Expressive

rs10131337 - PAX9

Magnitude 2.0 · 8 studies on file

Reported associations

  • Translational genomics of osteoarthritis in 1,962,069 individuals - Nature (2025) · Hatzikotoulas K, Southam L, Stefansdottir L, Boer CG, McDonald ML, Pett JP, Park YC, Tuerlings M, Mulders R, Barysenka A, Arruda AL, Tragante V, Rocco A, Bittner N, Chen S, Horn S, Srinivasasainagendra V, To K, Katsoula G, Kreitmaier P, Tenghe AMM, Gilly A, Arbeeva L, Chen LG, de Pins AM, Dochtermann D, Henkel C, Höijer J, Ito S, Lind PA, Lukusa-Sawalena B, Minn AKK, Mola-Caminal M, Narita A, Nguyen C, Reimann E, Silberstein MD, Skogholt AH, Tiwari HK, Yau MS, Yue M, Zhao W, Zhou JJ, Alexiadis G, Banasik K, Brunak S, Campbell A, Cheung JTS, Dowsett J, Faquih T, Faul JD, Fei L, Fenstad AM, Funayama T, Gabrielsen ME, Gocho C, Gromov K, Hansen T, Hudjashov G, Ingvarsson T, Johnson JS, Jonsson H, Kakehi S, Karjalainen J, Kasbohm E, Lemmelä S, Lin K, Liu X, Loef M, Mangino M, McCartney D, Millwood IY, Richman J, Roberts MB, Ryan KA, Samartzis D, Shivakumar M, Skou ST, Sugimoto S, Suzuki K, Takuwa H, Teder-Laving M, Thomas L, Tomizuka K, Turman C, Weiss S, Wu TT, Zengini E, Zhang Y, Ferreira MAR, Babis G, Baras A, Barker T, Carey DJ, Cheah KSE, Chen Z, Cheung JP, Daly M, de Mutsert R, Eaton CB, Erikstrup C, Furnes ON, Golightly YM, Gudbjartsson DF, Hailer NP, Hayward C, Hochberg MC, Homuth G, Huckins LM, Hveem K, Ikegawa S, Ishijima M, Isomura M, Jones M, Kang JH, Kardia SLR, Kloppenburg M, Kraft P, Kumahashi N, Kuwata S, Lee MTM, Lee PH, Lerner R, Li L, Lietman SA, Lotta L, Lupton MK, Mägi R, Martin NG, McAlindon TE, Medland SE, Michaëlsson K, Mitchell BD, Mook-Kanamori DO, Morris AP, Nabika T, Nagami F, Nelson AE, Ostrowski SR, Palotie A, Pedersen OB, Rosendaal FR, Sakurai-Yageta M, Schmidt CO, Sham PC, Singh JA, Smelser DT, Smith JA, Song YQ, Sørensen E, Tamiya G, Tamura Y, Terao C, Thorleifsson G, Troelsen A, Tsezou A, Uchio Y, Uitterlinden AG, Ullum H, Valdes AM, van Heel DA, Walters RG, Weir DR, Wilkinson JM, Winsvold BS, Yamamoto M, Zwart JA, Stefansson K, Meulenbelt I, Teichmann SA, van Meurs JBJ, Styrkarsdottir U, Zeggini E · 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

  • Combining cross-sectional and longitudinal genomic approaches to identify determinants of cognitive and physical decline - Nature communications (2025) · Schoeler T, Pingault JB, Kutalik Z · PubMed 40374629

    ABSTRACT: Large-scale genomic studies focusing on the genetic contribution to human aging have mostly relied on cross-sectional data. With the release of longitudinally curated aging phenotypes by the UK Biobank (UKBB), it is now possible to study aging over time at genome-wide scale. In this work, we evaluated the suitability of competing models of change in realistic simulation settings, performed genome-wide association scans on simulation-validated measures of age-related deweekcline, and followed up with LD-score regression and Mendelian Randomization (MR) analyses. Focusing on global cognitive and physical function, we observed marked differences between baseline function (θ) and accelerated decline (Δ). Both outcomes showed distinct heritability levels (e.g., 31.38% versus 3.15%

  • A scalable variational inference approach for increased mixed-model association power - Nature genetics (2025) · Loya H, Kalantzis G, Cooper F, Palamara PF · PubMed 39789286

    ABSTRACT: The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71%

  • Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures - Nature communications (2025) · Jee YH, Wang Y, Jung KJ, Lee JY, Kimm H, Duan R, Price AL, Martin AR, Kraft P · PubMed 40436827

    ABSTRACT: Genome-wide association studies (GWAS) have predominantly focused on European ancestry populations, limiting biological discoveries across diverse populations. Here we report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 301 previously unreported genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 4588 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotrop

  • Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program - Science (New York, N.Y.) (2024) · Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Kripke CM, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Shakt G, Sun YV, Tsao N, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao P, O'Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano JM, Madduri RK, Damrauer S, Liao KP · PubMed 39024449

    ABSTRACT: INTRODUCTION: Findings from genome-wide association studies (GWASs) have provided foundational knowledge of the genetic basis of disease, facilitating precision approaches for prevention and treatment. Current GWAS results are limited by underrepresentation of individuals from diverse populations, leading to concerns with generalizability regarding our knowledge of the relationships between genes, traits, and disease. The Department of Veterans Affairs (VA) Million Veteran Program (MVP), one of the largest US-based biobanks, addresses this need; 29% of MVP comprises individuals genetically similar to African (AFR), Admixed American (AMR), and East Asian (EAS) reference populations. With over 635,000 participants and more than 44.3M genotyped variants linked with detailed phenotyp

  • A Genomics England haplotype reference panel and imputation of UK Biobank - Nature genetics (2024) · Shi S, Rubinacci S, Hu S, Moutsianas L, Stuckey A, Need AC, Palamara PF, Caulfield M, Marchini J, Myers S · PubMed 39134668

    ABSTRACT: We built a reference panel with 342 million autosomal variants using 78,195 individuals from the Genomics England (GEL) dataset, achieving a phasing switch error rate of 0.18% for European samples and imputation quality of r2 = 0.75 for variants with minor allele frequencies as low as 2 × 10−4 in white British samples. The GEL-imputed UK Biobank genome-wide association analysis identified 70% of associations found by direct exome sequencing (P < 2.18 × 10−11), while extending testing of rare variants to the entire genome. Coding variants dominated the rare-variant genome-wide association results, implying less disruptive effects of rare non-coding variants. A Genomics England haplotype reference panel constructed using sequence data from 78,195 individuals

  • Tissue-specific genetic variation suggests distinct molecular pathways between body shape phenotypes and colorectal cancer - Science advances (2024) · Peruchet-Noray L, Sedlmeier AM, Dimou N, Baurecht H, Fervers B, Fontvieille E, Konzok J, Tsilidis KK, Christakoudi S, Jansana A, Cordova R, Bohmann P, Stein MJ, Weber A, Bézieau S, Brenner H, Chan AT, Cheng I, Figueiredo JC, Garcia-Etxebarria K, Moreno V, Newton CC, Schmit SL, Song M, Ulrich CM, Ferrari P, Viallon V, Carreras-Torres R, Gunter MJ, Freisling H · PubMed 38640244

    ABSTRACT: It remains unknown whether adiposity subtypes are differentially associated with colorectal cancer (CRC). To move beyond single-trait anthropometric indicators, we derived four multi-trait body shape phenotypes reflecting adiposity subtypes from principal components analysis on body mass index, height, weight, waist-to-hip ratio, and waist and hip circumference. A generally obese (PC1) and a tall, centrally obese (PC3) body shape were both positively associated with CRC risk in observational analyses in 329,828 UK Biobank participants (3728 cases). In genome-wide association studies in 460,198 UK Biobank participants, we identified 3414 genetic variants across four body shapes and Mendelian randomization analyses confirmed positive associations of PC1 and PC3 with CRC risk (52,77

  • Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation - Nature human behaviour (2024) · Carey CE, Shafee R, Wedow R, Elliott A, Palmer DS, Compitello J, Kanai M, Abbott L, Schultz P, Karczewski KJ, Bryant SC, Cusick CM, Churchhouse C, Howrigan DP, King D, Davey Smith G, Neale BM, Walters RK, Robinson EB · PubMed 38965376

    ABSTRACT: Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and

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