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    Home > Active Ingredient News > Immunology News > Cell 820,000 GWAS meta analysis reveals the genetic risk of osteoarthritis and new drug targets

    Cell 820,000 GWAS meta analysis reveals the genetic risk of osteoarthritis and new drug targets

    • Last Update: 2021-10-02
    • Source: Internet
    • Author: User
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    Written | Qi osteoarthritis is one of the main causes of disability and pain worldwide, affecting more than 300 million people
    .

    Osteoarthritis is a complex degenerative disease of the entire joint, characterized by thickening of the subchondral bone, inflammation of the synovial membrane, and structural changes in the joint capsule, ligaments and related muscles
    .

    In recent years, the use of genome-wide association studies has made progress in elucidating the genetic background of osteoarthritis.
    So far, 96 statistically independent risk variants have been reported, but these variants can only explain a small part of the phenotypic variants, and mainly It is related to osteoarthritis of the knee and hip joints [1]
    .

    In addition, the increase in body mass index (BMI) is also associated with disease risk [2], and a better understanding of the genetic differences between weight-bearing (knee, hip, and spine) and non-weight-bearing joints (finger and thumb) is needed to help Unravel the metabolic and biomechanical effects that lead to disease development
    .

    Overall, there is currently no curative treatment for osteoarthritis, and the clinical focus is on pain relief and arthroplasty to relieve symptoms
    .

    Therefore, there is an urgent need to understand the causes of the disease and the targets of new drugs in detail
    .

    On August 26, 2021, the Eleftheria Zeggini team from the National University Hospital of Iceland published an article in the Cell magazine entitled Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.
    The GWAS meta-analysis of the cohort emphasized the influence of joint location types on the characteristics of genetic risk variation and the link between osteoarthritis genetics and pain-related phenotypes, and pointed out potential targets for therapeutic intervention
    .

    First, the team performed GWAS meta-analysis on as many as 826,690 individuals (177,517 patients with osteoarthritis), defined 11 phenotypes including almost all diseased parts of osteoarthritis, and identified 11,897 genome-wide significantly related single phenotypes.
    Nucleotide variants (single nucleotide variants, SNVs)
    .

    Conditional analysis was used to determine non-overlapping associations in the definition of the disease phenotype.
    52 of the 100 independent variant associations were previously unknown genetic risk variants associated with any osteoarthritis phenotype
    .

    At the same time, the team also determined the presence of signals related to gender and early-onset osteoarthritis
    .

    Interestingly, some variants exhibit joint-specific effects, that is, although most SNVs (60 out of 100) are significantly associated with more than one osteoarthritis phenotype on a genome-wide scale, 40 One identified SNVs only showed significant associations with weight-bearing joints, and 4 SNVs only showed significant associations with non-weight-bearing joints
    .

    The signals represented by SNVs associated with multiple osteoarthritis sites may suggest common underlying mechanisms in the pathology of osteoarthritis, and may also serve as major candidates for drug development
    .

    In addition to confirming that these genetic risks are related to the location of arthritis, the team also found a strong correlation between the genetic component of osteoarthritis and the pain phenotype, and determined the signal enrichment in the neural pathway
    .

    Next, the team mapped the GWAS signal to a small group of possible causal variation, identified related tissues based on signal enrichment, and provided insights and mechanisms based on colocalization of expression quantitative trait locus (eQTL) Causal inference analysis
    .

    To further understand the biological role of the 77 high-confidence effector genes identified in the disease process, the team integrated additional information based on endophenotypic analysis that is more closely related to potential biology, single gene, and rare human disease data.
    Full phenotypic analysis and additional functional genomic data
    .

    Most high-confidence effector genes are related to bone development and joint degradation, and some such as SNARE, MTMR2 and CREBBP are related to synaptic transmission, neuron development, neuro-muscle signal transduction, neuron differentiation and precursor cell migration, etc.
    pathway, also have some of the effector gene immune or inflammatory effect, e.
    g.
    TLR4, NR3C1, TNFSF11 the like
    .

    Based on the results of fine positioning or functional analysis, the team further examined the druggability status of these possible effector genes.
    Among them, the molecules encoded by 71 related genes are the targets of approved (licensed) drugs and clinically developed drugs.
    These findings are important for these potential effects.
    The application of therapy provides evidence support
    .

    In general, this study demonstrates significant differences between different groups of patients with osteoarthritis, for example based on the severity of the disease, the affected joints, and gender
    .

    At the same time, it also enhances the understanding of the genetic causes of the disease, and provides a stepping stone for the transformation of genetic associations into the development of osteoarthritis drugs, thereby helping to improve the quality of life of patients with osteoarthritis
    .

    Original link: https://doi.
    org/10.
    1016/j.
    cell.
    2021.
    07.
    038 Platemaker: 11 References 1.
    Styrkarsdottir, U.
    , Lund, SH, Thorleifsson, G.
    , Zink, F.
    , Stefansson, OA, Sigurdsson, JK, Juliusson, K.
    , Bjarnadottir, K.
    , Sigurbjornsdottir, S.
    , Jonsson, S.
    , et al.
    (2018).
    Meta-analysis of Icelandic and UK data sets identify missense variants in SMO, IL11, COL11A1 and 13 more new loci associated with osteoarthritis.
    Nat.
    Genet.
    50, 1681–1687.
    2.
    Geusens, PP, and van den Bergh, JP (2016).
    Osteoporosis and osteoarthritis: shared mechanisms and epidemiology.
    Curr.
    Opin.
    Rheumatol.
    28 , 97–103.
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