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. 2026 Feb 11;16:169. doi: 10.1038/s41398-026-03897-8

Dissecting the shared genetic landscape of schizophrenia and hippocampal subfields: A genome-wide cross-trait analysis

Lining Guo 1,#, Jiaxuan Zhao 1,#, Qin Qin 2,3,#, Yayuan Chen 1, Zuhao Sun 1, Zhihui Zhang 1, Minghuan Lei 1, Ying Zhai 1, Jinglei Xu 1, Wenjie Cai 1, Qi An 1, Yue Wu 1, Haolin Wang 1, Fengtan Li 1,✉, Yanmin Peng 4,✉, Mengge Liu 1,✉, Feng Liu 1,✉
PMCID: PMC13021997  PMID: 41672967

Abstract

Schizophrenia is associated with hippocampal volumetric changes, yet the genetic mechanisms linking schizophrenia to alterations in hippocampal volume and its subfields remain poorly understood. We analyzed the largest available GWAS summary statistics for schizophrenia (53,386 cases, 77,258 controls) and hippocampal volumetric traits (33,224 individuals). Linkage disequilibrium score regression (LDSC) was employed to estimate global genetic correlations, while conditional and conjunctional false discovery rate (cond/conjFDR) method was used to identify shared genetic variants across 44 hippocampal traits, including both whole hippocampal and subfield-specific volumes. Although weak global genetic correlations were observed between schizophrenia and hippocampal volumetric traits, variant-level analysis identified significant shared genetic variants. Specifically, the cond/conjFDR method revealed substantial overlap of genetic loci across 44 hippocampal traits, identifying 171 distinct shared loci, including 6 novel ones. Hemispheric differences were observed: we identified 106 left-hemispheric and 117 right-hemispheric loci; among the hemisphere-specific subset, 49 were left-specific (46.2%) and 61 right-specific (52.1%), with the remainder appearing in both hemispheres. Enrichment analysis of the mapped genes revealed involvement in processes such as nervous system development, neuron generation, and differentiation. Validation and replication analyses confirmed the robustness and reproducibility of our findings across different datasets and methods. Our findings reveal significant genetic overlap with mixed effects between schizophrenia and hippocampal volumetric traits, underscoring the complexity of their shared genetic architecture. These results also highlight hemispheric differences in genetic influence, providing new insights into the neurobiological mechanisms of schizophrenia.

Subject terms: Genetics, Schizophrenia, Neuroscience

Introduction

Schizophrenia is a complex mental disorder affecting millions worldwide [1], marked by hallucinations, delusions, and cognitive disturbances [2]. These symptoms may have consequences on social interactions and overall functioning, affecting daily activities such as finding or retaining employment and maintaining relationships with family and friends [3, 4]. As research advances, it is increasingly apparent that the diversity of schizophrenia extends beyond observable symptoms to involve intricate neurobiological mechanisms [5, 6]. Among these mechanisms, the hippocampus, a crucial brain structure involved in memory, learning, and emotion processes, plays a significant role in the neurobiological study of schizophrenia [7]. Notably, structural abnormalities in the hippocampus have been consistently observed in patients with schizophrenia, suggesting a vital link between hippocampal pathology and the manifestation of core symptoms [8].

Neuroimaging studies have progressively revealed volumetric changes in the hippocampus and its subfields in schizophrenia patients [9–11]. For example, a meta-analysis found significant reductions in the volume of several hippocampal subfields in individuals with schizophrenia [12]. These changes, absent in unaffected relatives and healthy controls, suggest an early, stable trait of the disorder [13]. Furthermore, certain hippocampal subfields, such as the left dentate gyrus, show promise as potential intervention targets, with reductions in this area being identified as a biomarker for predicting treatment disengagement [14]. These findings highlight the physical impact of schizophrenia on key brain regions, providing valuable insights into its neurobiological underpinnings. Nevertheless, the relationship between schizophrenia and hippocampal volume is multifaceted, necessitating further research to clarify these structural changes and their clinical implications.

Genetic contributions are pivotal in elucidating the mechanisms underlying both schizophrenia and hippocampal volume changes [15, 16]. Genome-wide association studies (GWAS) have identified numerous genetic loci linked to schizophrenia, as well as to structural brain alterations, including hippocampal volume [17, 18]. To refine our understanding of these genetic relationships, advanced statistical approaches such as linkage disequilibrium score regression (LDSC) and local analysis of [co]variant association (LAVA) have been employed [19, 20]. Additionally, the conditional/conjunctional false discovery rate (cond/conjFDR) method has been applied to detect shared genetic loci of schizophrenia and hippocampal volume [21, 22]. These cutting-edge methodologies have provided deeper insights into the complex genetic architecture of schizophrenia and its relationship with whole hippocampal volume [19, 21], but focusing solely on whole hippocampal volume may obscure important genetic and structural differences within its subfields. Given the hippocampus’s complexity, subfield-focused studies are essential for uncovering localized genetic effects tied to core symptoms.

Building on this foundation, recent studies have begun to explore the shared genetic basis between schizophrenia and hippocampal subfields. Initial LDSC analyses using 12 broad subfields found no significant genome-wide correlations, but the cond/conjFDR method revealed genetic overlap with specific subfields [23]. Another study, employing 19 finer hippocampal subdivisions, provided greater anatomical resolution [22]. This increased granularity allowed for the detection of more localized genetic effects that might have been missed with broader divisions, enhancing the ability to pinpoint subfields relevant to schizophrenia pathology. However, this study focused solely on global genetic correlations and found only weak associations. Additionally, all the aforementioned studies averaged the volumes of the left and right hippocampal subfields, potentially overlooking lateralization effects that could be crucial for understanding schizophrenia.

In this study, we conducted a comprehensive analysis to investigate the shared genetic architecture between schizophrenia and the hippocampus. Using the largest available GWAS summary statistics for schizophrenia and 19 hippocampal subfields, we focused on applying LDSC and cond/conjFDR methods to explore genetic overlap between these traits. The genetic loci identified by conjFDR were further validated using LAVA. By examining hippocampal subfields at finer granularity and analyzing left and right volumes separately, we identified localized genetic effects that may have been obscured in previous whole-structure analyses. This refined approach deepens our understanding of the genetic connections between schizophrenia and hippocampal subfields, shedding light on the neurobiological processes involved.

Materials and methods

GWAS summary data

The GWAS summary statistics of hippocampal volumetric traits encompass a sample size of 33,224 individuals of European ancestry sourced from the UK Biobank [17]. Hippocampal volumetric traits were segmented by FreeSurfer v7.0 [24, 25] (https://surfer.nmr.mgh.harvard.edu/), covering 19 subfields on each side. These subfields encompass the parasubiculum, hippocampus-amygdala transition area (HATA), fimbria, hippocampal tail, hippocampal fissure, presubiculum head and body, subiculum head and body, cornu ammonis (CA)1/CA3/CA4 head and body, granule cell layer and molecular layer of the dentate gyrus (GC-ML-DG) head and body, molecular layer head and body. Additionally, the bilateral whole hippocampus, hippocampal head, and hippocampal body were included, resulting in a total of 44 traits for hippocampal and subfield volumes. All volumetric measures were generated under a standardized pipeline and adjusted for standard UK Biobank imaging confounds [17]. The schizophrenia GWAS summary statistics were sourced from multiple cohorts, comprising 74,776 cases and 101,023 controls [18]. For this study, we focused on individuals of European ancestry, yielding a dataset of 53,386 cases and 77,258 controls. All these included original studies claimed to have been approved by local ethics committees, and informed consent was obtained from all the participants.

Linkage disequilibrium score regression analysis

Although previous studies have found no or weak genome-wide genetic correlation between schizophrenia and hippocampal volumetric traits [22, 23], we performed cross-trait LDSC analyses using the latest GWAS summary statistics to reassess this relationship [26]. Precomputed linkage disequilibrium (LD) scores derived from the 1000 Genomes European reference panel were utilized, and the major histocompatibility complex (MHC) region (Chr6: 25,119,106–33,854,733) and chromosome 8p23.1 (Chr8: 7,200,000–12,500,000) were excluded due to extremely complex LD patterns. LDSC estimates the genetic covariance by regressing single nucleotide polymorphism (SNP) effect sizes from two GWAS datasets (schizophrenia and hippocampal volumetric traits) on the LD scores, after which genetic correlation (rg) is obtained by normalizing the covariance by SNP heritabilities. We computed rg between schizophrenia and each of the 44 hippocampal subfield traits, and pairs with P < 0.05 were retained for subsequent analyses.

Conditional and conjunctional false discovery rate

We constructed conditional Q-Q plots to visualize the putative genetic overlap between schizophrenia and hippocampal volumetric traits after excluding SNPs within the MHC and 8p23.1 genomic regions. Specifically, one trait is selected as the primary trait, and Q-Q plots are generated for several P value thresholds (P = 1, 0.1, 0.01, 0.001) in the secondary trait. A leftward deflection away from the null (diagonal) with a decrease in P value threshold is indicative of strong enrichment of the primary trait conditioned on the secondary [27, 28].

The cond/conjFDR method was employed to identify the shared genetic variants associated with schizophrenia and hippocampal volumetric traits [29]. The condFDR method is an extension of the standard FDR and builds on an empirical Bayesian statistical framework to exploit the power of combining two GWASs for improving the discovery of genetic variants. The method leverages the presence of SNP associations with the primary and secondary traits to identify variants more likely to be true associations even though the P values do not reach the genome-wide significance threshold [30]. Specifically, this method re-ranks test statistics using the associations between variants and the secondary trait and re-calculates the associations between these variants and the primary trait. Inverting the roles of primary and secondary traits yields the inverse condFDR value. ConjFDR is defined as the maximum of the two mutual condFDR values, providing a conservative estimate of the false discovery rate for a SNP associated with both traits. In this study, we used condFDR to improve the discovery of genetic variants related to schizophrenia and hippocampal volumetric traits, and conjFDR to pinpoint loci jointly associated with both traits. Given established hippocampal lateralization, left-dominant alterations in schizophrenia, and evidence for hemisphere-specific genetic effects [31, 32], we analyzed left and right subfields separately. SNPs were deemed significant if they had a condFDR < 0.01 in the condFDR analysis or a conjFDR < 0.05 in the conjFDR analysis [33–36].

For the cond/conjFDR analysis, independent genomic loci were defined using the sumstats.py script. Independent significant SNPs were identified as variants with condFDR < 0.01 or conjFDR < 0.05 and pairwise LD r2 < 0.6 with any other significant SNP. Lead SNPs were selected from this set as those showing pairwise LD r² < 0.1 among themselves. Candidate SNPs were defined as variants in high LD (r² ≥ 0.6) with an independent significant SNP and were used to determine the boundaries of each locus. Genomic loci within 250 kb of one another were merged. LD calculations were based on the European ancestry reference panel from the 1000 Genomes Project. Independent loci identified across all hippocampal volume-schizophrenia pairs were subsequently merged into distinct genomic loci. If the genetic boundaries of loci from different trait pairs overlapped, they were merged by unifying their genomic ranges. The SNP with the lowest FDR value within each merged locus was designated as the top lead SNP.

Functional annotation

All candidate SNPs within specific genomic loci were utilized for functional annotation using the Functional Mapping and Annotation (FUMA) online platform (https://fuma.ctglab.nl/) [37]. Candidate SNPs were annotated using combined annotation-dependent depletion (CADD) scores, with scores above 12.37 indicating a higher likelihood of pathogenicity [38], RegulomeDB scores, where lower scores suggest a greater likelihood of regulatory functionality [39], and 15-core chromatin states from ChromHMM, with categories 1–7 associated with open chromatin states and gene expression [40, 41]. Moreover, candidate SNPs derived from conjFDR analysis were mapped to protein-coding genes using any of three strategies: (1) positional mapping within a 10-kb window around each SNP, (2) eQTL mapping to link cis-eQTL SNPs with genes whose expression is influenced by allelic variation (GTEx v8 brain), and (3) chromatin interaction mapping to identify connections through three-dimensional DNA-DNA interactions between the genomic regions of SNPs and proximal or distal genes. Furthermore, we utilized g:Profiler (https://biit.cs.ut.ee/gprofiler/gost) to evaluate gene-set enrichment of gene ontology (GO) biological processes for all mapped genes [42], applying the Benjamini-Hochberg FDR method to correct for multiple comparisons, with a significance threshold set at q < 0.05.

Validation analysis

The LAVA approach was used to validate the shared loci identified by conjFDR analysis [43]. Given that LAVA requires sufficient local genetic signal for both traits, we first performed univariate tests to estimate the heritability within 2,495 predefined genomic regions that were generated by partitioning the human genome into ~1 Mb blocks. Only regions with a univariate signal at P < 0.05 on both traits were included in the bivariate analysis for testing the local genetic correlations. To adjust for multiple comparisons, the Benjamini-Hochberg FDR q < 0.05 was applied in the bivariate analysis. Loci that showed overlap between significant loci identified by conjFDR and those identified by LAVA were considered validated.

Reproducibility of results and novel loci definition

Two previous studies have employed the conjFDR approach to explore the genetic overlap between schizophrenia and hippocampal volume [21, 23]. Specifically, van der Meer et al. investigated this overlap across 12 hippocampal subfields [23]. However, their conditional Q-Q plot analysis revealed significant genetic enrichment only in two subfields, the presubiculum and subiculum, in relation to schizophrenia. Consequently, their subsequent conjFDR analysis focused specifically on these two regions, identifying nine shared loci [23]. Since the exact boundaries of these loci were not provided, we defined each locus as extending 1,000 kb upstream and downstream from the reported base pair position for our analysis. In parallel, Li et al. concentrated on the shared genetics between schizophrenia and the whole hippocampal volume, discovering three genetic loci [21]. Both studies demonstrate the utility of the conjFDR method in pinpointing genetic loci associated with schizophrenia and hippocampal volumetric traits.

To compare our findings with previous research, we defined loci as replicable if they were located within 500 kb of any locus identified in our primary analysis. Conversely, a locus identified by conjFDR was considered novel for a given trait (e.g., schizophrenia or hippocampal volume) if it met the following three criteria: first, none of the candidate SNPs within the identified locus had previously been reported as significantly associated with that trait or appeared in the NHGRI-EBI GWAS Catalog; second, the locus was located at least 500 kb beyond the boundaries of any loci defined in prior GWAS studies for that trait at a threshold of P < 1.0E-4 [17, 18]; additionally, the locus did not overlap with any shared loci identified in prior conjFDR studies.

Results

Genetic correlation

In the LDSC analysis, genetic correlations across all hippocampal volumetric traits ranged from −0.0778 to 0.0486 (Fig. 1). Significant negative genetic correlations were observed between schizophrenia and several hippocampal volumetric traits at a nominal level. These included the left CA1 body (rg = −0.0669, P = 0.0364), left subiculum head (rg = −0.0676, P = 0.0288), left molecular layer body (rg = −0.0761, P = 0.0394), left GC-ML-DG head (rg = −0.0761, P = 0.0219), left GC-ML-DG body (rg = −0.0778, P = 0.0261), left CA4 head (rg = −0.0702, P = 0.0314), left CA4 body (rg = −0.0731, P = 0.0282), the left whole hippocampal head (rg = −0.0609, P = 0.0423), and the right CA3 head (rg = −0.0769, P = 0.0129). However, no nominally significant positive genetic correlations were found between schizophrenia and any hippocampal volumetric traits.

Fig. 1. Global genetic correlation analyses between schizophrenia and hippocampal volumetric traits.

Fig. 1

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Forest plot illustrating genetic correlations between schizophrenia and different hippocampal subfields based on LDSC analysis. Blue points represent negative correlations, while red points represent positive correlations. Darker blue points represent nominally significant negative correlations (P < 0.05). Error bars indicate standard errors of the estimates. Abbreviations: CA, cornu ammonis; GC-ML-DG, granule cell layer and molecular layer of the dentate gyrus; HATA, hippocampus-amygdala transition area; lh, left hemisphere; rh, right hemisphere.

Loci discovery for schizophrenia and hippocampal volumetric traits

The conditional Q-Q plots revealed a progressive increase in SNP enrichment for schizophrenia when conditioned on hippocampal volumetric traits (Figure S1). Likewise, the reverse conditional Q-Q plots showed enrichment for hippocampal volumetric traits when conditioned on schizophrenia associations (Figure S2). The consistent leftward shift in genetic variants with higher significance in the conditional trait suggests a substantial polygenic overlap between schizophrenia and hippocampal volumetric traits. With a condFDR threshold of < 0.01, the number of loci associated with schizophrenia, conditioned on the 44 hippocampal volumetric traits, ranged from 215 to 267. In total, 10,743 independent loci were identified and merged into 429 distinct loci across all schizophrenia-hippocampal volume pairs. Similarly, conditioning on schizophrenia revealed 944 independent loci, which were merged into 278 distinct loci for the 44 hippocampal volumetric traits, with the number of loci per trait ranging from 3 to 46 (Tables S1, S2).

Shared genetic loci between schizophrenia and hippocampal volumetric traits

To uncover shared genetic loci, conjFDR analyses were conducted. Genetic variants associated with both schizophrenia and hippocampal volumetric traits were distributed across the genome, leading to the identification of 635 loci (Fig. 2A). Shared loci with schizophrenia were found across 43 of the 44 hippocampal volumetric traits, with the exception of the right parasubiculum, which showed no overlap (Fig. 2B). The most significant locus for the left hippocampal volumetric traits was chr17: 43,463,493–44,865,603 (lead SNP: rs62062288, conjFDR = 1.92E-5, Fig. 2C), while for the right hippocampal volumetric traits, it was chr16: 29,923,510–30,380,486 (lead SNP: rs4788190, conjFDR = 1.72E-6, Fig. 2D). After merging overlapping conjFDR loci across all hippocampal volumetric traits, 171 distinct loci were jointly associated with schizophrenia and hippocampal volume. Across merged loci, the contributing subfields and the number of subfields per locus are listed in Table S3. We identified 90 loci that were significant for exactly one hippocampal subfield–schizophrenia pair, with no other subfields reaching significance at those loci. Separately, considering all pre-merge subfield–schizophrenia lead SNP pairs, 54.7% showed concordant effects (risk+ / volume + ) and 45.3% discordant effects (risk+ / volume–), indicating a mixed pattern with a modest predominance of concordant effects (Table S4).

Fig. 2. Genetic overlaps between schizophrenia and hippocampal volumetric traits.

Fig. 2

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A Manhattan plots displaying shared loci between schizophrenia and hippocampal volumetric traits. Larger circles outlined in black represent lead SNPs and yellow triangles denote novel lead SNPs. The red line represents the conjFDR threshold of 0.05. B Ideograms depicting the chromosomal locations of lead SNPs shared between schizophrenia and hippocampal volumetric traits (conjFDR < 0.05). Variants are colored based on the corresponding hippocampal subfields. C LocusZoom plots for a genomic region on chromosome 17 (chr17: 43,463,493–44,865,603; lead SNP: rs62062288). Regional SNP associations (-log10(P)) for schizophrenia and left whole hippocampal body volumes are shown based on the original GWAS summary data. D LocusZoom plots for a genomic region on chromosome 16 (chr16: 29,923,510–30,380,486; lead SNP: rs4788190). Regional SNP associations (-log10(P)) for schizophrenia and right GC-ML-DG head volume are displayed using the original GWAS summary data. The lead SNP at each locus is marked in purple, and other variants are colored according to their LD (r2) value with the lead SNP. Abbreviations: CA, cornu ammonis; GC-ML-DG, granule cell layer and molecular layer of the dentate gyrus; HATA, hippocampus-amygdala transition area; LD, linkage disequilibrium; SNP, single nucleotide polymorphism.

To investigate hemispheric differences in shared genetic loci between schizophrenia and hippocampal volumetric traits, we identified distinct sets of loci for each hemisphere by combining independent loci from schizophrenia-hippocampal volumetric trait pairs. In the left hemisphere, we identified 106 distinct loci shared between schizophrenia and all hippocampal volumetric traits, with 49 (46.2%) specific to the left hemisphere. In the right hemisphere, we identified 117 distinct loci, with 61 (52.1%) specific to the right hemisphere (Tables S5, S6). These results highlight the importance of considering hemisphere-specific genetic structures, as the distinct and shared loci in each hemisphere may contribute differently to the relationship between schizophrenia and hippocampal volumetric traits.

Functional annotation

Functional annotation of all candidate SNPs identified in the condFDR/conjFDR analysis was presented in Fig. 3A. The annotation of the candidate SNPs (n = 58,252) within the loci associated with schizophrenia conditional on hippocampal volumetric traits revealed that the majority were situated in intronic regions (49.3%) and intergenic regions (31.4%), with only 0.9% located in exonic regions (Table S7). Similarly, the annotation of the candidate SNPs (n = 27,232) within the loci associated with hippocampal volumetric traits conditional on schizophrenia demonstrated that the majority were located in intronic regions (55.2%) and intergenic regions (25.4%), with only 0.9% found in exonic regions (Table S8). When considering all candidate SNPs (n = 17,703) within the shared loci between schizophrenia and hippocampal volumetric traits, the functional annotation indicated that the majority of these loci were situated in intronic regions (58.1%) and intergenic regions (21.4%), with 1.0% located in exonic regions (Table S9).

Fig. 3. Results of functional annotation and enrichment analysis.

Fig. 3

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(A) Distribution of functional annotations for all candidate SNPs identified by cond/conjFDR analysis for schizophrenia and hippocampal volumetric traits. The panels represent the distribution across functional categories, RDB scores, and minimum chromatin states. (B) Bubble plot showing enriched GO terms for biological processes associated with protein-coding genes identified by conjFDR analysis. The color gradient of the bubbles indicates the -log10(FDR q-value), while the bubble size represents the number of genes associated with each term. Abbreviations: RDB, RegulomeDB.

For the candidate SNPs identified in conjFDR analysis, approximately 3.8% of the candidate SNPs were indicated as pathogenic, with CADD scores exceeding 12.37. About 6.1% of the candidate SNPs exhibited a higher likelihood of regulatory functionality, as indicated by a RegulomeDB level of less than 3. Moreover, 91.6% of the candidate SNPs were located in regions with a predominantly open chromatin configuration, characterized by a chromatin state score of less than 8. Among the 171 top lead SNPs, eight (rs10758611, rs73026761, rs13374459, rs11578963, rs10421292, rs9309322, rs11045, and rs4540048) had CADD larger than 12.37, with rs9309322 in gene RP11-444A22.1 having the highest CADD value of 17.39 (Table S10). Furthermore, the candidate SNPs in 171 distinct shared loci were mapped to 1,065 protein-coding genes (Table S11). Enrichment analysis revealed 255 significant GO biological process terms (Table S12), mainly including nervous system development (FDR q = 7.08E-5), regulation of generation of neurons (FDR q = 8.46E-5), and neuron differentiation (FDR q = 1.10E-4) (Fig. 3B).

Validation analysis

In our local genetic correlation analyses between schizophrenia and the 44 hippocampal volumetric traits, we observed varying numbers of nominally significant local heritability estimates across the 2,495 predefined genomic loci. The fewest were identified in the intersection of significant heritability regions for schizophrenia and the right parasubiculum, with 842 loci. In contrast, the largest number was observed in the intersection between schizophrenia and the right CA1 head, with 1005 loci. In total, 41,253 significant local heritability estimates were identified across all hippocampal volumetric traits in relation to schizophrenia (Table S13). Following this, bivariate LAVA analyses were conducted on these 41,253 loci, identifying 218 regions with significant local genetic correlations between schizophrenia and hippocampal volumetric traits (Fig. 4 and Table S14). A total of 100 overlap pairs were observed between LAVA regions and conjFDR loci, corresponding to 84 of 218 LAVA regions showing at least one overlap. Conversely, 29 of 171 conjFDR loci were supported by LAVA (Table S15).

Fig. 4. Local genetic correlation analyses between schizophrenia and hippocampal volumetric traits.

Fig. 4

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Volcano plot showing local genetic correlations (rho) between schizophrenia and hippocampal volumetric traits. Gray points indicate nonsignificant correlations, while blue and red points represent nominally significant (P < 0.05) negative and positive correlations, respectively. Darker blue and red points indicate correlations that survived FDR correction (q < 0.05). Analyses are presented separately for left (upper panel) and right (lower panel) hippocampal subfields. Abbreviations: CA, cornu ammonis; GC-ML-DG, granule cell layer and molecular layer of the dentate gyrus; FDR, false discovery rate; HATA, hippocampus-amygdala transition area.

Reproducibility of results and novel loci definition

We successfully replicated several loci identified in earlier work. Specifically, five of the nine shared loci identified by van der Meer et al., and two of the three shared loci reported by Li et al. were reproduced (Table S16) [21, 23]. In our primary analysis, we identified 171 distinct loci jointly associated with schizophrenia and hippocampal volumetric traits. Among these, three were newly associated with schizophrenia and three were novel loci for hippocampal volume (Table S17).

Discussion

In this study, the shared genetic architecture between schizophrenia and hippocampal volumetric traits was investigated. The results revealed weak global genetic correlations, likely due to the coexistence of both consistent and inconsistent genetic effects across different genomic regions. Variant-level analysis using the cond/conjFDR method revealed a substantial overlap of genetic loci between schizophrenia and 44 hippocampal traits, identifying 171 distinct shared loci, including six novel ones. Notably, hemispheric differences were observed, with 46.2% of shared loci located in the left hippocampus and 52.1% in the right, suggesting that genetic influences may differ between hemispheres. Functional enrichment analysis of the mapped genes revealed their involvement in processes such as nervous system development, neuron generation, and differentiation, highlighting their relevance to the neurobiological mechanisms underlying schizophrenia. Validation and replication analyses further confirmed the robustness of these findings. Overall, these results advance our understanding of the complex genetic architecture linking schizophrenia and hippocampal volumetric traits, offering potential directions for future research into the disease’s pathophysiology and the development of targeted therapies.

In the LDSC analysis, the global genetic correlations between schizophrenia and hippocampal volumetric traits ranged from −0.0778 to 0.0486. Nominally significant negative correlations were observed between schizophrenia and several hippocampal regions, such as the left CA1 body and the left subiculum head. These weak global correlations suggest that LDSC may not fully capture the complexity of the genetic relationship between schizophrenia and hippocampal volume. As LDSC focuses on measuring the overall average effect across the genome, it can overlook region-specific interactions and may underestimate the genetic overlap, particularly when directional effects are mixed across different regions. In contrast, the cond/conjFDR method provides a clearer understanding of how genetic variants influence hippocampal volume in schizophrenia [44–46], revealing effects that could be missed by the broader, genome-wide approach of LDSC. Combining LDSC with localized analysis techniques could also provide a fuller view of the genetic architecture underlying schizophrenia and hippocampal volumetric traits.

The cond/conjFDR approach revealed substantial polygenic overlap, identifying 171 distinct loci across 44 hippocampal traits. This overlap points to a complex genetic architecture underlying both schizophrenia and hippocampal volume alterations, reinforcing previous studies that have linked hippocampal abnormalities to psychiatric disorders, especially schizophrenia [15, 19]. Our investigation of hemispheric differences in shared loci further suggests that genetic influences on hippocampal volume are not equally distributed between the left and right hemispheres. This asymmetry implies that each hemisphere may have distinct genetic factors contributing to hippocampal volume changes in schizophrenia, possibly reflecting functional differences in hippocampal processing [47, 48]. For example, the left hippocampus is more associated with verbal memory [49], whereas integrity of specific hippocampal subfields has been linked to visuospatial associative memory performance in schizophrenia-spectrum disorders [50]. Brain network electrophysiological studies have highlighted the specificity of interhemispheric neural connectivity [51], which aligns with our finding of hemisphere-specific genetic loci and may help interpret the functional heterogeneity between the two hemispheres. In addition, at the locus level, the shared loci implicate biologically plausible pathways. For example, the strongest left-hemispheric signal (rs62062288) is located within the MAPT locus, which encodes tau, a key regulator of microtubule stabilization and axonal transport [52]. The strongest right-hemispheric signal (rs4788190) maps to TMEM219, a receptor for IGFBP3 that regulates cell survival and apoptosis [53]. The distinct loci found for each hemisphere may indicate differential genetic contributions to the cognitive and behavioral symptoms observed in schizophrenia. Notably, we also discovered several novel loci, including three newly associated with schizophrenia and three novel loci related to hippocampal volume. These novel loci provide valuable insights into the genetic mechanisms linking schizophrenia and hippocampal volumetric changes, shedding light on previously unexplored pathways that may contribute to both the structural brain alterations and cognitive deficits observed in the disorder. Compared with previous studies that focused on global or bilateral hippocampal volumes [21, 23], this work integrates subfield-level resolution, hemispheric specificity, and LAVA-based validation, enabling finer localization of shared loci and revealing lateralized genetic effects that were previously undetected.

Enrichment analysis of the mapped genes from these shared loci highlighted significant biological processes related to nervous system development, regulation of neuron generation and neuron differentiation. Nervous system development pathway aligns with the neurodevelopmental hypothesis of schizophrenia, suggesting that early disruptions in brain development—particularly in the hippocampus—may predispose individuals to structural and functional abnormalities seen in the disorder [54]. Additionally, the regulation of generation of neurons and neuron differentiation pathways underscore the importance of neurogenesis and neuronal maturation in schizophrenia. Impaired neurogenesis in the hippocampus has been linked to cognitive dysfunction and psychotic symptoms, supporting the idea that genetic variants affecting these processes may contribute to hippocampal volume loss and cognitive deficits in schizophrenia [55]. Together, these findings highlight that schizophrenia is closely associated with genetic factors that regulate neurodevelopmental processes, particularly those influencing hippocampal growth and function. Research combining high-resolution neurotechnologies, such as high-throughput microelectrode arrays and high-density electroencephalogram [56, 57], provides us with precise neural activity data, helping to reveal the genetic relationship between schizophrenia and hippocampal subfields, as well as their functional changes.

Our validation and reproducibility analyses provide strong support for the robustness and reliability of our findings. LAVA analyses revealed varying numbers of significant local heritability estimates, with the fewest identified in the intersection of schizophrenia and the right parasubiculum, and the most in the right CA1 head. This regional variation suggests that genetic influences on hippocampal volume alterations in schizophrenia may differ across hippocampal subfields, with the right CA1 head potentially being more genetically implicated in schizophrenia-related changes. Bivariate LAVA showed partial overlap with the conjFDR discoveries, providing orthogonal support for a subset of signals. Additionally, the replication of loci previously identified in studies further emphasizes the consistency and reproducibility of our findings. The modest overlap likely reflects differences in phenotype definition and granularity, since we analyze subfield- and hemisphere-specific volumes, whereas several prior studies used total or bilateral hippocampal volume, as well as differences in GWAS dataset versions. Given the progress in brain-computer interface technology that integrates brain-to-brain coupling [58], future research could adopt such cross-disciplinary approaches to validate the functional effects of the identified hippocampal genetic loci.

Although this study provides valuable insights, there are several limitations to consider. First, the use of GWAS data primarily from individuals of European ancestry limits the generalizability of our results to other populations. To improve the broader applicability of these findings, future studies should aim to include more diverse populations. Second, clinical subtyping for schizophrenia was not available in the summary statistics; consequently, we could not evaluate subtype-specific associations. Third, this study does not include functional validation of the implicated genes, which restricts our ability to make definitive conclusions about how these genes influence hippocampal volume or contribute to schizophrenia pathology. Fourth, our investigation was restricted primarily to genetic factors and did not consider environmental exposures or gene-environment interactions [59, 60], both of which are known to be important in schizophrenia risk. Finally, our analysis focuses on common variants located on autosomal chromosomes, while rare variants, which are also thought to play a role in schizophrenia, were not examined. Therefore, further research exploring the effects of rare variants is important for gaining a more comprehensive understanding of the genetic architecture of schizophrenia.

In conclusion, this study sheds light on the shared genetic mechanisms underlying both schizophrenia and hippocampal volumetric traits. Through advanced statistical approaches, we identified multiple genetic loci that influence both schizophrenia susceptibility and hippocampal volume alterations. Our findings underscore the genetic overlap between these two traits, revealing both common and lateralized effects on the left and right hippocampus. These insights not only enhance our understanding of the neurobiological basis of schizophrenia but also pave the way for future research aimed at uncovering the molecular mechanisms behind hippocampal alterations in the disorder.

Supplementary information

Supplementary Figures (1.3MB, docx)
Supplementary Tables (8.8MB, xlsx)

Acknowledgements

We are deeply grateful to all the researchers and affiliated institutions for making the GWAS data publicly available, enabling us to utilize these valuable resources in conducting the present study.

Author contributions

Conceptualization: F.Liu, M.L., Y.P., F.Li; Methodology: F.Liu, L.G., J.Z., Q.Q.; Data Curation: L.G., J.Z., Q.Q., M.L., Y.Z., J.X., W.C., Q.A., Y.W., H.W.; Data Analysis: L.G., J.Z., Q.Q., Y.C., Z.S., Z.Z.; Original draft writing: F.Liu, L.G.; Review and editing: F.Liu, L.G., J.Z., Q.Q.; Supervision: F.Liu, M.L., Y.P., F.Li.

Funding

This work was funded by the Tianjin Municipal Education Commission Scientific Research Project (2025KJ016).

Data availability

All datasets used in this study are publicly accessible. The GWAS summary statistics for the 44 hippocampal volumetric traits based on UK Biobank participants are available at https://open.win.ox.ac.uk/ukbiobank/big40/, and GWAS summary statistics for schizophrenia can be downloaded from https://figshare.com/articles/dataset/scz2022/19426775.

Code availability

In the present study, we employed various publicly accessible software and tools, including: LDSC (https://github.com/bulik/ldsc), LAVA (https://github.com/josefin-werme/LAVA), cond/conjFDR (https://github.com/precimed/pleiofdr), FUMA (https://fuma.ctglab.nl/), g:Profiler (https://biit.cs.ut.ee/gprofiler/gost). Utility scripts for preprocessing cond/conjFDR summary statistics and LD-based clumping are accessible at https://github.com/precimed/python_convert.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All analyses in this study were based on publicly available GWAS datasets. The UK Biobank obtained approval from the North West Multi-center Research Ethics Committee (MREC) for data and sample collection, in accordance with ethical guidelines (http://www.ukbiobank.ac.uk/ethics/), and written informed consent was provided by all participants. The study protocols for the schizophrenia data were approved by the institutional review boards at each recruitment center. Informed consent and permission to share the data were obtained from all individuals, in compliance with the relevant guidelines of the recruiting centers’ institutional review boards. All methods were performed in accordance with the relevant guidelines and regulations.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Lining Guo, Jiaxuan Zhao, Qin Qin.

Contributor Information

Fengtan Li, Email: left9999@sina.com.

Yanmin Peng, Email: pymnn@163.com.

Mengge Liu, Email: menggeliu@tmu.edu.cn.

Feng Liu, Email: fengliu@tmu.edu.cn.

Supplementary information

The online version contains supplementary material available at 10.1038/s41398-026-03897-8.

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Associated Data

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

Supplementary Materials

Supplementary Figures (1.3MB, docx)
Supplementary Tables (8.8MB, xlsx)

Data Availability Statement

All datasets used in this study are publicly accessible. The GWAS summary statistics for the 44 hippocampal volumetric traits based on UK Biobank participants are available at https://open.win.ox.ac.uk/ukbiobank/big40/, and GWAS summary statistics for schizophrenia can be downloaded from https://figshare.com/articles/dataset/scz2022/19426775.

In the present study, we employed various publicly accessible software and tools, including: LDSC (https://github.com/bulik/ldsc), LAVA (https://github.com/josefin-werme/LAVA), cond/conjFDR (https://github.com/precimed/pleiofdr), FUMA (https://fuma.ctglab.nl/), g:Profiler (https://biit.cs.ut.ee/gprofiler/gost). Utility scripts for preprocessing cond/conjFDR summary statistics and LD-based clumping are accessible at https://github.com/precimed/python_convert.

Articles from Translational Psychiatry are provided here courtesy of Nature Publishing Group

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