Browsing by Subject "TYPE-2 DIABETES RISK"

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  • Horikoshi, Momoko; Maegi, Reedik; van de Bunt, Martijn; Surakka, Ida; Sarin, Antti-Pekka; Mahajan, Anubha; Marullo, Letizia; Thorleifsson, Gudmar; Haegg, Sara; Hottenga, Jouke-Jan; Ladenvall, Claes; Ried, Janina S.; Winkler, Thomas W.; Willems, Sara M.; Tsernikova, Natalia; Esko, Tonu; Beekman, Marian; Nelson, Christopher P.; Willenborg, Christina; Wiltshire, Steven; Ferreira, Teresa; Fernandez, Juan; Gaulton, Kyle J.; Steinthorsdottir, Valgerdur; Hamsten, Anders; Magnusson, Patrik K. E.; Willemsen, Gonneke; Milaneschi, Yuri; Robertson, Neil R.; Groves, Christopher J.; Bennett, Amanda J.; Lehtimaeki, Terho; Viikari, Jorma S.; Rung, Johan; Lyssenko, Valeriya; Perola, Markus; Heid, Iris M.; Herder, Christian; Grallert, Harald; Mueller-Nurasyid, Martina; Roden, Michael; Hypponen, Elina; Isaacs, Aaron; van Leeuwen, Elisabeth M.; Karssen, Lennart C.; Mihailov, Evelin; Kaprio, Jaakko; Eriksson, Johan G.; Groop, Leif; Ripatti, Samuli; ENGAGE Consortium (2015)
    Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency >= 0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.
  • Vinuela, Ana; Varshney, Arushi; van de Bunt, Martijn; Prasad, Rashmi B.; Asplund, Olof; Bennett, Amanda; Boehnke, Michael; Brown, Andrew A.; Erdos, Michael R.; Fadista, Joao; Hansson, Ola; Hatem, Gad; Howald, Cedric; Iyengar, Apoorva K.; Johnson, Paul; Krus, Ulrika; MacDonald, Patrick E.; Mahajan, Anubha; Manning Fox, Jocelyn E.; Narisu, Narisu; Nylander, Vibe; Orchard, Peter; Oskolkov, Nikolay; Panousis, Nikolaos I.; Payne, Anthony; Stitzel, Michael L.; Vadlamudi, Swarooparani; Welch, Ryan; Collins, Francis S.; Mohlke, Karen L.; Gloyn, Anna L.; Scott, Laura J.; Dermitzakis, Emmanouil T.; Groop, Leif; Parker, Stephen C. J.; McCarthy, Mark I. (2020)
    Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.
  • Mahajan, Anubha; Sim, Xueling; Ng, Hui Jin; Manning, Alisa; Rivas, Manuel A.; Highland, Heather M.; Locke, Adam E.; Grarup, Niels; Im, Hae Kyung; Cingolani, Pablo; Flannick, Jason; Fontanillas, Pierre; Fuchsberger, Christian; Gaulton, Kyle J.; Teslovich, Tanya M.; Rayner, N. William; Robertson, Neil R.; Beer, Nicola L.; Rundle, Jana K.; Bork-Jensen, Jette; Ladenvall, Claes; Blancher, Christine; Buck, David; Buck, Gemma; Burtt, Noel P.; Gabriel, Stacey; Gjesing, Anette P.; Groves, Christopher J.; Hollensted, Mette; Huyghe, Jeroen R.; Jackson, Anne U.; Jun, Goo; Justesen, Johanne Marie; Mangino, Massimo; Murphy, Jacquelyn; Neville, Matt; Onofrio, Robert; Small, Kerrin S.; Stringham, Heather M.; Syvanen, Ann-Christine; Trakalo, Joseph; Abecasis, Goncalo; Bell, Graeme I.; Blangero, John; Cox, Nancy J.; Duggirala, Ravindranath; Isomaa, Bo; Tuomi, Tiinamaija; Tuomilehto, Jaakko; Groop, Leif; T2D-GENES Consortium; Go-T2D Consortium (2015)
  • Sliz, Eeva; Sebert, Sylvain; Würtz, Peter; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Kähönen, Mika; Viikari, Jorma; Männikkö, Minna; Ala-Korpela, Mika; Raitakari, Olli T.; Kettunen, Johannes (2018)
    Fatty liver has been associated with unfavourable metabolic changes in circulation. To provide insights in fatty liver-related metabolic deviations, we compared metabolic association profile of fatty liver versus metabolic association profiles of genotypes increasing the risk of non-alcoholic fatty liver disease (NAFLD). The cross-sectional associations of ultrasound-ascertained fatty liver with 123 metabolic measures were determined in 1810 (N-fatty liver = 338) individuals aged 34-49 years from The Cardiovascular Risk in Young Finns Study. The association profiles of NAFLD-risk alleles in PNPLA3, TM6SF2, GCKR, and LYPLAL1 with the corresponding metabolic measures were obtained from a publicly available metabolomics GWAS including up to 24 925 Europeans. The risk alleles showed different metabolic effects: PNPLA3 rs738409-G, the strongest genetic NAFLD risk factor, did not associate with metabolic changes. Metabolic effects of GCKR rs1260326-T were comparable in many respects to the fatty liver associations. Metabolic effects of LYPLAL1 rs12137855-C were similar, but statistically less robust, to the effects of GCKR rs1260326-T. TM6SF2 rs58542926-T displayed opposite metabolic effects when compared with the fatty liver associations. The metabolic effects of the risk alleles highlight heterogeneity of the molecular pathways leading to fatty liver and suggest that the fatty liver-related changes in the circulating lipids and metabolites may vary depending on the underlying pathophysiological mechanism. Despite the robust cross-sectional associations on population level, the present results showing neutral or cardioprotective metabolic effects for some of the NAFLD risk alleles advocate that hepatic lipid accumulation by itself may not increase the level of circulating lipids or other metabolites.