rs10953758 - FOXP2

Magnitude 2.2 · 3 studies on file

Reported associations

• Novel genetic loci associated with osteoarthritis in multi-ancestry analyses in the Million Veteran Program and UK Biobank. - Nature genetics (2022) · McDonald MN, Lakshman Kumar P, Srinivasasainagendra V, Nair A, Rocco AP, Wilson AC, Chiles JW, Richman JS, Pinson SA, Dennis RA, Jagadale V, Brown CJ, Pyarajan S, Tiwari HK, Bamman MM, Singh JA · PubMed 36411363

  Osteoarthritis is a common progressive joint disease. As no effective medical interventions are available, osteoarthritis often progresses to the end stage, in which only surgical options such as total joint replacement are available. A more thorough understanding of genetic influences of osteoarthritis is essential to develop targeted personalized approaches to treatment, ideally long before the end stage is reached. To date, there have been no large multiancestry genetic studies of osteoarthritis. Here, we leveraged the unique resources of 484,374 participants in the Million Veteran Program and UK Biobank to address this gap. Analyses included participants of European, African, Asian and Hispanic descent. We discovered osteoarthritis-associated genetic variation at 10 loci and replicated

• Translational genomics of osteoarthritis in 1,962,069 individuals - Unknown journal (n.d.) · Unknown authors · PubMed 40205036

  ABSTRACT: Osteoarthritis is the third most rapidly growing health condition associated with disability, after dementia and diabetes. By 2050, the total number of patients with osteoarthritis is estimated to reach 1 billion worldwide. As no disease-modifying treatments exist for osteoarthritis, a better understanding of disease aetiopathology is urgently needed. Here we perform a genome-wide association study meta-analyses across up to 489,975 cases and 1,472,094 controls, establishing 962 independent associations, 513 of which have not been previously reported. Using single-cell multiomics data, we identify signal enrichment in embryonic skeletal development pathways. We integrate orthogonal lines of evidence, including transcriptome, proteome and epigenome profiles of primary joint tiss

• Genetic risk shared across 24 chronic pain conditions: Identification and characterization with genomic structural equation modeling - Unknown journal (n.d.) · Unknown authors · PubMed 37219871

  ABSTRACT: Chronic pain conditions frequently co-occur, suggesting common risks and paths to prevention and treatment. Previous studies have reported genetic correlations among specific groups of pain conditions and reported genetic risk for within-individual multi-site pain counts (≤7). Here, we identified genetic risk for multiple distinct pain disorders across individuals using 24 chronic pain conditions and genomic structural equation modeling (Genomic SEM). First, we ran individual genome-wide association studies (GWASs) on all 24 conditions in the UK Biobank (N ≤ 436,000) and estimated their pairwise genetic correlations. Then we used these correlations to model their genetic factor structure in Genomic SEM, employing both hypothesis- and data-driven exploratory approaches. A comp

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