Global medical genetics · 2026
Cross-trait analysis reveals shared genetic architecture between mental health and cardiovascular traits.
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Yin Quanting, Zhao Jun, Yi Pengcheng, Yang Chunhua, Mi Jia, Xu Fuyi, Tian Geng, Shang Li, He Qingxia
Аннотация
BACKGROUND: The association between cardiovascular disease (CVD) and mental health (MH) is well established. However, the shared genetic architecture and underlying causal relationships between these conditions remain insufficiently understood.
MATERIAL AND METHODS: Summary statistics from Genome-wide association studies (GWAS) for 8 MH traits and 10 cardiovascular system (CVS) traits were used in the analyses. LDSC regression was applied to estimate heritability and genetic correlations. Bidirectional Mendelian randomization (MR) analysis was conducted to explore potential causal relationships between MH and CVS traits. In addition, Summary data-based Mendelian randomization (SMR) analysis was used to identify gene expression in circulating causally associated with both MH and CVS traits. Functional annotation of genes was further explored by querying gene information across multiple databases.
RESULTS: LDSC identified five cross-trait genetic correlations that survived Bonferroni correction, with nine additional pairs reaching false discovery rate significance. Bidirectional causality between DBP and anxiety, and unidirectional links between DBP-NM, SBP-anxiety, anxiety-angina, and NM-CAD/myocardial infarction. SMR analysis further identified 12 shared genes that regulate both MH and CVS traits. Notably, CNNM2 was implicated across transcriptional levels, mouse knockout models, and pathogenic mutations, with associations to both cardiovascular and neurological phenotypes. Additionally, RGS6 and GAB2 simultaneously influence both anxiety and blood pressure, and ZCCHC14 exhibits pleiotropic effects on heart rate variability and blood vessels.
CONCLUSION: Our study provides evidence for a shared genetic basis and suggests potential causal relationships between CVS and MH traits. This highlights the intricate interplay between genetic factors, mental well-being, and cardiovascular health, underscoring the value of further mechanistic and experimental research to clarify how these shared pathways might eventually inform clinical strategies.
MATERIAL AND METHODS: Summary statistics from Genome-wide association studies (GWAS) for 8 MH traits and 10 cardiovascular system (CVS) traits were used in the analyses. LDSC regression was applied to estimate heritability and genetic correlations. Bidirectional Mendelian randomization (MR) analysis was conducted to explore potential causal relationships between MH and CVS traits. In addition, Summary data-based Mendelian randomization (SMR) analysis was used to identify gene expression in circulating causally associated with both MH and CVS traits. Functional annotation of genes was further explored by querying gene information across multiple databases.
RESULTS: LDSC identified five cross-trait genetic correlations that survived Bonferroni correction, with nine additional pairs reaching false discovery rate significance. Bidirectional causality between DBP and anxiety, and unidirectional links between DBP-NM, SBP-anxiety, anxiety-angina, and NM-CAD/myocardial infarction. SMR analysis further identified 12 shared genes that regulate both MH and CVS traits. Notably, CNNM2 was implicated across transcriptional levels, mouse knockout models, and pathogenic mutations, with associations to both cardiovascular and neurological phenotypes. Additionally, RGS6 and GAB2 simultaneously influence both anxiety and blood pressure, and ZCCHC14 exhibits pleiotropic effects on heart rate variability and blood vessels.
CONCLUSION: Our study provides evidence for a shared genetic basis and suggests potential causal relationships between CVS and MH traits. This highlights the intricate interplay between genetic factors, mental well-being, and cardiovascular health, underscoring the value of further mechanistic and experimental research to clarify how these shared pathways might eventually inform clinical strategies.