Expressive

rs10761716 - RNU6-543P - NRBF2

Magnitude 2.0 · 8 studies on file

Reported associations

  • 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%

  • GWAS and multi-omics integrative analysis reveal novel loci and their molecular mechanisms for circulating fatty acids - HGG advances (2025) · Sun Y, Xu H, Ye K · PubMed 40545721

    ABSTRACT: Summary Previous genome-wide association studies (GWAS) have identified genetic loci associated with the circulating levels of fatty acids (FAs), but the biological mechanisms of these genetic associations remain largely unexplored. Here, we conducted GWAS to identify additional genetic loci for 19 circulating FA traits in UK Biobank participants of European ancestry (n = 239,268) and five other ancestries (n = 508-4,663). We leveraged the GWAS findings to characterize genetic correlations and colocalized regions among FAs, explore sex differences, examine FA loci influenced by lipoprotein metabolism, and apply statistical fine-mapping to pinpoint putative causal variants. We integrated GWAS signals with multi-omics quantitative trait loci (QTL) to reveal intermediate molecular

  • A genetic map of human metabolism across the allele frequency spectrum - Nature genetics (2025) · Zoodsma M, Beuchel C, Yasmeen S, Kohleick L, Nepal A, Koprulu M, Kronenberg F, Mayr M, Williamson A, Pietzner M, Langenberg C · PubMed 41044249

    ABSTRACT: Genetic studies of human metabolism have been limited in scale and allelic breadth. Here we provide a data-driven map of the genetic regulation of circulating small molecules and lipoprotein characteristics (249 traits) measured using proton nuclear magnetic resonance spectroscopy across the allele frequency spectrum in ~450,000 individuals. Trans-ancestral meta-analyses identify 29,824 locus-metabolite associations mapping to 753 regions with effects largely consistent between men and women and large ancestral groups represented in UK Biobank. We observe and classify extreme genetic pleiotropy, identify regulators of lipid metabolism, and assign effector genes at >100 loci through rare-to-common allelic series. We propose roles for genes less established in metabolic control (

  • Genome-wide characterization of circulating metabolic biomarkers - Nature (2024) · Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, Surendran P, Zhang W, Jousilahti P, Kristiansson K, Salomaa V, Goodwin M, Hughes DA, Boehnke M, Fernandes Silva L, Yin X, Mahajan A, Neville MJ, van Zuydam NR, de Mutsert R, Li-Gao R, Mook-Kanamori DO, Demirkan A, Liu J, Noordam R, Trompet S, Chen Z, Kartsonaki C, Li L, Lin K, Hagenbeek FA, Hottenga JJ, Pool R, Ikram MA, van Meurs J, Haller T, Milaneschi Y, Kähönen M, Mishra PP, Joshi PK, Macdonald-Dunlop E, Mangino M, Zierer J, Acar IE, Hoyng CB, Lechanteur YTE, Franke L, Kurilshikov A, Zhernakova A, Beekman M, van den Akker EB, Kolcic I, Polasek O, Rudan I, Gieger C, Waldenberger M, Asselbergs FW, Hayward C, Fu J, den Hollander AI, Menni C, Spector TD, Wilson JF, Lehtimäki T, Raitakari OT, Penninx BWJH, Esko T, Walters RG, Jukema JW, Sattar N, Ghanbari M, Willems van Dijk K, Karpe F, McCarthy MI, Laakso M, Järvelin MR, Timpson NJ, Perola M, Kooner JS, Chambers JC, van Duijn C, Slagboom PE, Boomsma DI, Danesh J, Ala-Korpela M, Butterworth AS, Kettunen J · PubMed 38448586

    ABSTRACT: Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associa

  • Metabolomic investigation of major depressive disorder identifies a potentially causal association with polyunsaturated fatty acids - Biological psychiatry (2023) · Davyson E, Shen X, Gadd DA, Bernabeu E, Hillary RF, McCartney DL, Adams M, Marioni R, McIntosh AM · PubMed 36764567

    ABSTRACT: Background: Metabolic differences have been reported between individuals with and without Major Depressive Disorder (MDD), but their consistency and causal relevance has been unclear. Methods: We conducted a metabolome-wide association study of MDD with 249 metabolomic measures available in UK Biobank (N = 29, 757). We then applied 2-sample bidirectional Mendelian Randomisation (MR) and colocalization analysis to identify potentially causal relationships between each metabolite and MDD. Results: One hundred and ninety-one metabolites tested were significantly associated with MDD (PFDR < 0.05), which reduced to 129 after adjustment for likely confounders. Lower abundance of Omega-3 fatty acid measures and a higher Omega-6: Omega-3 ratio showed potentially causal effects on liabili

  • Genetic diversity fuels gene discovery for tobacco and alcohol use - Nature (2023) · Saunders GRB, Wang X, Chen F, Jang SK, Liu M, Wang C, Gao S, Jiang Y, Khunsriraksakul C, Otto JM, Addison C, Akiyama M, Albert CM, Aliev F, Alonso A, Arnett DK, Ashley-Koch AE, Ashrani AA, Barnes KC, Barr RG, Bartz TM, Becker DM, Bielak LF, Benjamin EJ, Bis JC, Bjornsdottir G, Blangero J, Bleecker ER, Boardman JD, Boerwinkle E, Boomsma DI, Boorgula MP, Bowden DW, Brody JA, Cade BE, Chasman DI, Chavan S, Chen YI, Chen Z, Cheng I, Cho MH, Choquet H, Cole JW, Cornelis MC, Cucca F, Curran JE, de Andrade M, Dick DM, Docherty AR, Duggirala R, Eaton CB, Ehringer MA, Esko T, Faul JD, Fernandes Silva L, Fiorillo E, Fornage M, Freedman BI, Gabrielsen ME, Garrett ME, Gharib SA, Gieger C, Gillespie N, Glahn DC, Gordon SD, Gu CC, Gu D, Gudbjartsson DF, Guo X, Haessler J, Hall ME, Haller T, Harris KM, He J, Herd P, Hewitt JK, Hickie I, Hidalgo B, Hokanson JE, Hopfer C, Hottenga J, Hou L, Huang H, Hung YJ, Hunter DJ, Hveem K, Hwang SJ, Hwu CM, Iacono W, Irvin MR, Jee YH, Johnson EO, Joo YY, Jorgenson E, Justice AE, Kamatani Y, Kaplan RC, Kaprio J, Kardia SLR, Keller MC, Kelly TN, Kooperberg C, Korhonen T, Kraft P, Krauter K, Kuusisto J, Laakso M, Lasky-Su J, Lee WJ, Lee JJ, Levy D, Li L, Li K, Li Y, Lin K, Lind PA, Liu C, Lloyd-Jones DM, Lutz SM, Ma J, Mägi R, Manichaikul A, Martin NG, Mathur R, Matoba N, McArdle PF, McGue M, McQueen MB, Medland SE, Metspalu A, Meyers DA, Millwood IY, Mitchell BD, Mohlke KL, Moll M, Montasser ME, Morrison AC, Mulas A, Nielsen JB, North KE, Oelsner EC, Okada Y, Orrù V, Palmer ND, Palviainen T, Pandit A, Park SL, Peters U, Peters A, Peyser PA, Polderman TJC, Rafaels N, Redline S, Reed RM, Reiner AP, Rice JP, Rich SS, Richmond NE, Roan C, Rotter JI, Rueschman MN, Runarsdottir V, Saccone NL, Schwartz DA, Shadyab AH, Shi J, Shringarpure SS, Sicinski K, Skogholt AH, Smith JA, Smith NL, Sotoodehnia N, Stallings MC, Stefansson H, Stefansson K, Stitzel JA, Sun X, Syed M, Tal-Singer R, Taylor AE, Taylor KD, Telen MJ, Thai KK, Tiwari H, Turman C, Tyrfingsson T, Wall TL, Walters RG, Weir DR, Weiss ST, White WB, Whitfield JB, Wiggins KL, Willemsen G, Willer CJ, Winsvold BS, Xu H, Yanek LR, Yin J, Young KL, Young KA, Yu B, Zhao W, Zhou W, Zöllner S, Zuccolo L, Batini C, Bergen AW, Bierut LJ, David SP, Gagliano Taliun SA, Hancock DB, Jiang B, Munafò MR, Thorgeirsson TE, Liu DJ, Vrieze S · PubMed 36477530

    ABSTRACT: Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in s

  • Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation - PLoS biology (2022) · Richardson TG, Leyden GM, Wang Q, Bell JA, Elsworth B, Davey Smith G, Holmes MV · PubMed 35213538

    ABSTRACT: Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to

  • Genomic insights in ascending aortic size and distensibility - EBioMedicine (2022) · Benjamins JW, Yeung MW, van de Vegte YJ, Said MA, van der Linden T, Ties D, Juarez-Orozco LE, Verweij N, van der Harst P · PubMed 34968759

    ABSTRACT: Summary Background Alterations in the anatomic and biomechanical properties of the ascending aorta (AAo) can give rise to various vascular pathologies. The aim of the current study is to gain additional insights in the biology of the AAo size and function. Methods We developed an AI based analysis pipeline for the segmentation of the AAo, and the extraction of AAO parameters. We then performed genome-wide association studies of AAo maximum area, AAo minimum area and AAo distensibility in up to 37,910 individuals from the UK Biobank. Variants that were significantly associated with AAo phenotypes were used as instrumental variables in Mendelian randomization analyses to investigate potential causal relationships with coronary artery disease, myocardial infarction, stroke and aneur

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