Background: Inflammatory bowel disease (IBD) and systemic lupus erythematosus (SLE) are autoimmune diseases that often coexist clinically. This phenomenon might be due to shared genetic components.
Methods: Genome-wide association study (GWAS) data for IBD and SLE were analyzed to determine both global and local genetic correlations using three methodologies: linkage disequilibrium score regression (LDSC), genetic covariance analyzer (GNOVA), and SUPERGNOVA. The genetic overlap and risk loci were subsequently examined using the conditional/conjunctional false discovery rate (cond/conjFDR) statistical framework. Furthermore, a multi-trait analysis of MTAG was employed to validate the loci, followed by an LDSC analysis focusing on tissue-specific gene expression.
Results: GWAS findings demonstrated a marked global genetic correlation between IBD (including Crohn's disease and ulcerative colitis) and SLE. Locally, SLE showed a strong association with IBD and Crohn's disease on chromosomes 10, 19, and 22. ConjFDR analysis confirmed the genetic overlap and identified relevant genetic risk loci. MTAG further validated several shared susceptibility genes. Additionally, the LDSC-SEG analysis results indicate that IBD (including CD and UC) and SLE are jointly enriched in the tissues of Spleen and Whole Blood.
Conclusion: This study confirms a genetic overlap between IBD and SLE, identifying marked comorbid genes and offering new insights for treating these diseases.
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http://dx.doi.org/10.1186/s12864-024-10787-0 | DOI Listing |
J Clin Endocrinol Metab
January 2025
Centre for Endocrinology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Context: Pachydermoperiostosis (primary hypertrophic osteoarthropathy, PHO) usually due to biallelic loss-of-function variants in HPGD and SLCO2A1, has some features overlapping with acromegaly and often referred to endocrinologists. A detailed endocrine assessment is not available for these patients.
Objective: To assess the genetic and endocrine characteristics of PHO patients referred to endocrine centres with a possible diagnosis of acromegaly.
Nat Hum Behav
January 2025
Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
We conducted a genome-wide association study on income among individuals of European descent (N = 668,288) to investigate the relationship between socio-economic status and health disparities. We identified 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes (the Income Factor). Our polygenic index captures 1-5% of income variance, with only one fourth due to direct genetic effects.
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January 2025
Department of Gastroenterology, the Second Hospital of Dalian Medical University, Dalian, 116021, China.
The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD.
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January 2025
Institute of Molecular Biology and Biotechnology (IMBB), The University of Lahore (UOL), Lahore, Pakistan.
Highly variable response shown by individuals against mosquito-borne infections suggests that host genetic factors play an important role in determining mosquito-borne disease onset. Therefore, it is necessary to determine the genetic risk of these diseases in specific populations. The current study aimed to determine the percentage of individuals in the general population carrying mosquito-borne disease susceptibility and protection-related variants.
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January 2025
School of Computer Science and Technology, Liaocheng University, Liaocheng, 252000, Shandong, P.R. China.
Copy number variation (CNV) is an important part of human genetic variations, which is associated with various kinds of diseases. To tackle the limitations of traditional CNV detection methods, such as restricted detection types, high error rates, and challenges in precisely identifying the location of variant breakpoints, a new method called MSCNV (copy number variations detection method for multi-strategies integration based on a one-class support vector machine model) is proposed. MSCNV establishes a multi-signal channel that integrates three strategies: read depth, split read, and read pair.
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