Last updated:
Author(s):
Siyi Liu, Yu Zeng, Liling Xiao, Yulu Wu, Can Hou, Huazhen Yang, Menghan Wei, Yiguo Tang, Yunqi Huang, Yunjia Liu, Qianshu Ma, Yubing Yin, Mengting Zhang, Yang Chen, Huan Song, Qiang Wang
Publish date:
1 June 2026
Journal:
Translational Psychiatry
PubMed ID:
42225622

Abstract

Major depressive disorder (MDD) and autoimmune thyroid disease (AITD) frequently co-occur, yet the genetic factors underlying their comorbidity remain unclear. We performed a population-matched cohort study from the UK Biobank to evaluate the phenotypic association between MDD and AITD. Genetic correlation, causal relationships, and pleiotropic loci/genes shared between the diseases were assessed based on common variants using genome-wide association study (GWAS) summary statistics, complemented by individual-level validation through polygenic risk score analysis. We additionally performed an exome-wide association analysis using the UK Biobank 450k whole-exome sequencing (WES) release to identify disease-specific risk genes from rare variants. Findings from common and rare variants were integrated and subjected to pathway enrichment, protein-protein interaction (PPI) and transcription factor (TF) analyses to locate functional modules. In the cohort study, MDD and AITD were associated with a 2.8-fold increased risk of developing the other condition. We confirmed a modest but statistically significant positive genetic correlation (rg = 0.14, P = 2.96 × 10−9) and confirmed the absence of a direct causal relationship. Integrative pleiotropy analyses identified 14 pleiotropic loci mapped to 58 shared genes. Gene ontology, TF enrichment and PPI analyses of disease-specific and shared genes revealed that the genetic signals converge on shared modules involving T-cell receptor signaling, thyroid hormone metabolic process and neurodegeneration, prioritized by key immune-inflammatory and neuro-developmental regulators. Our findings provide a molecular framework for MDD-AITD comorbidity, highlighting specific pathways as potential targets for integrated therapeutic strategies.

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Institution:
Sichuan University, China

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