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The genetic overlap and causal relationship between attention deficit hyperactivity disorder and obstructive sleep apnea: a large-scale genomewide cross-trait analysis

BMC Psychiatry volume 25, Article number: 454 (2025) Cite this article

Abstract

Background

Attention deficit hyperactivity disorder (ADHD) and Obstructive sleep apnea (OSA) are highly clinically co-occurring, but the mechanisms behind this remain unclear, so this article analyzes the reasons for the co-morbidities from a genetic perspective.

Methods

We examined the genetic architecture of ADHD and OSA based on the large genome-wide association studies (GWAS). The global genetic relationship between OSA and ADHD was explored. Cross-trait analysis from single nucleotide polymorphism (SNP) and gene level was performed subsequently to detect the crucial genomic regions. Finally, we revealed the anatomical change on which genetic overlap relies and further explored whether genetic factors exert a causal effect.

Results

After using both linkage disequilibrium score regression (LDSC) and High-definition likelihood inference (HDL) methods, we identified a significant genetic correlation between OSA and ADHD (PLDSC = 2.45E-28, PHDL = 1.09E-25), demonstrating a consistent direction. Furthermore, through the application of various cross-trait methods, we pinpointed 5 loci and 57 genes involved in regulating the co-occurrence of these disorders. These genetic regions were thought to be associated with the prefrontal lobes (P = 3.07E-06) and the nucleus accumbens basal ganglia (P = 2.85E-06). Lastly, utilizing Mendelian randomization (MR), we established a link indicating that individuals with ADHD were at an elevated risk of developing OSA (PIVM = 0.02, OR (95%CI):1.09 (1.01–1.17)).

Conclusions

This study reveals a strong genetic correlation between ADHD and OSA. It offers insights for future drug target development and sleep management in ADHD.

Peer Review reports

Introduction

Attention deficit hyperactivity disorder (ADHD) is the most common mental developmental disorder in children, affecting 5.29-7.1% of the population [1]. It is clinically characterized by hyperactivity and inattention, which also affects cognition and intelligence [2]. Due to its high prevalence and the negative consequences into adulthood, we need to pay more attention to the risk factors that affect its prognosis and severity.

Obstructive sleep apnea (OSA) is a sleep-breathing disorder that affects up to 9.5% of children [3], and it is thought to cause cardiovascular disease [4], diabetes [5], and neurocognitive deficits [6]. There is increasing interest in its pathogenesis and health effects, and ADHD has been shown to be associated with OSA in children in previous retrospective studies [7, 8], its prevalence is 20–30% in adolescents with ADHD [9], OSA in middle and late childhood can lead to inattention and learning difficulties [10], a study performed by Ahmadi et al. evaluated children with ADHD achieved 69% remission after adenotonsillectomy [11]. Other sleep difficulties are also present in ADHD and medications often used to treat ADHD and psychiatric comorbidities which can have a direct effect on sleep and neurocognitive functioning. The results of OSA and ADHD were inconsistent, some studies have not found a link between ADHD and OSA [12, 13], Puzino K et al. concluded that OSA did not affect the severity of ADHD [14], the differences in the diagnosis of ADHD and the measurement of OSA in each study may affect the results. The comorbidities of ADHD also result in considerable functional and psychosocial impairments. They also worsen symptom progression, course, and outcome. It is important to elucidate the mechanisms between ADHD and OSA.

Disease development is determined by a combination of environmental and genetic factors [15], and genes play an important role in the etiology of ADHD and the development of co-morbidities in other diseases [16]. Abnormal sleep structure may be a marker of mental illness [17]. An improved understanding of this relationship is important as it may have clinical implications. The genetic loci associated with OSA traits have been identified through GWAS [18], however, its individualized genetic architecture is still unclear. BMI and sex were found not the only genetic driver of OSA after sensitivity testing [19].

Genetic power is derived from multiple loci, and in contrast to previous studies of individual genetic variants, GWAS examine millions of common genetic variants to test whether effective alleles are associated with a trait, thus providing an unbiased approach. Genetic analysis helps to study the intrinsic link between the two traits, reducing the influence of confounding factors. Finally, the revelation of genetic structure facilitates us to demonstrate the biological mechanisms by which ADHD and OSA influence each other dependently.

In order to elucidate the genetic background behind ADHD and OSA leading to the emergence of their co-morbidities, we (I) estimated their genetic correlations, (II) conducted cross-trait analyses from the SNP to the gene level to identify common genetic factors, and (III) tested for a causal role of ADHD on OSA using Mendelian randomization (MR).

Methods

The GWAS summary data utilized for this study were derived from publicly available datasets. The GWAS for ADHD were obtained from the Psychiatric Genomics Consortium (https://pgc.unc.edu/), comprising 38,691 cases and 186,843 controls. Summary GWAS for OSA comprising 38,998 cases and 336,659 controls, were obtained from FinnGen consortium R9 (https://www.finngen.fi/en/access_results). This study is based on quality-controlled GWAS summary data for subsequent analyses, with SNP selection adhering to the genome-wide significance threshold and linkage independence criteria [20]. The diagnosis of ADHD was based on ICD-10 codes (F90.0, F90.1, F98.8) and conducted through assessments by psychiatrists, leading to the identification of 27 genome-wide loci and 76 potential risk genes independently associated with ADHD. For OSA, the diagnosis relies on ICD codes from the Finnish National Hospital Discharge Registry and the Causes of Death Registry, incorporating subjective symptoms, clinical examination, and sleep registration. The diagnosis emphasizes an Apnea-Hypopnea Index (AHI) ≥ 5 events per hour or a Respiratory Event Index (REI) ≥ 5 events per hour. The samples used were all from European populations, and there was no sample overlap. During data cleaning, we first selected data from the 1000 Genomes European population and retained only common variants (i.e., minor allele frequency [MAF] > 0.01) with bi-allelic variations. To ensure the accuracy and consistency of the data, we excluded records with missing rsIDs and removed duplicate rsID entries. Furthermore, to enhance data completeness, we filled in missing information on chromosome positions and other attributes based on the hg19 reference genome. Figure 1 showed the overview of research of shared genetic architecture between ADHD and OSA.

Fig. 1
figure 1

Overview of research of shared genetic architecture between ADHD and OSA

Full size image

General genetic correlation

We conducted linkage disequilibrium score regression (LDSC) analysis to estimate the heritability (h2) of single-trait [21] and genetic correlation [22] (rg) between OSA and ADHD (ranging from 0.1 to 1). It can measure the average sharing of genetic effects across the entire genome. The standard error of its genetic correlation estimation applied by LDSC is significantly larger than that of the restricted maximum likelihood method (REML). We further used High-definition likelihood inference [23] (HDL) to calculate the rg which can effectively address these shortcomings. We performed HDL using R package HDL-v1.4.0 (https://github.com/zhenin/HDL), taking 1,029,876 well-imputed HapMap3 SNPs as reference panel (https://github.com/zhenin/HDL/wiki/Reference-panels).

Multi-traits analysis

SNP level analysis

To explore the genetic overlap influencing both traits, we employed the Cross -Phenotype Association [24] (CPASSOC) algorithm which integrates GWAS for various traits, minimizes heterogeneity, and identifies variation associated with multiple traits across studies. SHom extends the linear combination of univariate test statistics, but its statistical power diminishes in the presence of between-study heterogeneity for the fixed-effects meta-analysis. Shet extends SHom, exhibiting enhanced efficacy by accommodating heterogeneous effects for traits from different studies as well as heterogeneous effects across phenotypes. Significant SNPs are defined as having P-single trait < 1E-3 and P-CPASSOC < 5E-8, the potential new loci are defined as having correlation in a single trait but not being significant (5E-8 < P < 1E-3), but become highlighted after CPASSOC [25].

The polytropic SNPs from CPASSOC may be (I) associated only with ADHD, (II) associated only with OSA, and (III) associated with both. We utilized colocalization (COLOC) based on a bayesian algorithm to determine if two different traits share the same genetic information in a given region [26]. This tool generates posterior probabilities for five mutually exclusive hypotheses related to the sharing of causal variants in a genomic region, including H0 (no association), H1 or H2 (association to one trait only), H3 (association to both traits, two genomic regions), and H4 (association to both traits, one genomic region). Following the discovery of the pleiotropic SNP, we used Functional Mapping and annotation [27] (FUMA) to locate independent genomic loci and provide information about the SNP’s functional categories, CADD scores, RegulomeDB scores, and chromatin states. In each locus, the SNP with the smallest P value was identified as the topSNP. Independent SNPs and significant SNPs were defined as SNPs with distances from topSNPs of r2 < 0.01 and r2 < 0.06, respectively. We extracted summary statistics for variants within 500 kb of the topSNP at each shared locus and calculated the posterior probability for H4 (PPH4) and H3 (PPH3). A locus was considered colocalized if PPH4 or PPH3 was greater than 0.7 [26, 28].

Genetic level analysis

Understanding the role of genetic factors in the occurrence of co-morbidities is more challenging with single genetic locus variants. Therefore, we utilized FUMA software to perform physical annotation for SNPs located in the same or nearby genomic regions and integrate them with genes. We also performed a calculation of the significance of gene- and trait-based associations using Multi-marker Analysis of GenoMic Annotation [29] (MAGMA). This algorithm utilizes multiple linear principal component regression models based on the 1000 Genomes project European samples 3 references to aggregate the effects of multiple weak SNPs, thus increasing power values. During the analysis, genes within 50 kb of each candidate SNP were mapped and prioritized, which were in LD with genomewide significant SNPs at the adjusted r2 threshold. To identify the gene part of the tissue dependency, we performed tissue enrichment analysis using the FUMA platform with 54 tissue types available from GTEx (v.8). The Benjamin Hochberg procedure was used to correct for multiple testing [30].

Finally, we took the intersected portion of physically annotated and MAGMA validated genes and performed Bofferni correction again as the gene portion of genetic overlap between the two traits.

Causal analysis

To assess the causal relationship between ADHD and OSA, we conducted a two-sample MR, a method that employs genetic variation as an instrumental variable (IV) to estimate causal associations [31]. IV must be strongly linked to the exposure and unrelated to the outcome, typically set at P < 5 × 10− 8, clumping: R2 < 0.001 (clumping window size = 1000 kb). F-values below 10 indicate weak instrumental variables. The inverse variance weighting (IVW) method served as our primary MR analysis, while MR-Egger and weighted median methods were utilized to verify the results under relaxed modeling assumptions. We also conducted pleiotropy and heterogeneity analysis to ensure result robustness and reliability. Additionally, we employed the “leave-one-out” method to iteratively remove each SNP and observed changes in the meta-effects to assess the sensitivity of the results to specific SNPs.

Results

General genetic correlation

Genome-wide genetic correlation quantifies the average sharing of genetic effects between two traits that are not affected by environmental confounders. LDSC indicated heritabilities of 0.095 and 0.043 for OSA and ADHD, respectively. Genetic correlation analyses indicated that ADHD has a significant genetic correlation with OSA (Rg = 0.315, P = 2.45E-28). This was consistent in the results from HDL, which obtained heritabilities of 0.102 and 0.030 for ADHD and OSA, respectively, with genetic correlation and significance of (Rg = 0.380, P = 1.09E-25) (Table 1).

Table 1 The source of GWAS and genome-wide genetic correlation between ADHD and OSA using LDSC and HDL
Full size table

Multi-traits analysis

The meta-analysis revealed 320 pleiotropic SNPs, including 115 newly identified ones (Supplementary Tables 1, 2). Nearly half of the SNPs are located in intronic regions, while the majority of the remaining SNPs reside in intronic non-coding RNA regions and intergenic regions (Fig. 2A). 45 loci were identified by annotation and subsequently (Supplementary Table 3), 5 loci driving the co-occurrence of ADHD and OSA were identified using COLOC (Table 2), The topSNP of the most significant loci was rs11599313 (Pcpassoc = 4.11E-10, PHH4 = 0.91) mapping to the SORCS3 gene, closely associated with neuronal development and plasticity, and previously identified as a risk locus for ADHD [20]. The topSNP associated with the less significant loci was rs13099657 (Pcpassoc = 2.18E-09, PHH4 = 0.87), situated near the PCCB and STAG1 genes. The PCCB mutation causes mitochondrial dysregulation of energy [32]. The topSNP for the last significant loci was rs2933195 (Pcpassoc = 6.11E-09, PHH4 = 0.72), located near the MDGA2 gene, which regulates the development of inhibitory synapses and is associated with autism [33].

Table 2 Significant results from colocalization analysis for each pleiotropic locus identified from CPASSOC
Full size table

We identified 18,786 genes using the MAGMA method (Fig. 2B, Supplementary Table 4). It utilized the results of CPASSOC and determined 72 genes overlapping with loci annotated by FUMA, identifying 57 significant pleiotropic genes post-Bonferroni correction (Supplementary Tables 5, 6). FTO, encoding the fat mass and obesity-associated protein, emerged as the most significant gene (Pmagma = 6.51E-12) and was previously found to be linked to body mass index, which is also a pleiotropic gene for OSA and other metabolic diseases [34, 35]. The genes mapped by the pleiotropic loci identified using COLOC are also significant in MAGMA. With the most significant being SORCS3 (Pmagma = 7.15E-11). Significant enrichment was observed in 11 brain regions (Supplementary Table 7). Among these, the cerebellum (P = 3.0189E-07) exhibited the most significance, while the cerebellar hemisphere (P = 5.539E-07) showed the second most significance. Additionally, the pituitary gland was also found to be involved in the co-occurrence of both diseases (P = 1.9442E-04) (Fig. 2C).

Fig. 2
figure 2

A: The genetic distribution based on FUMA. B: The Manhattan plot based on the result of MAGMA. C: The result of tissue enrichment in GTEx 8 on the pleiotropic genes, the color red represents the significant tissue, and the color blue represents the tissue did not reach threshold

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Causal analysis

Upon screening, 23 robust instrumental variables (Fig. 3A, Supplementary Table 8) were identified and all methods consistently indicated that ADHD increases the risk of developing OSA (PIVM=0.02, OR (95%CI):1.09 (1.01–1.17)). Similarly, the weighted median and simple mode methods supported this finding. However, the MR-Egger method did not reach statistical significance and showed slightly different effect direction (P = 0.77, OR = 0.94, 95% CI: 0.64–1.40) (Table 3). Our analysis, employing the MR-Egger regression intercept approach, revealed no evidence of horizontal pleiotropy in the association of ADHD with OSA, with a p-value exceeding 0.05 (P = 0.475). Furthermore, the Cochrane Q statistics results showed significant heterogeneity, with a p-value slighter (PIVM=0.035) higher than 0.05. Therefore, we will utilize a random effects model. The “leave-one-out” method demonstrated that the removal of any SNP had no impact on the exposure-outcome relationship (Fig. 3B). Additionally, reverse mendelian randomization were performed in supplementary analysis, which did not identify any instrumental variables harmonizing OSA with ADHD.

Fig. 3
figure 3

A: Scatter plots for the causal association between ADHD and OSA; B: Leave-one-out for the causal association between ADHD and OSA

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Table 3 MR results of causal effects between ADHD and OSA (P < 5 × 10− 8)
Full size table

Discussion

This study elucidates the genetic architecture of ADHD and OSA through diverse methods based on the GWAS. It indicates a genetic correlation between them and proposes a genetic factor-mediated anatomical and functional explanation for their co-occurrence, driven by brain-specific genes rather than genes from the entire body.

The high prevalence of OSA and its impact on other diseases may be an interactive process, making us more interested in its mechanisms. In this paper, we found high heritability of OSA using LDSC, which can lay the foundation for subsequent studies on the genetic background of the trait, in addition to this, we found that OSA and ADHD are genetically correlated, which is in line with previous epidemiological results. Several previous studies were mentioned that reported SNPs or mutations affecting gene function or being associated with OSA and ADHD. For ADHD, studies such as Vidal et al. (2022) identified genomic variations in the ADGRL3 gene that are implicated in neurogenesis and ADHD [36]. Xu et al. (2022) conducted correlation research on susceptibility SNPs and the severity of clinical symptoms in ADHD [37]. Regarding OSA, Chen et al. (2018) conducted a multiethnic meta-analysis identifying RAI1 as a possible OSA-related quantitative trait locus in men [18]. Raptis et al. (2021) explored intergenic SNPs in OSA syndrome, revealing metabolic, oxidative stress, and immune-related pathways. However, the pleiotropic SNPs influencing their co-occurrence remain unknown to date [38]. We identified 320 pleiotropic genes through CPASSOC, and further pinpointed key genes at the gene level using FUMA annotation and MAGMA-based analyses. Propionyl-CoA Carboxylase Subunit Beta (PCCB), a nuclear-encoded mitochondrial gene [32], its mutations can impair mitochondrial energy metabolism by disrupting the tricarboxylic acid (TCA) cycle and mitochondrial dysfunction. Neurometabolic disorders (NMDs) are biochemical alterations due to a genetic variant resulting in the complete or partial inactivation of an enzyme or metabolite transporter, which may cause neurodevelopmental abnormalities or behavioral disorders [39]. Oxidative stress triggered by mitochondrial dysmetabolism can significantly contribute to neuronal apoptosis, thereby influencing schizophrenia [40] and bipolar disorder [41]. This mechanism has been identified in prior studies as a potential cause for the manifestation of psychiatric and somatic symptoms in ADHD [42, 43]. Houshmand M et al. demonstrated that individuals with mitochondrial DNA mutations develop ADHD symptoms during adolescence [44]. Mitochondrial DNA copy number was found to be associated with severity of ADHD in treatment in a one-year follow-up study [45]. On the other hand, imbalanced oxidative stress is an important predictor of prognosis and complications in patients with OSA [46], interstitial hypoxia may trigger systemic oxidative stress leading to behavioral abnormalities [47], the lower mtDNA copy number in whole blood DNA of OSA patients correlated with their severity [48]. Reduced PCCB gene expression decreases GABA levels by inhibiting the TCA cycle and has been identified to be a risk gene for SCZ [49]. GABA, as a major inhibitory neurotransmitter in the brain [50], can reduce abnormal brain impulses. Obstructive sleep apnea is correlated with decreased GABA and increased glutamate levels in the insular cortex [51], and GABAergic and glutamatergic neurotransmitter disorders also play a role in the pathogenesis of OSA [52]. It was found that the decreased expression of tyrosine kinase adaptor protein 1 (NCK1) in OSA patients was related to their dyslipidemia [53] and that the noncatalytic region of NCK1 is an actin regulatory factor that promotes memory formation and affects emotions [54, 55]. As a result of the above, we can tentatively hypothesize that genetic factors may be responsible for the co-occurrence of the two diseases by regulating energy and metabolism.

In addition, we have also identified other shared genes, in which SORCS3 has been previously associated with ADHD in studies [20], SORCS3 acts as an intracellular transport receptor for protomyosin-associated kinase B, which together with another VPS10p-domain receptor-SORCS1, can control energy balance and orexigenic peptide production by attenuating BDNF signaling [56]. SORCS3 mutations cause an increase in pro-food neuropeptides thereby leading to a chronic state of energy excess BDNF is also involved in the pathophysiology behind OSA and its sequelae, BDNF may influence airway patency by modulating neuroplasticity in the sublingual nucleus [57], thus, it may reduce episodes of hypoxia. It may also counteract the damage caused by IH as a way to maintain cellular structures in the CNS [58]. SORCS3 is primarily expressed in the brain and spinal cord, exerting a greater influence on glutamate receptor function compared to the GABAergic signaling pathway [59]. This protein acts as a postsynaptic regulator of inhibition and fears extinction, impacting both NMDA receptor-dependent and nondependent long-term depression. The reduction of BDNF by SORCS3 mutations may be one of the mechanisms in the development of OSA. The MAM domain containing glycosylphosphatidylinositol anchor 2 gene (MDGA2) located on chromosome 14 has been found to be associated with ASD, whose mutation are identified to elevate excitatory transmission, leading to altered cortical processing and cognitive performance [33], It has also been found to regulate neuronal development and migration [60]. It may be the genetic background for the overlap between the ASD spectrum and the behavior of slow wave sleep syndrome (CSWSS) [61], and whether this genetic variation is responsible for the overlap of ADHD and OSA symptoms needs to be further explored. The findings are consistent with the first two genes, in which mutations affect the function of inhibitory neurons [62, 63], Thus, leading to the hypothesis that the brains of OSA patients are in an arousal state, which could potentially trigger OSA and contribute to the high prevalence of OSA in patients with BP [64].

The discovered genetic variants affecting metabolism supporting both disorders validate earlier hypotheses suggesting that OSA may induce neurodevelopmental disorders in children through oxidative stress. Our findings also indicate that both disorders coexist with interdependent anatomical structural changes, affecting the brain extensively, with more pronounced effects in the prefrontal lobes and the nucleus accumbens basal ganglia. The hypoxia and sleep disturbances induced by OSA lead to cellular and chemical imbalances, resulting in dysfunction of the prefrontal cortex and heightened neurobehavioral deficits. These deficits may manifest as hyperactivity and impulsivity in children [65]. The limbic system volume was significantly reduced in OSA patients compared to normal subjects. Additionally, NCK1 activity in the lateral amygdala regulates the formation of long-term fear memories, shedding light on the potential role of the amygdala in OSA. Finally, Upon applying MR, we discovered that ADHD increases the risk of developing OSA. Moreover, we observed similarities between the sleep disorders associated with ADHD and the symptoms of OSA, potentially leading to misdiagnosis or overdiagnosis of the two disorders. Notably, as OSA treatment contributes to the improvement of ADHD symptoms, lifestyle habits aimed at improving or preventing the occurrence of OSA can be integrated into ADHD treatment regimens.

This study exhibits several strengths. It is the first study to investigate the genetic underpinnings of ADHD and OSA, utilizing the largest sample size of GWAS to enhance the reliability of the findings. Moreover, our analysis delved into the genetic correlation of the two traits at both SNP and gene levels, bolstering the depth of our exploration. Lastly, the utilization of a variety of methods to study the genetic architecture of both traits contributes to the robustness of our results.

This paper is limited by the use of GWAS data solely from European populations. The sample size of GWAS studies associated with OSA in other ethnicities is small. As a result, we did not explore the genetic overlap across multiple ethnicities. This could be an area for future research. Additionally, future research could explore stratifying ADHD GWAS to investigate the relationship between ADHD in adults or children and OSA. Additionally, a limitation of this study is the inconsistency in effect directions between different Mendelian randomization methods, particularly between MR-Egger and other methods like IVW. This discrepancy may be due to the assumption of horizontal pleiotropy in MR-Egger, which adjusts for pleiotropic bias, whereas other methods do not account for such biases. This inconsistency suggests potential biases or differences in method sensitivity, which should be further investigated in future studies.

This article explores the genetic overlap between ADHD and OSA and concludes that they are genetically related. It also identifies key genetic loci that can influence their co-occurrence. This sheds light on the mechanisms of comorbidities and highlights the importance of preventing and treating OSA in patients with ADHD.

Data availability

Data supporting the findings of this study are available from the article/additional material.

Abbreviations

ADHD:

Attention deficit hyperactivity disorder

OSA:

Obstructive sleep apnea

MR:

Mendelian randomization

IVW:

Inverse variance weighting

IV:

Instrumental variable

LDSC:

linkage disequilibrium score regression

Rg:

Genetic correlation

REML:

Restricted maximum likelihood method

HDL:

High-definition likelihood inference

CPASSOC:

Cross -Phenotype Association

FUMA:

Functional Mapping and annotation

MAGMA:

Multi-marker Analysis of GenoMic Annotation

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Acknowledgements

The authors extend their gratitude to the referenced studies or consortiums for contributing open-access datasets for the analysis.

Funding

This research received support from the National Natural Science Foundation of China (grants 82471936 to G.B.C. and 82302148 to B.H.).

Author information

Author notes

  1. Yao Tong and Yan-Jing Chen have contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University (Fourth Military Medical University), Xi’an, Shaanxi Province, China

    Yao Tong & Guang-Bin Cui

  2. Department of Radiology, Peking University Third Hospital, Beijing, China

    Yan-Jing Chen

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  1. Yao Tong
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  2. Yan-Jing Chen
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Contributions

Y.T: Data curation, Formal analysis, Writing-original draft, Conceptualization. YJ.C: Data curation, Methodology, Formal analysis, Writing-original draft, Conceptualization. GB.C: Supervision, Writing-review and editing, Conceptualization. The final draft of the manuscript was approved by all writers.

Corresponding author

Correspondence to Guang-Bin Cui.

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Not applicable. The present analysis was based on summary data from previous genome-wide association studies that had achieved written informed consent and ethics approval.

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The authors declare no competing interests.

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Tong, Y., Chen, YJ. & Cui, GB. The genetic overlap and causal relationship between attention deficit hyperactivity disorder and obstructive sleep apnea: a large-scale genomewide cross-trait analysis. BMC Psychiatry 25, 454 (2025). https://doi.org/10.1186/s12888-025-06899-w

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Keywords

BMC Psychiatry

ISSN: 1471-244X

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