Jul 2026· Journal of environmental biology· Vol 47, pp. 876-883· 0 citations
TL;DR
Multiple genetic loci were identified, reaffirming the contribution of immune dysregulation and impaired skin barrier function in psoriasis and reaffirming the contribution of immune dysregulation and impaired skin barrier function in psoriasis.
Abstract
Aim: To identify and functionally characterise key genetic susceptibility loci for psoriasis using GWAS data integrated with pathway, network, and regulatory analyses.
Methodology: GWAS datasets were systematically analysed to identify genes and genetic variants associated with psoriasis. Functional enrichment analyses were performed to delineate key biological pathways, regulatory elements, and functional annotations. Furthermore, transcription factor and microRNA interactions with psoriasis-related genes were evaluated to provide mechanistic insights.
Results: Multiple genetic loci were identified, reaffirming the contribution of immune dysregulation and impaired skin barrier function in psoriasis. Functional enrichment highlighted significant pathways including cytokine signalling, NF-κB activation, and keratinocyte proliferation. Several transcription factors and miRNAs regulating psoriasis-associated genes were also identified, offering additional layers of gene expression control.
Interpretation: Integration of GWAS findings with pathway and regulatory network analyses enhances the understanding of psoriasis genetics. Novel susceptibility genes, transcriptional regulators, and potential biomarkers have been identified, which may serve as therapeutic targets. Future research should focus on experimental validation and clinical translation to advance precision medicine approaches in psoriasis management.
Key words: Genetic susceptibility, GWAS Psoriasis, Immune-related loci, Inflammatory skin disease, Psoriasis genetics
Background Psoriasis is a chronic immune-mediated inflammatory skin disease driven by complex interactions between genetic susceptibility, immune dysregulation, and keratinocyte dysfunction. Although genome-wide association studies have identified numerous risk loci, most studies have focused on genetic associations rather than causal inference, limiting the identification of effective therapeutic targets. We employed an integrative multi-omics strategy to identify causal genes and potential therapeutic candidates for psoriasis. Methods We integrated psoriasis genome-wide association study (GWAS), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) datasets to perform two-sample Mendelian Randomization (MR) analyses and prioritize high-confidence causal genes. Most genetic datasets were derived from European-ancestry populations. Single-cell transcriptomic analysis was then used to determine their cell-type-specific expression patterns. Finally, drug-target prediction and molecular docking were applied to identify potential therapeutic compounds. Results Integrative MR analyses indicated that genetically predicted higher BLMH expression and plasma protein levels were significantly associated with reduced psoriasis risk, with colocalization analysis supporting shared causal variants at the BLMH locus. Single-cell transcriptomics further demonstrated keratinocyte-enriched expression of BLMH and marked downregulation in psoriatic lesional skin. Drug screening using DSigDB combined with molecular docking identified tamibarotene as a potential BLMH-binding compound with favorable docking affinity. In vitro experiments showed that tamibarotene treatment was associated with increased BLMH expression and reduced KRT16, IL6, and IL8 expression in the M5-stimulated HaCaT model. Conclusion Our integrative multi-omics analysis nominates BLMH as a keratinocyte-enriched protective candidate gene in psoriasis and suggests tamibarotene as a potential repurposed compound for further investigation. However, the experimental validation remains preliminary, and further mechanistic, in vivo, and translational studies are required to validate these findings.
Rui Shen, Bin-Yi Ran, Jia-Zheng Liu et al.· Journal of Inflammation Rese...· 0 citations
Background Genome-wide association studies have identified >200 loci associated with IBD, yet the causal gene for most remains unknown. As multiple epithelial functions have been linked with susceptibility to IBD, there is a need to prioritize candidate causal genes for functional studies in this cellular context. Methods Using a standardized definition of regions implicated by index SNPs from three GWAS studies, we categorized regions as containing: (1) a known casual gene, (2) a single gene or (3) multiple genes. We then developed an IBD Priority Score to rank genes based on genetic, genomic and functional data. We next developed and applied an Epithelial Priority Score, based on expression patterns and quantitative traits, to prioritize genes for functional validation in epithelial models. Two candidate genes identified through this approach were tested for their impact on viral response pathways in HT-29 cells. Results The IBD Priority Score prioritized a single gene in 71 of the 104 regions containing multiple genes. The Epithelial Priority Score identified 31 epithelial candidates. Functional studies demonstrated that IRF6 enhanced, whereas IRF8 suppressed, antiviral responses in intestinal epithelial cells stimulated with Poly(I:C). Conclusions Combining multiple genetic, genomic, and functional data is a useful approach for prioritizing the most likely causal gene within IBD GWAS loci, and for prioritizing functional validation studies in epithelial cells and tissues. Moreover, we provide functional evidence for two IBD genes playing a role in the regulation of anti-viral responses in intestinal epithelial cells.
Isabelle Hébert-Milette, Virginie Mercier, Jean Paquette et al.· medRxiv· 0 citations
The complex interplay of genetic, metabolic and immune-related processes influences chronic diseases. Even though genomic research has found susceptibility loci to specific conditions, there have been fewer studies that combine variant level, pathway-level, and cumulative risk using a single framework to examine chronic disease susceptibility. This study aimed to investigate genomic variation associated with susceptibility patterns and examine its implications for personalised medicine using type 2 diabetes mellitus as a model chronic disease. A secondary analysis was conducted using SNP genotype data from the GEO dataset GSE226084. Following quality control, principal component analysis was applied for dimensionality reduction, and K-means clustering was used to derive genomic susceptibility groups. Logistic regression identified associated SNPs, which were subsequently annotated, aggregated at the gene level, and evaluated through pathway enrichment analysis. A weighted genetic risk score was calculated to assess cumulative genetic burden across clusters. Two distinct genomic clusters were identified, comprising 63 and 243 individuals. Among 3,733 tested SNPs, 871 remained significant after false discovery rate correction. Key loci included HLA-DPA1, ETV6, TRIM15, TRIM26, and TCF7L2, with notable signal concentration on chromosome 6. Enrichment analysis revealed pathways related to immune regulation, inflammatory response, and cellular signaling. Genetic risk scores differed markedly between clusters, with one group exhibiting consistently higher cumulative genetic burden. These findings demonstrate that genomic susceptibility is organised into biologically distinct profiles defined by coordinated variant, gene, pathway, and cumulative risk signals. Integrating these layers provides a practical basis for risk stratification and supports the application of genomics in personalised medicine.
Bana Sarahbibi Mohmedsalim, Subhabrata Sarkar, Sukanta Bandyopadhyay et al.· Genetics and Molecular Resea...· 0 citations
Rheumatoid arthritis (RA) is an autoimmune disease involving the interaction of genetic and immunological factors. Genome-wide association studies (GWAS) have identified many RA risk loci, but the biological mechanisms linking genetic variation to disease pathogenesis are not yet fully understood. This study aims to prioritize RA candidate genes through a multi-database bioinformatics approach. SNPs significantly associated with RA were obtained from the GWAS catalog, followed by linkage disequilibrium (LD) screening and functional annotation to identify missense variants. The data were integrated with cis-expression quantitative trait loci (cis-eQTL) information, gene ontology (GO) annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) molecular pathway mapping. Genes with a total score ≥2 were classified as RA risk genes. A total of 3.145 RA-significant SNPs were identified, of which 58 were missense variants that could potentially affect protein function. The integration of cis-eQTL and functional annotation resulted in a number of candidate genes with the highest scores (score = 4), where TYK2, IL23R, and IRAK1 were identified as priority RA genes in the main immune pathways, namely JAK-STAT signaling, IL-23/Th17 axis, and Toll-like receptor-NF-κB signaling. These findings demonstrate that this multi-database-based bioinformatics approach successfully identifies RA candidate genes with strong biological relevance.
Muhammad Nuh, L. Lolita· International Journal of Pub...· 0 citations
Background Endometriosis (EMS) and inflammatory bowel disease (IBD) are both chronic inflammatory disorders with overlapping clinical features, suggesting a potentially shared etiology. Although epidemiological studies have reported an association between the two conditions, the genetic basis of this relationship remains poorly understood. This study is aimed at characterizing the shared genetic architecture of EMS and IBD, identify pleiotropic loci implicated in both conditions, and explore the potential causal relationships and underlying biological mechanisms. Methods We analyzed large‐scale GWAS summary statistics to investigate the genetic relationship between EMS and IBD. Linkage‐disequilibrium score regression (LDSC) was used to estimate genetic correlation. The PLACO method was applied to identify pleiotropic loci, which were further evaluated by Bayesian colocalization. Functional implications were assessed using MAGMA gene‐set analysis, tissue enrichment analysis, and functional annotation. Bidirectional two‐sample Mendelian randomization (MR) was performed to examine causal inference, accompanied by leave‐one‐out sensitivity analyses and statistical power calculations. Results A statistically significant but modest positive genetic correlation was observed between EMS and IBD (rg = 0.0957, p = 0.0042). Twenty‐four genomic loci showed evidence of pleiotropic effects, and six of these carried strong evidence of a shared causal variant (posterior probability: PP.H4 > 0.7), including loci at 2p23.3 (ADCY3/DNAJC27), 4q12, 5q23.3, 7p15.2, 11q13.1 (CCDC88B), and 1q22. Functional analyses consistently implicated eight pleiotropic genes across multiple platforms: ADCY3, CCDC88B, GREB1, NOD2, RSPO3, SYNE1, THADA, and TRAIP. Pathway enrichment highlighted “positive regulation of gene expression” as a key shared mechanism (p < 0.05). Tissue‐specific analyses revealed strong signals in whole blood, spleen, and colon, consistent with the immunoinflammatory etiology of both diseases. Bidirectional MR did not support a causal relationship in either direction (IBD on EMS: OR = 1.007, 95% CI: 0.986–1.028, p = 0.543; EMS on IBD: OR = 1.054, 95% CI: 0.991–1.122, p = 0.092). Leave‐one‐out analyses confirmed that no single variant drove these estimates, and power analysis indicated adequate power to detect moderate causal effects. Conclusions The findings provide evidence for a shared, albeit modest, and genetic basis between EMS and IBD that is likely mediated through immune regulation, hormonal signaling, and tissue repair. The absence of significant causal effects in MR suggests that the observed comorbidity reflects common genetic susceptibility rather than a direct causal link. These results offer new insight into the mechanisms underlying the co‐occurrence of the two conditions and may inform future investigation of shared therapeutic targets.
Xinyue Cui, Jian-Hong Wang, Jingjing Chen et al.· BioMed Research Internationa...· 0 citations
Abstract Pleiotropic genetic loci have been increasingly reported in cancer, and identifying genetic variants with pleiotropic associations can reveal shared biological pathways influencing multiple cancers. Using summary statistics from genome-wide association studies for 37 cancer types (N = 433 836), we identified extensive genome-wide and local genetic correlations among cancers. Through pairwise pleiotropic analysis, we identified 75 243 significant pleiotropic single nucleotide polymorphisms (SNPs) across 372 cancer pairs, among which 3472 were lead SNPs with potential regulatory functions. Using FUMA and MAGMA, we identified 2527 pleiotropic risk loci and 4272 candidate pleiotropic genes. Notably, genes such as TERT (5p15.33), POU5F1B (8q24.21), and FANCA (16q24.3) exhibited widespread pleiotropy across multiple cancer types. Pathway enrichment analysis highlighted the critical roles of pigment synthesis, metabolism, and apoptosis in skin-related cancers, while cross-cancer enrichment analysis emphasized pathways related to apoptosis, chromatin structure, and intermediate filaments. We also identified 33 novel functional genes harboring previously unreported cancer risk variants. Drug-gene interaction analysis revealed several repositionable FDA-approved drugs. Importantly, drug sensitivity assays demonstrated that bosutinib and cobimetinib exhibited promising therapeutic potential in breast cancer cell lines. Finally, we developed the PleioCancer database (https://gonglab.hzau.edu.cn/PleioCancer/), providing a comprehensive resource for cancer pleiotropy research. These findings have important implications for carcinogenesis cancer, prevention and treatment.
Unknown authors· Briefings in Bioinformatics· 0 citations
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