loci based on linkage disequilibrium (LD) structure and physical proximity. Loci not overlapping with any previously reported GWAS hits for the studied diseases in GWAS Catalog were defined as novel. For each novel locus, fine‐mapping of causal variants was performed using FINEMAP (Benner et al., 2016). SNPs with posterior probabilities >0.99, in LD (r 2 > 0.6) with the index SNP, and with p < 1 × 10−4 in the MTAG results were considered potential causal variants.

Functional and pathway annotation

Functional enrichment of significant SNPs was tested using GARFIELD (Iotchkova et al., 2019) to assess enrichment within regulatory elements, including open chromatin and histone modifications across brain and embryonic stem cell tissues. Bonferroni‐corrected significance threshold was defined as p < .05/1,005, with 1,005 being the number of annotations.

Candidate genes at each genome‐wide significant locus were prioritized using three complementary approaches: (1) positional mapping, based on the physical proximity of SNPs to genes; (2) expression quantitative trait loci (eQTL) mapping, linking cis‐eQTL variants to genes whose expression they regulate; and (3) chromatin interaction mapping (Hi‐C), identifying genes whose promoters physically interact with SNP‐containing regions. Expression datasets included EyeGEx (Goetz et al., 2020), PsychENCODE (Wang et al., 2018), BRAINEAC (Ramasamy et al., 2014), GTEx v8 (Bulik‐Sullivan et al., 2015) Brain and Nerve. Hi‐C data were derived from PsychENCODE, adult and fetal brain, and neural progenitor cells (Schmitt et al., 2016).

Gene‐set and tissue expression analyses were performed using MAGMA (De Leeuw, Mooij, Heskes, & Posthuma, 2015) on candidate genes at each genome‐wide significant locus, integrating GTEx v8 and BrainSpan (Miller et al., 2014) datasets to determine spatial and developmental specificity.

Biological pathway enrichment was further examined using KOBAS (Bu et al., 2021) with gene sets from MSigDB (Liberzon et al., 2011). p‐values were adjusted for multiple testing using the Benjamini–Hochberg method. To enhance the robustness of the enrichment results, Gene Ontology (GO) terms with background gene counts between 10 and 300 were retained for analysis.

Results

We performed a systematic investigation into the genetic link between neuropsychiatric and ocular disorders (Figure 1).

Both positive and inverse genetic relationships identified between neuropsychiatric disorders and ocular diseases

We assessed pairwise genetic correlations between eight neuropsychiatric disorders and five ocular phenotypes based on GWAS summary statistics (Table S3). We identified significant genetic correlations (false discovery rate < 0.05; |r g| > 0.1) between seven pairs of disorders (Figure 2A, Table S4). These correlations were primarily observed between childhood‐ or adolescent‐onset psychiatric disorders (ADHD, ASD, and OCD) and refractive‐error‐related ocular traits, whereas associations involving psychiatric disorders with onset in late adolescence or early adulthood (MDD, BIP, and SCZ) were relatively limited. Specifically, we found positive genetic correlations of ASD with both astigmatism and myopia, OCD with astigmatism, and MDD with hypermetropia. In contrast, ADHD showed negative genetic correlations with both astigmatism and myopia, and AN displayed an inverse correlation with astigmatism. These findings reveal a complex genetic architecture underlying neurodevelopmental and visual traits.

Figure 2.

Figure 2

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Genetic relationships between neuropsychiatric disorders and ocular disorders. (A) Overall genetic correlations (r g) between neuropsychiatric disorders and ocular disorders. The color and size of the circles indicate the direction and magnitude of the genetic correlation (r g). Red denotes positive correlations, and blue denotes negative correlations; greater color intensity and larger circle size correspond to stronger |r g| values. The red box highlights correlations that were replicated in an independent cohort. Asterisk signs * indicate FDR <0.05, ** represent FDR <0.01, and *** represent FDR <0.001. The horizontal arrow above the heatmap denotes the age‐at‐onset spectrum for each neuropsychiatric disorder. Childhood (early childhood‐onset: ADHD, ASD, TS), Adolescence (late childhood through adolescence‐onset: OCD, AN), and Adulthood (late adolescence to early adulthood‐onset: MDD, BIP, SCZ). As illustrated in the figure, genetic correlations with refractive error‐related ocular traits are predominantly observed for childhood‐ and adolescent‐onset neuropsychiatric disorders, whereas associations involving psychiatric disorders with onset in late adolescence or early adulthood were relatively limited. (B) Significant associations between genetically correlated neuropsychiatric disorders and ocular disorders in mendelian randomization analyses. The blue dots represent the point estimates of the causal effects, and the error bars indicate the corresponding 95% confidence intervals (CIs). (C–H) Plots comparing of the effect size and direction of lead SNPs at the genome‐wide significant loci from MTAG analyses between pairs of neuropsychiatric disorders and ocular disorders. The effect size of each SNP for the two diseases is plotted on the X‐axis and Y‐axis respectively. The regression line is shown in each plot. Due to the limited number of genome‐wide significant loci for ASD and astigmatism, the significance threshold was relaxed to p < 1 × 10−6. (C) Lead SNPs from ADHD (yellow) and astigmatism (pink); (D) ADHD (yellow) and myopia (purple); (E) ASD (orange) and myopia (purple); (F) ASD (orange) and astigmatism (pink); (G) AN from AN (brown) and astigmatism (pink); (H) MDD (blue) and hypermetropia (indigo). ADHD, attention deficit/hyperactivity disorder; AN, anorexia nervosa; ASD, autism spectrum disorder; BIP, bipolar disorder; MDD, major depressive disorder; OCD, obsessive compulsive disorder; SCZ, schizophrenia; TS, Tourette syndrome

We evaluated the local genetic correlations across the pairwise traits. Suggestive significance (p < .05) was observed for 16 pairwise traits (Table S5, Figures S1–S4), though none surpassed the significance threshold with Bonferroni correction (p < .05/1,704). These results suggested that the significant overall genetic correlation between diseases was likely attributed to shared genetic variations across the entire genome rather than in specific genomic regions. Notably, genes within these suggestively significant regions have been shown to play important roles in neuronal development. For instance, mutations in the ELFN1 and KCNC2 genes, identified in the ADHD‐myopia analysis, have been linked to developmental and epileptic encephalopathy (Schwarz et al., 2022; Tomioka et al., 2014).

Bidirectional MR analysis revealed four significant associations that survived Bonferroni correction (Figure 2B). We identified an inverse association between ADHD and both astigmatism and myopia. Additionally, protective effects were observed from MDD to myopia and from hypermetropia to BIP, although they share limited overall genetic architecture. This suggests that their association is more likely mediated by specific biological mechanisms rather than by widespread genetic pleiotropy. Our analysis showed no significant evidence of heterogeneity or horizontal pleiotropy (Figure S5). Leave‐one‐out analysis further confirmed the robustness of these findings, indicating that no single SNP disproportionately influenced the causal relationships between these pairs (Figure S6).

Guided by the significant genetic correlations identified between neuropsychiatric disorders and refractive error, we conducted joint analyses for the seven disorder pairs that showed significant overall genetic correlations using the MTAG approach. This analysis revealed 124 genome‐wide significant loci (Table S6). The directional effects of these loci were consistent with the overall genetic correlations observed between each disorder pair (Figure 2C–H), with ADHD and myopia/astigmatism showing opposing effects at their associated loci.

Lower genetic risk of astigmatism and myopia among children with ADHD compared to controls

We further investigated the aggregated inverse genetic correlation between ADHD and myopia/astigmatism. This was performed in an independent cohort of 2,726 ADHD cases and 16,299 controls. We computed the GRS of myopia based on the 19 genome‐wide significant myopia SNPs (Table S6) for each individual in the validation cohort. The average GRS for myopia was significantly lower among the ADHD cases compared to that among the controls (0.294 vs. 0.396, p‐value = 0.019). As there are only two genome‐wide significant SNPs for astigmatism, we calculated the GRS of astigmatism based on the six SNPs with p‐value <1 × 10−6 (Table S7). The average astigmatism GRS was similarly lower among ADHD patients than controls (0.0429 vs. 0.0436, p‐value = 0.024). These findings from the independent cohort validated the inverse correlation between ADHD and both myopia and astigmatism.

Novel genome‐wide significant loci identified for ADHD and astigmatism

Our analysis uncovered novel loci for the childhood‐onset disorders under study. We uncovered one novel locus for ADHD (from the joint analysis of ADHD‐myopia using MTAG) and two novel loci for astigmatism (one from the joint analysis of ADHD‐astigmatism and another one from the joint analysis of ASD‐astigmatism) (Table 1, Figure 3, Table S8). The novel ADHD locus was at 14q23.3 indexed by SNP rs8022529. The novel loci for astigmatism were located at 5p12 with lead SNP rs10941690 and 1p31.1 with lead SNP rs61765620, respectively. Conditional analyses did not identify any additional independent signals at these loci.

Table 1.

Novel genome‐wide significant loci for astigmatism and ADHD identified in pairwise joint analyses using the MTAG approach (two‐sided p‐value < 5 × 10−8)

Trait Pairwise trait Lead SNP Chr: Pos EA OA EAF Beta p Gene
ADHD Myopia rs8022529 14:63026228 G T 0.430 0.034 3.06E‐08 KCNH5(a)
Astigmatism ADHD rs10941690 5:44974812 A G 0.191 0.014 4.48E‐08 MRPS30(c), NNT(d), HCN1(d), PAIP1(d)
Astigmatism ASD rs61765620 1:72662347 G T 0.468 −0.011 2.22E‐08 NEGR1(a,b,c,d), ZRANB2(d)
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Gene, the gene mapped via causal variants position (a), and lead variants position (b), eQTL (c), or Hi‐C (d).

ADHD, attention deficit/hyperactivity disorder; ASD, autism spectrum disorder; beta, effect size estimate; Chr: Pos, chromosome: position; EA, effect allele; EAF, effect allele frequency; OA, other allele; p, two‐sided p‐value; SNP, single‐nucleotide polymorphism.

Figure 3.

Figure 3

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Circular Manhattan plots from joint analyses via MTAG identifying novel genome‐wide significant loci and corresponding regional association plots. (A–C) Circular Manhattan plots. The arcs in the outer layer represent chromosome positions and the color represent the number of significant SNPs at each position. Each dot in the inner layer represents a SNP: red dots denote genome‐wide significant variants, and green dots denote suggestively significant variants. (A) The Manhattan plot for ADHD (middle gray layer) and myopia (inner gray layer). (B) The Manhattan plot for ADHD (middle gray layer) and astigmatism (inner gray layer). (C) The Manhattan plot for ASD (middle gray layer) and astigmatism (inner gray layer). (D–F) Novel genome‐wide significant loci and their corresponding regional association plots. In these plots, each dot represents a SNP. The y‐axis indicates the −log10 p‐values, with higher values representing a more reliable association of the loci with the combined traits. The purple dot denotes the lead SNP, which is positioned at the highest point on the y‐axis. This represents the most statistically significant loci within that chromosomal region and serves as the core signal that drives the observed association peak. The colors of the surrounding dots reflect their degree of linkage disequilibrium (LD) with the lead SNP: red indicates high LD, meaning these variants tend to be inherited together with the lead SNP, while blue indicates a relatively independent genetic relationship. (D) The novel locus associated with ADHD from MTAG analysis of ADHD and myopia. (E) The novel locus associated with astigmatism from MTAG analysis of ADHD and astigmatism. (F) The novel locus associated with astigmatism from MATG analysis of ASD and astigmatism

We next performed fine‐mapping and uncovered 3 potential causal variants (Table S9). These variants overlapped with active histone marks (H3K4me1 and H3K9me3) in brain regions implicated in the pathophysiology of neuropsychiatric disorders and refractive errors, including the frontal cortex and substantia nigra, as well as in neural progenitor tissues involved in neurodevelopment and differentiation (Figure S7). These findings suggest that the identified variants may exert regulatory effects on gene expression relevant to the development of ADHD, ASD, and refractive error.

To prioritize target genes, we integrated positional mapping, eQTL, and Hi‐C chromatin interaction data. The novel ADHD locus at 14q23.3 lies in an intergenic region near gene KCNH5, a gene involved in regulating neurotransmitter release and neuronal excitability (Bauer & Schwarz, 2018). The locus for astigmatism at 1p31.1 was located in the intronic region of NEGR1, a gene implicated in synapse assembly and brain development (Sanz, Ferraro, & Fournier, 2015; Zhang et al., 2025). The locus at 5p12 was mapped to multiple genes including MRPS30, HCN1, PAIP1, and NNT, which are highly expressed in brain tissues (Uhlén et al., 2015). The known neurobiological roles of these candidate genes provide converging evidence for pleiotropic mechanisms linking neuropsychiatric and ocular phenotypes.

Significant enrichment of pleiotropic SNPs and genes in developing brain

Epigenetic annotation revealed that genome‐wide significant SNPs from the joint analyses were enriched in regulatory regions active during brain development (Table S10). SNPs identified from the ADHD–refractive errors joint analysis were overrepresented in H3K36me3‐marked regions, an epigenetic signature implicated in structural and functional maturation of the central nervous system during early development (Shen et al., 2018). Similarly, variants from the MDD‐refractive error analysis showed enrichment in multiple histone‐modified regions essential for brain and retinal development, consistent with their roles in neural differentiation (Dai et al., 2022; Zenk et al., 2024). These findings suggest that shared loci may influence disease susceptibility through epigenetic regulation of developmental genes in the nervous system.

Genes jointly associated with neuropsychiatric and refractive error‐related traits showed peak expression during early brain development (Figure 4; Tables S11–S13), underscoring the contribution of disrupted neural signaling and ocular neurodevelopment during this critical period. Tissue‐specific expression further confirmed enrichment of refractive error‐associated genes across cerebellum, frontal cortex, and anterior cingulate cortex—regions important for ADHD and ASD pathogenesis (Castellanos & Proal, 2012; Stoodley, 2016). Additional enrichment in the pituitary and hypothalamus for MDD‐ and myopia‐related genes implicates the hypothalamic–pituitary–adrenal axis and dopaminergic regulation in both conditions (Brown et al., 2022; Ugrumov, 2024). MAGMA gene‐set analyses indicated that genes shared by ASD and refractive errors were enriched in apoptosis‐related pathways, whereas those shared by MDD and refractive errors were involved in synaptic structure and function (Table S14).

Figure 4.

Figure 4

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Tissue and developmental stage enrichment analyses of significant loci. Each row indicates the enriched tissue (A–D) or developmental stages (E–G). The red dashed line indicates the reference line for the significant p‐value thresholds with multiple‐testing adjustment. The results demonstrated that the loci shared by ADHD (A), AN (B), MDD (C), ASD (D), and refractive error were significantly enriched in various brain regions. Furthermore, the loci shared by ADHD (E), AN (F), MDD (G), and refractive error were significantly enriched during the early developmental stages

Pathway enrichment analysis of genes across the 124 genome‐wide significant loci identified robust associations with key biological processes, including epigenetic and chromatin remodeling, neuronal development and function, metabolic and redox regulation, and immune modulation (Table S15). Of particular interest, loci shared between childhood‐onset psychiatric disorders ADHD and ASD and refractive errors were significantly overrepresented in pathways related to neurodevelopment and neuronal function, such as the regulation of synaptic plasticity, brain development, and neuronal differentiation and projection (Figure 5; Table S16). These findings emphasize the central role of neurodevelopmental processes in both early‐onset psychiatric and refractive phenotypes. In addition, genes in the visual perception pathway were shared between childhood‐onset psychiatric disorders and refractive errors, suggesting that alterations in early neurodevelopmental programs affecting visual circuit formation may also influence broader neural networks implicated in cognition and emotion. It has been reported that daylight exposure, a critical environmental regulator of visual system maturation, has direct regulatory effects on mood, cognition, alertness, performance, and sleep (Lazzerini Ospri, Prusky, & Hattar, 2017; Wirz‐Justice, Skene, & Münch, 2021). These findings suggest the intricate interplay between genetic and environmental factors may influence the structure and function of brain regions engaged in vision and cognition, shaping both neuropsychiatric and visual traits.

Figure 5.

Figure 5

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Pathway enrichment analysis of candidate genes at genome‐wide significant loci for ADHD and ASD. The x‐axis shows the proportion of genes contributed by joint analyses relative to total genes in the pathway. Biological processes are ranked on the y‐axis in descending order by gene ratio. Dot size corresponds to the number of enriched genes; color intensity indicates the minus logarithm of FDR p‐value. The background colors of the bars on the left indicate three functional modules clustered from the most significantly enriched pathways. Green indicates visual perception (including photoreceptor cell maintenance and visual perception). Orange indicates epigenetic regulation and metabolism (including the cellular response to glucose starvation and the negative regulation of protein ubiquitination). Blue indicates neural development and function (including positive regulation of neuronal differentiation, regulation of synaptic plasticity, and brain development, among others)

Collectively, these convergent enrichment analyses highlight a central role of early neurodevelopmental processes, including epigenetic modification, neuronal differentiation, and synaptic organization, in the molecular architecture linking childhood‐onset neuropsychiatric and ocular traits. These findings suggest that genetic variation within shared developmental pathways, particularly those shaping early brain and visual system formation, may interact with environmental influences to affect neural, cognitive, and sensory outcomes during critical windows of development.

Discussion

Our study represents an initial step in unraveling the complex relationships between neuropsychiatric and ocular disorders with a high prevalence in childhood and adolescence by examining their genetic underpinnings. Through analysis of shared genetic architecture, we revealed both concordant and divergent genetic influences across neuropsychiatric and vision‐related traits, particularly for the childhood‐onset neurodevelopmental disorders ADHD and ASD.

Genetic correlations between childhood‐onset neuropsychiatric disorders and refractive errors

In this study, we identified significant positive genetic correlations between ASD and both astigmatism and myopia. Conversely, a significant inverse genetic correlation was identified between ADHD and both myopia and astigmatism, a finding that was subsequently confirmed in an independent cohort.

Previous clinical researches on the associations between ASD/ADHD and refractive errors have produced inconsistent results. For ASD, Anketell, Saunders, Gallagher, Bailey, & Little (2016) reported no correlation with spherical refractive error but a positive association with astigmatism. Similarly, Wang et al. (2018) reported no link to refractive error, whereas Chang et al. (2019) reported a significant association. The evidence for ADHD has been similarly inconsistent. Several studies reported a positive correlation with refractive error and axial length—a key biometric parameter of myopia (Ayyildiz & Ayyildiz, 2019; Chou et al., 2023), whereas others detected no significant association (Bellato et al., 2022; López‐Hernández, Miquel‐López, García‐Medina, & García‐Ayuso, 2024). These discrepancies likely arise from two major factors: the challenge of obtaining reliable refractive error measurements in children with ASD/ADHD due to limited cooperation, and methodological heterogeneity across studies in design, sample size, age distribution, and ancestry composition.

Our genetic analyses, which are less susceptible to environmental confounding present in prior clinical studies, reveal significant genetic correlations. These correlations provide a foundation for exploring the shared molecular basis underlying the phenotypic trajectories of childhood‐onset neuropsychiatric and visual conditions.

Shared neural pathways in mental health and eye conditions

Our analyses identified novel gene associations for ADHD and astigmatism that are involved in regulating neurodevelopment and neuronal signaling within both central and ocular nervous systems. KCNH5, is predominantly expressed in excitatory neurons of the cerebral cortex, thalamus, and retina (Uhlén et al., 2015). It encodes a voltage‐gated potassium channel that modulates neuronal excitability and cortical electrophysiology (Bauer & Schwarz, 2018). Previous studies have linked it to epilepsy, various neurodevelopmental disorders, and visual development (Happ et al., 2023; Katayama, Kumamoto, Wada, Hanashima, & Ohtaka‐Maruyama, 2024; Yu, Liu, Xia, Feng, & Chen, 2024).

The NEGR1 gene encodes a synaptic cell adhesion molecule involved in neuronal growth, connectivity, and synapse formation (Sanz et al., 2015). It has been linked to multiple neuropsychiatric and cognitive traits, including MDD (Deng et al., 2022), intelligence (Sniekers et al., 2017), and neurodegenerative disease (Flores‐Dorantes, Díaz‐López, & Gutiérrez‐Aguilar, 2020). Experimental studies also suggest that NEGR1 influences retinal differentiation from embryonic stem cells (Mao et al., 2022), indicating a potential developmental convergence between neural and ocular pathways.

HCN1 encodes a hyperpolarization‐activated cyclic nucleotide‐gated channel and is highly expressed in both the brain and retinal photoreceptors (Benarroch, 2013; Demontis et al., 2002). HCN1 plays a key role in regulating neuronal excitability and visual signaling (Bleakley et al., 2021; Zhao et al., 2023) and has been associated with developmental and epileptic encephalopathy, intellectual disability, and ASD (Bonzanni et al., 2018; Marini et al., 2018).

Further integrative analyses revealed enrichment of pleiotropic genetic variants in biological processes related to neural signal transduction and neuronal differentiation, particularly in the cerebellum, frontal cortex, and anterior cingulate cortex during embryonic and early postnatal periods. These pleiotropic variants may exert transcriptional and epigenetic regulatory effects that influence neurodevelopmental and neuronal functions. Such mechanisms provide a plausible interface between genetic predisposition and environmental influences during sensitive stages of development.

Potential clinical guidance from genetic links for neuropsychiatric disorders and refractive error

The relationship between ADHD treatments and refractive error development presents an intriguing clinical consideration. First‐line ADHD treatments, including methylphenidate and selective norepinephrine reuptake inhibitors (SNRIs), have been implicated in the onset or progression of myopia (Brown et al., 2022; Pagán, Huizar, Short, Gotcher, & Schmidt, 2023; Wolraich et al., 2019). Although several studies have explored the effects of ADHD medications on refractive outcomes (López‐Hernández et al., 2024), the evidence remains inconsistent. Such discrepancies may reflect heterogeneity in population characteristics, diagnostic definitions of refractive error, treatment duration, or underlying genetic variation between study cohorts. A better understanding of genetic loci exerting opposing pleiotropic influences on ADHD and refractive traits could provide critical insights for preventing visual impairment in children receiving ADHD medication and inform the development of individualized therapeutic strategies that minimize adverse visual effects.

Children with ASD represent another clinically vulnerable population, characterized by developmental delays and social communication challenges. The diagnosis of refractive error such as myopia and astigmatism in these children is often difficult, as accurate assessment requires sustained attention, communication, and cooperation, skills that may be compromised in ASD. Consequently, visual problems in this group are frequently under‐detected and undertreated. Our finding of positive genetic correlations between ASD and refractive errors has important clinical implications. Because early identification and intervention are critical for both ASD management and visual development, these results underscore the need for heightened attention to eye health in children with ASD and support the inclusion of routine ophthalmologic screening and adequate outdoor activities as part of comprehensive care strategies.

Emerging evidence indicates that refractive error is closely linked to neural processes. Studies have shown that myopia development involves abnormalities in retinal ganglion and dopaminergic amacrine cells, alongside dysregulated neural gene expression (Hysi et al., 2020). Our findings of a shared genetic architecture between refractive error and neuropsychiatric disorders further support the potential of integrating genetic and neurobiological data to inform preventive and therapeutic strategies for children at risk of visual or psychiatric conditions.

Strengths and limitations of this study

Our study represents the first comprehensive investigation of genetic links between neuropsychiatric and ocular disorders using GWAS data, an approach that inherently reduces the bias and confounding factors common in traditional epidemiological studies. However, several limitations warrant consideration. First, our analysis was restricted to individuals of European ancestry, necessitating future studies in diverse ethnic populations. Second, the reliance on self‐reported data for some variables and the inclusion of adult phenotypic data. Third, the absence of genetic correlations in some disease pairs might be attributed to limited statistical power, particularly for ocular traits with smaller sample sizes or restricted instrumental variables for MR analysis. Absence of individual‐level age data also prevented evaluation of age‐dependent genetic effects. Nevertheless, the negative genetic association between ADHD and myopia/astigmatism, validated in our pediatric cohort, supports the relevance of these findings to childhood populations. Future studies incorporating diverse ancestries, longitudinal data, and functional experiments will be crucial for elucidating developmental mechanisms and confirming the causal roles of pleiotropic loci in both neuropsychiatric and ocular pathogenesis.

Conclusion

This study provides an important first step toward elucidating the biological relationships between neuropsychiatric and ocular disorders through genetic analyses. We identified a shared genetic architecture and overlapping functional pathways between neuropsychiatric disorders and refractive errors, particularly for ADHD and ASD, involving neural signal transduction and early neuronal development in the cerebellum and cortex. These findings advance understanding of the neurogenetics linking brain and eye traits and may inform future genomics‐guided prevention and therapeutic strategies for neurodevelopmental and visual disorders.

Ethical considerations

The summary statistics of GWAS datasets used in this study were publicly available and ethical approval was acquired for all those original studies. The independent ADHD cohort was approved by the Institutional Review Board of Children's Hospital of Philadelphia (IRB 16‐013278; approved August 3, 2017). Written informed consent was obtained from all participants or their parents/legal guardians, as appropriate.

Key points.

What's known?

  • While clinical and epidemiological studies suggest connections between neuropsychiatric and ocular disorders, the nature and direction of these relationships remain unclear.

What's new?

  • Through cross‐trait analyses of GWAS data, we revealed both positive and inverse genetic relationships between neuropsychiatric disorders and refractive error, most notably in the neurodevelopmental disorders ADHD and ASD.

  • We identified shared genetic loci and enriched gene expression patterns in early neurodevelopmental processes, involving epigenetic modification and synaptic plasticity; related genetic variation may interact with environmental influences to affect neural, cognitive, and sensory outcomes during critical windows of development.

What's relevant?

  • Understanding genetic loci with consistent or opposing pleiotropic effects on ADHD, ASD, and refractive error could inform comprehensive eye care strategies for preventing vision impairment in these vulnerable populations.

Supporting information

Appendix S1. Rationale for integrating genetic correlation and Mendelian randomization analyses.

Table S1. Literature support for comorbidity or association of the selected psychiatric diseases and ocular disorders.

Table S2. Information of the genome‐wide association study data sets included in our study.

Table S3. Genetic parameter estimates from GWAS summary statistics for each study cohort.

Table S4. Overall genetic correlation between pairs of diseases.

Table S5. Suggestive significant associations identified by local genetic correlation analysis using ρ‐HESS.

Table S6. Genome‐wide significant loci from pairwise joint analysis using MTAG approach.

Table S7. Genetic loci with p‐value <1 × 10−6 from joint analysis between astigmatism and ADHD.

Table S8. The novel genome‐wide significant loci (p < 5 × 10−8) from pairwise joint analysis using MTAG approach.

Table S9. Candidate causal variants at each novel locus identified by FINEMAP approach.

Table S10. The results of functional enrichment analysis for epigenetic modification using GARFIELD approach (p‐value < 0.05/1,005).

Table S11. Tissue‐specific enrichment of MTAG‐GWAS loci across GTEx v8 tissues. Significant associations were determined after Bonferroni correction for 54 tested tissues (p < .05/54).

Table S12. Tissue enrichment analysis of MTAG‐GWAS across 29 developmental stages in BrainSpan. Significant associations are shown after Bonferroni correction for 29 age points (p < .05/29).

Table S13. Enrichment of MTAG‐GWAS signals across 11 general developmental stages in BrainSpan. Significant associations are shown after Bonferroni correction (p < .05/11).

Table S14. MAGMA gene‐set enrichment analysis of target genes from shared genetic loci between neuropsychiatric disorders and refractive error.

Table S15. Pathway enrichment analysis of all candidate genes at 124 genome‐wide significant loci in our study.

Table S16. Pathway enrichment analysis of candidate genes at genome‐wide significant loci for psychiatric disorders at different age stages in our study.

Figure S1. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, MDD, ASD, AN and strabismus.

Figure S2. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, MDD, AN, ADHD and hypermetropia.

Figure S3. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, MDD, AN, ADHD and myopia.

Figure S4. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, AN, ADHD and astigmatism or amblyopia.

Figure S5. Mendelian randomized (MR) scatter plot.

Figure S6. MR leave‐one‐out sensitivity analysis.

Figure S7. Overlap of candidate causal SNPs with histone marks in different brain regions.

JCPP-67-1460-s001.docx (2.7MB, docx)

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant no.: 81770956, 82572080), Project of Tianjin 131 Innovative Talent Team (grant no.: 201936), the Science and Technology Fund for Health of Tianjin (grant no.: TJWJ2023ZD008), Tianjin Key Medical Discipline Construction Project (grant no.: TJYXZDXK‐3‐004A‐3, TJYXZDXK‐040A), and Tianjin Outstanding Health Professional Selection and Training Program (grant No. TJSJMYXYC‐D2‐031). The authors gratefully acknowledge all participants enrolled in the study at CAG, CHOP. They thank the High‐Performance Computing Platform at Tianjin Medical University, National Supercomputer Center of Tianjin for technical support. The authors have declared that they have no competing or potential conflicts of interest.

Conflict of interest statement: No conflicts declared.

Contributor Information

Hakon Hakonarson, Email: hakonarson@chop.edu.

Jin Li, Email: jli01@tmu.edu.cn.

Xuefeng Shi, Email: shixf_tmu@163.com.

Data availability statement

The information of the summary statistics data of each GWAS cohort is presented in Table S2, including the related literature information. All the summary GWAS statistics data of the neuropsychiatric disorders were downloaded from the Psychiatric Genomics Consortium (PGC) website (https://www.med.unc.edu/pgc/), and the GWAS summary data of ocular disorders were downloaded from the GWAS catalog (https://www.ebi.ac.uk/gwas).

The in‐house ADHD cohort was derived from the Center for Applied Genomics (CAG) at CHOP with detailed description in our previous publication (Yao et al., 2021). Reasonable requests of CHOP ADHD cohort data should be addressed to Dr. Hakon Hakonarson.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1. Rationale for integrating genetic correlation and Mendelian randomization analyses.

Table S1. Literature support for comorbidity or association of the selected psychiatric diseases and ocular disorders.

Table S2. Information of the genome‐wide association study data sets included in our study.

Table S3. Genetic parameter estimates from GWAS summary statistics for each study cohort.

Table S4. Overall genetic correlation between pairs of diseases.

Table S5. Suggestive significant associations identified by local genetic correlation analysis using ρ‐HESS.

Table S6. Genome‐wide significant loci from pairwise joint analysis using MTAG approach.

Table S7. Genetic loci with p‐value <1 × 10−6 from joint analysis between astigmatism and ADHD.

Table S8. The novel genome‐wide significant loci (p < 5 × 10−8) from pairwise joint analysis using MTAG approach.

Table S9. Candidate causal variants at each novel locus identified by FINEMAP approach.

Table S10. The results of functional enrichment analysis for epigenetic modification using GARFIELD approach (p‐value < 0.05/1,005).

Table S11. Tissue‐specific enrichment of MTAG‐GWAS loci across GTEx v8 tissues. Significant associations were determined after Bonferroni correction for 54 tested tissues (p < .05/54).

Table S12. Tissue enrichment analysis of MTAG‐GWAS across 29 developmental stages in BrainSpan. Significant associations are shown after Bonferroni correction for 29 age points (p < .05/29).

Table S13. Enrichment of MTAG‐GWAS signals across 11 general developmental stages in BrainSpan. Significant associations are shown after Bonferroni correction (p < .05/11).

Table S14. MAGMA gene‐set enrichment analysis of target genes from shared genetic loci between neuropsychiatric disorders and refractive error.

Table S15. Pathway enrichment analysis of all candidate genes at 124 genome‐wide significant loci in our study.

Table S16. Pathway enrichment analysis of candidate genes at genome‐wide significant loci for psychiatric disorders at different age stages in our study.

Figure S1. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, MDD, ASD, AN and strabismus.

Figure S2. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, MDD, AN, ADHD and hypermetropia.

Figure S3. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, MDD, AN, ADHD and myopia.

Figure S4. Suggestive significant local genetic correlation, genetic covariance and SNP heritability between SCZ, AN, ADHD and astigmatism or amblyopia.

Figure S5. Mendelian randomized (MR) scatter plot.

Figure S6. MR leave‐one‐out sensitivity analysis.

Figure S7. Overlap of candidate causal SNPs with histone marks in different brain regions.

JCPP-67-1460-s001.docx (2.7MB, docx)

Data Availability Statement

The information of the summary statistics data of each GWAS cohort is presented in Table S2, including the related literature information. All the summary GWAS statistics data of the neuropsychiatric disorders were downloaded from the Psychiatric Genomics Consortium (PGC) website (https://www.med.unc.edu/pgc/), and the GWAS summary data of ocular disorders were downloaded from the GWAS catalog (https://www.ebi.ac.uk/gwas).

The in‐house ADHD cohort was derived from the Center for Applied Genomics (CAG) at CHOP with detailed description in our previous publication (Yao et al., 2021). Reasonable requests of CHOP ADHD cohort data should be addressed to Dr. Hakon Hakonarson.

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