Search Results (2)

Background: Ankylosing Spondylitis (AS) is a rare autoinflammatory disorder affecting 0.1–1.4% of the population, with increasing recognition over the past 20 years. Although the specific causes of AS remain unclear, the presence of the HLA-B27 gene is associated with increased risk, though only 1–5% of carriers develop the disease. Despite extensive research, no definitive lab tests exist, and many patients are diagnosed years after symptom onset. Methods: In the present study, in order to investigate the disease’s genetic background in correlation with autoimmune diseases, a metanalysis has been performed following PRISMA guidelines using Scopus and PubMed publications towards extracting single-nucleotide polymorphisms (SNPs) of high importance for the disease. Moreover, the polymorphisms have been annotated and analyzed using information from several databases, including PubMed, LitVar2, ClinVar, and Gene Ontology. Results: From 1940 screened titles and abstracts, 57,909 studies were selected, with 539 meeting the inclusion criteria. The genetic background of AS is described through 794 genetic variants, of which 76 SNPs are directly associated with AS (Classes A and B), predominantly located in intronic regions. ERAP1 and IL23R emerged as key genes implicated in AS, while chromosomes 1, 2, and 5 accumulated the most associated SNPs. Functional enrichment revealed strong associations with immune regulation and interleukin signaling pathways, particularly IL6 and IL10 signaling. IL-6 promotes inflammation in AS, while IL-10 tries to suppress it, acting as an anti-inflammatory cytokine. Of the 78 AS-related SNPs, 16 were unique to AS, while 66 were common to autoimmune diseases, especially rheumatoid arthritis (RA) and psoriasis (PsO), suggesting genetic overlap between these diseases. Conclusions: This study creates a comprehensive genetic map of AS-associated SNPs, highlighting key pathways and genetic overlap with autoimmune diseases. These findings contribute to understanding disease mechanisms and could guide therapeutic interventions, advancing precision medicine in AS management. Full article

Linear mixed models (LMMs) are commonly used in genome-wide association studies (GWASs) to evaluate population structures and relatedness. However, LMMs have been shown to be ineffective in controlling false positive errors for the analysis of resistance to Columnaris disease in Rainbow Trout. To solve this problem, we conducted a series of studies using generalized linear mixed-model association software such as GMMAT (v1.4.0) (generalized linear mixed-model association tests), SAIGE (v1.4.0) (Scalable and Accurate Implementation of Generalized mixed model), and Optim-GRAMMAR for scanning a total of 25,853 SNPs. Seven different SNPs (single-nucleotide polymorphisms) associated with the trait of resistance to Columnaris were detected by Optim-GRAMMAR, four SNPs were detected by GMMAT, and three SNPs were detected by SAIGE, and all of these SNPs can explain 8.87% of the genetic variance of the trait of resistance to Columnaris disease. The heritability of the trait of resistance to Columnaris re-evaluated by GMMAT was calibrated and was found to amount to a total of 0.71 other than 0.35, which was seriously underestimated in previous research. The identification of LOC110520307, LOC110520314, and LOC110520317 associated with the resistance to Columnaris disease will provide us more genes to improve the genetic breeding by molecular markers. Finally, we continued the haplotype and gene-based analysis and successfully identified some haplotypes and a gene (TTf-2) associated with resistance to Columnaris disease. Full article