Cross-trait genetic architecture between breast cancer and psychiatric disorders

A genome-wide cross-trait analysis characterises the shared genetic architecture between rheumatoid arthritis and psychiatric disorders.

OBJECTIVES Patients with rheumatoid arthritis (RA) have a 2- to 3-fold elevated risk of psychiatric disorders, suggesting an underlying genetic link between these phenotypes. However, the shared genetic architectures and pathological mechanisms driving RA-psychiatric disorder comorbidity remain to be fully elucidated. Herein, we performed cross-trait analysis to investigate the shared genetic architecture between RA and psychiatric disorders. METHODS Leveraging European-ancestry genome-wide association studies (GWASs) datasets of RA (N = 1,026,690) and ten major psychiatric disorders (N = 14,307 to 1,222,882), we performed cross-trait pleiotropic analysis to identify the shared pleiotropic loci and genes between RA and psychiatric disorders, followed by functional annotation and Mendelian randomization analysis to explore the pathological mechanisms underlying RA-psychiatric disorder comorbidity. RESULTS Our analysis revealed significant positive genetic correlations between RA and seven psychiatric disorders, such as major depressive disorder. From these correlations, we identified 61 pleiotropic loci jointly influencing RA and psychiatric disorder risk, along with 208 pleiotropic genes predominantly involved in immune and inflammatory response biological processes. Druggable target exploration identified 21 drug-gene interactions involving pleiotropic genes, with two genes (RHOA and TRAF3) classified as clinically actionable category, representing potential therapeutic target for both RA and psychiatric disorders. Mendelian randomization further demonstrated a bidirectional causal relationship between RA and SCZ, while supporting the causal roles of ADHD, MDD, and PTSD in increasing RA risk. CONCLUSIONS Our findings elucidate the shared genetic architecture between RA and psychiatric disorders, providing novel insights into the pathological mechanisms underlying their comorbidity and laying groundwork for improved comorbidity management.

Genetic architecture shared between body shape phenotypes and preeclampsia-related diseases: a genome-wide cross-trait analysis.

This study indicates a genetic correlation and common risk genes, linking adiposity-related traits to PE-related diseases, and offers novel insights into the biological mechanisms underlying this comorbidity.

Multi-trait GWAS identifies pleiotropic loci shared between early pregnancy bleeding and psychiatric traits

Early pregnancy bleeding is a common pregnancy complication, yet its genetic basis and potential links with psychiatric traits remain poorly understood. This study aimed to characterize the shared genetic architecture between early pregnancy bleeding and reproductive, psychiatric, and cardiometabolic traits. We integrated linkage disequilibrium score regression (LDSC), local genetic correlation analysis (LAVA), and multi-trait genome-wide association analysis (MTAG). LDSC was used to estimate genome-wide genetic correlations, including sex-stratified analyses. LAVA was applied to identify genomic regions contributing to local genetic sharing. Guided by these correlation patterns, MTAG was performed to improve locus discovery, followed by cis-eQTL analysis using GTEx v8 to explore potential regulatory mechanisms. LDSC revealed significant positive genetic correlations between early pregnancy bleeding and reproductive traits, including endometriosis, miscarriage, and uterine fibroids. Strong positive correlations were also observed with several psychiatric disorders, including major depressive disorder, post-traumatic stress disorder, and attention deficit hyperactivity disorder. Sex-stratified analyses suggested stronger genetic correlations with emotional reactivity-related traits in females, whereas social and behavioral traits were more prominent in males. LAVA localized these shared signals to specific genomic regions and identified pleiotropic hotspots at 8q21 near RUNX1T1 and 9p21 near CDKN2A/B . MTAG identified two novel loci, 15q15.1 marked by rs45457497 and 11q13.1 marked by rs2452681. Cis-eQTL analysis showed that the lead variant at 15q15.1 regulates RMDN3 expression across multiple brain regions, while the 11q13.1 locus regulates PACS1, GAL3ST3 , and SF3B2 expression in brain tissues and the pituitary. These findings position early pregnancy bleeding as a multifactorial trait shaped by shared reproductive, psychiatric, neuroendocrine, and stress-related biology. The implication of RMDN3 , which encodes a mitochondrial outer membrane protein involved in ER-mitochondria tethering and calcium homeostasis, suggests a potential molecular link between neuroendocrine stress pathways, psychiatric susceptibility, and reproductive vulnerability.

Shared Genetic Basis of Autoimmune Diseases and Follicular Lymphoma by Mendelian Randomization and Multi-Omics

Background/Objectives: Epidemiological studies link autoimmune diseases (AIDs) to follicular lymphoma (FL) risk, but their shared genetic architecture and causal mechanisms remain unclear. Methods: A two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships between 15 AIDs and FL. Pleiotropic loci were identified through the Pleiotropy Analysis under Composite Null (PLACO). Bayesian colocalization analysis, functional mapping, and Multi-marker Analysis of GenoMic Annotation were applied to fine-map shared genetic variants and identify their target genes. Summary data-based MR was used with multitissue expression quantitative trait locus data to infer causal effects of gene expression. HyPrColoc analysis was applied to decipher shared genetic regulation of immune cell phenotypes. Results: MR revealed that rheumatoid arthritis increased FL risk (ORIVW = 1.55, nominal p = 7.16 × 10−5, FDR-corrected p = 1.07 × 10−3), whereas composite autoimmune disease reduced FL risk (ORIVW = 0.70, nominal p = 4.61 × 10−5, FDR-corrected p = 6.92 × 10−4). Hypothyroidism showed only a nominally suggestive protective trend (ORIVW = 0.88, nominal p = 0.015), which did not survive Benjamini–Hochberg multiple-testing correction (FDR-corrected p = 0.075). Fifty-five pleiotropic loci shared between FL and AIDs were identified, among which key loci such as 1p36.32, 6p21.32, 17p13.1, and 11q23.3 exhibited strong colocalization evidence. Core pleiotropic genes (e.g., TNFRSF14, MMEL1, CXCR5, and RNASET2) were prioritized, which implicated pathways related to MHC class II antigen presentation, interferon signaling, and T cell activation. HyPrColoc analysis demonstrated that these loci colocalized with the expression of immune receptors, including BAFF-R on B cells and HVEM (TNFRSF14) on naïve CD8+ T cells. Conclusions: Our study identifies divergent causal effects of selected AIDs on FL risk and demonstrates localized pleiotropy at key loci, providing novel insights into shared immunogenetic mechanisms.

A cross-ancestry genetic atlas of shared susceptibility between infectious diseases and cancer

Statistical integration of GWAS, eQTL and pQTL data for multi-omics analysis of Alzheimer's Disease

Alzheimer's Disease (AD) is a complex neurodegenerative disorder with a strong genetic architecture. Genome-Wide Association Studies (GWAS) have identified numerous susceptibility loci. However, the majority of associated variants reside in non-coding regions, making it difficult to resolve their functional consequences and identify causal genes. To address this limitation, integration of GWAS with expression Quantitative Trait Loci (eQTL) and protein Quantitative Trait Loci (pQTL) data has emerged as a key strategy for linking genetic variation to downstream molecular phenotypes. This review discusses statistical frameworks for multi-omics integration in AD research, with a focus on approaches that enable causal inference and gene prioritization. Major methods include colocalization analysis for detecting shared causal variants, Mendelian Randomization (MR) for assessing putative causal relationships, and Transcriptome-Wide Association Studies (TWAS) for linking genetically predicted gene expression to disease risk. Applications of these frameworks have facilitated the identification of candidate causal genes and proteins, thereby improving the mechanistic interpretation of AD-associated loci. However, challenges remain, including tissue specificity and cell-type specificity, limited ancestral diversity in available datasets, and constraints in causal inference. Emerging single-cell and spatial multi-omics approaches are expected to provide a more detailed characterization of AD-associated molecular mechanisms while supporting therapeutic target discovery.