Unveiling genetic and biological links: exploring the intersection of autoimmune and psychiatric disorders

Abstract

Background: A substantial body of observational evidence suggests the genetic link between autoimmune system diseases and psychiatric disorders. However, the shared genetic mechanisms remain largely unclear. This study aims to systematically explore these genetic correlations and identify potential therapeutic targets through integrative bioinformatics analyses.

Methods: We conducted a comprehensive analysis by integrating genome-wide association study (GWAS) summary data from public databases, including 15 autoimmune diseases and 8 psychiatric disorders. We examined genome-wide and regional genetic correlations and overlaps between these disorders using LDSC, HDL, LAVA, and GPA. Potential pleiotropic single nucleotide variants (SNVs) were identified through PLACO analysis. FUMA and GCTA-COJO were further used to scrutinize. In addition, MAGMA, POPS, and SMR were utilized to investigate their functions, biological mechanisms, and expression across various cell types and tissues.

Results: Of the 120 trait pairs analyzed, 105 exhibited significant genome-wide genetic correlations or overlaps. We identified 864 pleiotropic loci and annotated 36 pleiotropic genes involved in immune response, cell activation, and signaling pathways. Notably, 11 pleiotropic genes (IL2RB, INSR, CD3G, CD247, CKB, PLCL1, STAT3, IL6R, SLAMF7, BLK, and HSPH1) were highlighted as potential therapeutic targets, reflecting their key roles in the shared genetic architecture of autoimmune and psychiatric disorders. In addition, 41 unique plasma proteins were associated with either condition.

Conclusions: This study provides new insights into the shared genetic landscape and biological mechanisms underlying autoimmune and psychiatric disorders. The identification of key pleiotropic genes offers promising avenues for future research and therapeutic intervention.

Fig. 1

Flowchart. MGAMA multi-marker analysis of genomic annotation, POPS polygenic priority score, SMR summary data-based Mendelian randomization, FUMA functional mapping and annotation of genetic associations, PLACO pleiotropic analysis under the composite null hypothesis, MGI mouse genome informatics

Fig. 2

Positive traits revealed after Bonferroni correction in three methods: LDSC, HDL, and GPA. Each circle represents a trait, and each edge represents a significant genetic correlation. Orange lines indicate negative correlations, while blue lines indicate positive correlations. The lighter the color, the closer the correlation is to 0. Thicker edges correspond to more significant P values. LDSC linkage disequilibrium score regression, HDL high-definition likelihood, GPA genetic analysis incorporating pleiotropy and annotation, SLE systemic lupus erythematosus, RA rheumatoid arthritis, PsA psoriatic arthritis, AS ankylosing spondylitis, MS multiple sclerosis, ALS amyotrophic lateral sclerosis, MG myasthenia gravis, T1D Type 1 diabetes, Hyperthyroidism autoimmune hyperthyroidism, Hypothyroidism autoimmune hypothyroidism, CD Crohn’s disease, UC ulcerative colitis, CeD celiac disease, IPF idiopathic pulmonary fibrosis, ADHD attention–deficit hyperactivity disorder, AN anorexia nervosa, ASD autism spectrum disorder, BD bipolar disorder, MDD major depressive disorder, OCD obsessive–compulsive disorder, PTSD posttraumatic stress disorder, SC schizophrenia

Fig. 3

A Venn diagram for the selection of pleiotropic genes using MAGMA, POPS, and SMR. B Visualization of results for the 36 pleiotropic genes in the SMR analysis. The correlation is based on beta and se values

Fig. 4

deTS analysis based on GTEx and ENCODE and parallel phenotypic enrichment analysis. The P values for deTS analysis are Bonferroni-corrected P values, and the phenotypic enrichment analysis uses the original P values