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Downie CG, Dimos SF, Bien SA, et al. Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study. Diabetologia. 2021;doi: 10.1007/s00125-021-05635-9.
Downie, C. G., Dimos, S. F., Bien, S. A., Hu, Y., Darst, B. F., Polfus, L. M., Wang, Y., Wojcik, G. L., Tao, R., Raffield, L. M., Armstrong, N. D., Polikowsky, H. G., Below, J. E., Correa, A., Irvin, M. R., Rasmussen-Torvik, L. J. F., Carlson, C. S., Phillips, L. S., Liu, S., Pankow, J. S., Rich, S. S., Rotter, J. I., Buyske, S., Matise, T. C., North, K. E., Avery, C. L., Haiman, C. A., Loos, R. J. F., Kooperberg, C., Graff, M., & Highland, H. M. (2021). Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study. Diabetologia, . https://doi.org/10.1007/s00125-021-05635-9
Downie, Carolina G, et al. "Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study." Diabetologia vol. (2021). doi: https://doi.org/10.1007/s00125-021-05635-9
Downie CG, Dimos SF, Bien SA, Hu Y, Darst BF, Polfus LM, Wang Y, Wojcik GL, Tao R, Raffield LM, Armstrong ND, Polikowsky HG, Below JE, Correa A, Irvin MR, Rasmussen-Torvik LJF, Carlson CS, Phillips LS, Liu S, Pankow JS, Rich SS, Rotter JI, Buyske S, Matise TC, North KE, Avery CL, Haiman CA, Loos RJF, Kooperberg C, Graff M, Highland HM. Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study. Diabetologia. 2021 Dec 24; doi: 10.1007/s00125-021-05635-9. Epub 2021 Dec 24. PMID: 34951656.
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Diabetologia. 2021 Dec 24; doi: 10.1007/s00125-021-05635-9. Epub 2021 Dec 24.

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study.

Diabetologia

Carolina G Downie, Sofia F Dimos, Stephanie A Bien, Yao Hu, Burcu F Darst, Linda M Polfus, Yujie Wang, Genevieve L Wojcik, Ran Tao, Laura M Raffield, Nicole D Armstrong, Hannah G Polikowsky, Jennifer E Below, Adolfo Correa, Marguerite R Irvin, Laura J F Rasmussen-Torvik, Christopher S Carlson, Lawrence S Phillips, Simin Liu, James S Pankow, Stephen S Rich, Jerome I Rotter, Steven Buyske, Tara C Matise, Kari E North, Christy L Avery, Christopher A Haiman, Ruth J F Loos, Charles Kooperberg, Mariaelisa Graff, Heather M Highland

Affiliations

  1. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. [email protected].
  2. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  3. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  4. Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, USA.
  5. Ambry Genetics, Aliso Viejo, CA, USA.
  6. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  7. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
  8. Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
  9. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  10. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.
  11. Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  12. Department of Medicine, Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS, USA.
  13. Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  14. Atlanta VA Medical Center, Decatur, GA, USA.
  15. Department of Medicine, Division of Endocrinology, Emory University School of Medicine, Atlanta, GA, USA.
  16. Department of Medicine, Division of Endocrinology, Warren Alpert School of Medicine, Brown University, Providence, RI, USA.
  17. Department of Epidemiology, Brown School of Public Health, Providence, RI, USA.
  18. Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA.
  19. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
  20. Department of Pediatrics, Genome Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
  21. Department of Statistics, Rutgers University, Piscataway, NJ, USA.
  22. Department of Genetics, Rutgers University, Piscataway, NJ, USA.
  23. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

PMID: 34951656 DOI: 10.1007/s00125-021-05635-9

Abstract

AIMS/HYPOTHESIS: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA

METHODS: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA

RESULTS: Four novel associations were identified (p < 5 × 10

CONCLUSIONS/INTERPRETATION: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations.

DATA AVAILABILITY: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).

© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords: Fine-mapping; Genome-wide association study; Glucose; Glycaemic traits; HbA1c; Insulin; Transethnic population

References

  1. Cowie C, Casagrande S, Geiss L (2018) Prevalence and incidence of type 2 diabetes and prediabetes. In: Cowie C, Casagrande S, Menke A et al (eds) Diabetes in America: 3rd edition, vol 17-1468. National Institutes of Health, Bethesda, MD - PubMed
  2. Tancredi M, Rosengren A, Svensson AM et al (2015) Excess mortality among persons with type 2 diabetes. N Engl J Med 373(18):1720–1732. https://doi.org/10.1056/NEJMoa1504347 - PubMed
  3. Rowley WR, Bezold C, Arikan Y, Byrne E, Krohe S (2017) Diabetes 2030: insights from yesterday, today, and future trends. Popul Health Manag 20(1):6–12. https://doi.org/10.1089/pop.2015.0181 - PubMed
  4. Huang ES, Basu A, O'Grady M, Capretta JC (2009) Projecting the future diabetes population size and related costs for the U.S. Diabetes Care 32(12):2225–2229. https://doi.org/10.2337/dc09-0459 - PubMed
  5. Dupuis J, Langenberg C, Prokopenko I et al (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42(2):105–116. https://doi.org/10.1038/ng.520 - PubMed
  6. Manning AK, Hivert MF, Scott RA et al (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 44(6):659–669. https://doi.org/10.1038/ng.2274 - PubMed
  7. Wheeler E, Leong A, Liu CT et al (2017) Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med 14(9):e1002383. https://doi.org/10.1371/journal.pmed.1002383 - PubMed
  8. Prasad RB, Groop L (2015) Genetics of type 2 diabetes-pitfalls and possibilities. Genes (Basel) 6(1):87–123. https://doi.org/10.3390/genes6010087 - PubMed
  9. Scott RA, Scott LJ, Magi R et al (2017) An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66(11):2888–2902. https://doi.org/10.2337/db16-1253 - PubMed
  10. Prokopenko I, Langenberg C, Florez JC et al (2009) Variants in MTNR1B influence fasting glucose levels. Nat Genet 41(1):77–81. https://doi.org/10.1038/ng.290 - PubMed
  11. Scott RA, Lagou V, Welch RP et al (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44(9):991–1005. https://doi.org/10.1038/ng.2385 - PubMed
  12. Haiman CA, Fesinmeyer MD, Spencer KL et al (2012) Consistent directions of effect for established type 2 diabetes risk variants across populations: the Population Architecture Using Genomics and Epidemiology (PAGE) Consortium. Diabetes 61(6):1642–1647. https://doi.org/10.2337/db11-1296 - PubMed
  13. Fesinmeyer MD, Meigs JB, North KE et al (2013) Genetic variants associated with fasting glucose and insulin concentrations in an ethnically diverse population: results from the Population Architecture Using Genomics and Epidemiology (PAGE) study. BMC Med Genet 14:98. https://doi.org/10.1186/1471-2350-14-98 - PubMed
  14. Bien SA, Pankow JS, Haessler J et al (2017) Transethnic insight into the genetics of glycaemic traits: fine-mapping results from the Population Architecture Using Genomics and Epidemiology (PAGE) consortium. Diabetologia 60(12):2384–2398. https://doi.org/10.1007/s00125-017-4405-1 - PubMed
  15. Liu CT, Raghavan S, Maruthur N et al (2016) Trans-ethnic Meta-analysis and functional annotation illuminates the genetic architecture of fasting glucose and insulin. Am J Hum Genet 99(1):56–75. https://doi.org/10.1016/j.ajhg.2016.05.006 - PubMed
  16. Sigma Type 2 Diabetes Consortium, Estrada K, Aukrust I et al (2014) Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. Jama 311(22):2305–2314. https://doi.org/10.1001/jama.2014.6511 - PubMed
  17. Moltke I, Grarup N, Jorgensen ME et al (2014) A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature 512(7513):190–193. https://doi.org/10.1038/nature13425 - PubMed
  18. Manning A, Highland HM, Gasser J et al (2017) A low-frequency inactivating AKT2 variant enriched in the Finnish population is associated with fasting insulin levels and type 2 diabetes risk. Diabetes 66(7):2019–2032. https://doi.org/10.2337/db16-1329 - PubMed
  19. Zaitlen N, Pasaniuc B, Gur T, Ziv E, Halperin E (2010) Leveraging genetic variability across populations for the identification of causal variants. Am J Hum Genet 86(1):23–33. https://doi.org/10.1016/j.ajhg.2009.11.016 - PubMed
  20. Ong RT, Wang X, Liu X, Teo YY (2012) Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping. Eur J Hum Genet 20(12):1300–1307. https://doi.org/10.1038/ejhg.2012.88 - PubMed
  21. Teo YY, Ong RT, Sim X, Tai ES, Chia KS (2010) Identifying candidate causal variants via trans-population fine-mapping. Genet Epidemiol 34(7):653–664. https://doi.org/10.1002/gepi.20522 - PubMed
  22. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium, South Asian Type 2 Diabetes (SAT2D) Consortium et al (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46(3):234–244. https://doi.org/10.1038/ng.2897 - PubMed
  23. Matise TC, Ambite JL, Buyske S et al (2011) The next PAGE in understanding complex traits: design for the analysis of population architecture using genetics and epidemiology (PAGE) study. Am J Epidemiol 174(7):849–859. https://doi.org/10.1093/aje/kwr160 - PubMed
  24. International Expert Committee (2009) International expert committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32(7):1327–1334. https://doi.org/10.2337/dc09-9033 - PubMed
  25. Bien SA, Wojcik GL, Zubair N et al (2016) Strategies for enriching variant coverage in candidate disease loci on a multiethnic genotyping Array. PLoS One 11(12):e0167758. https://doi.org/10.1371/journal.pone.0167758 - PubMed
  26. Wojcik GL, Graff M, Nishimura KK et al (2019) Genetic analyses of diverse populations improves discovery for complex traits. Nature 570(7762):514–518. https://doi.org/10.1038/s41586-019-1310-4 - PubMed
  27. Lin DY, Tao R, Kalsbeek WD et al (2014) Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet 95(6):675–688. https://doi.org/10.1016/j.ajhg.2014.11.005 - PubMed
  28. Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26(17):2190–2191. https://doi.org/10.1093/bioinformatics/btq340 - PubMed
  29. Benner C, Spencer CC, Havulinna AS, Salomaa V, Ripatti S, Pirinen M (2016) FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32(10):1493–1501. https://doi.org/10.1093/bioinformatics/btw018 - PubMed
  30. Pruim RJ, Welch RP, Sanna S et al (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26(18):2336–2337. https://doi.org/10.1093/bioinformatics/btq419 - PubMed
  31. Spracklen CN, Shi J, Vadlamudi S et al (2018) Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. PLoS Genet 14(4):e1007275. https://doi.org/10.1371/journal.pgen.1007275 - PubMed
  32. Lagou V, Magi R, Hottenga JJ et al (2021) Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun 12(1):24. https://doi.org/10.1038/s41467-020-19366-9 - PubMed
  33. Nolte IM (2020) Metasubtract: an R-package to analytically produce leave-one-out meta-analysis GWAS summary statistics. Bioinformatics 36(16):4521–4522. https://doi.org/10.1093/bioinformatics/btaa570 - PubMed
  34. Miguel-Escalada I, Bonas-Guarch S, Cebola I et al (2019) Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes. Nat Genet 51(7):1137–1148. https://doi.org/10.1038/s41588-019-0457-0 - PubMed
  35. Pasquali L, Gaulton KJ, Rodriguez-Segui SA et al (2014) Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants. Nat Genet 46(2):136–143. https://doi.org/10.1038/ng.2870 - PubMed
  36. Ramos-Rodriguez M, Raurell-Vila H, Colli ML et al (2019) The impact of proinflammatory cytokines on the beta-cell regulatory landscape provides insights into the genetics of type 1 diabetes. Nat Genet 51(11):1588–1595. https://doi.org/10.1038/s41588-019-0524-6 - PubMed
  37. Carithers LJ, Moore HM (2015) The Genotype-Tissue Expression (GTEx) Project. Biopreserv Biobank 13(5):307–308. https://doi.org/10.1089/bio.2015.29031.hmm - PubMed
  38. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W et al (2015) Integrative analysis of 111 reference human epigenomes. Nature 518(7539):317–330. https://doi.org/10.1038/nature14248 - PubMed
  39. Chen J, Spracklen CN, Marenne G et al (2021) The trans-ancestral genomic architecture of glycemic traits. Nat Genet 53(6):840–860. https://doi.org/10.1038/s41588-021-00852-9 - PubMed
  40. GTEx Consortium (2015) Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348(6235):648–660. https://doi.org/10.1126/science.1262110 - PubMed
  41. Vujkovic M, Keaton JM, Lynch JA et al (2020) Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y - PubMed
  42. Heid IM, Jackson AU, Randall JC et al (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 42(11):949–960. https://doi.org/10.1038/ng.685 - PubMed
  43. Zhu Z, Guo Y, Shi H et al (2020) Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol 145(2):537–549. https://doi.org/10.1016/j.jaci.2019.09.035 - PubMed
  44. Vuckovic D, Bao EL, Akbari P et al (2020) The polygenic and monogenic basis of blood traits and diseases. Cell 182(5):1214–1231 e1211. https://doi.org/10.1016/j.cell.2020.08.008 - PubMed
  45. Astle WJ, Elding H, Jiang T et al (2016) The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167(5):1415–1429 e1419. https://doi.org/10.1016/j.cell.2016.10.042 - PubMed
  46. Staels W, Heremans Y, Heimberg H, De Leu N (2019) VEGF-A and blood vessels: a beta cell perspective. Diabetologia 62(11):1961–1968. https://doi.org/10.1007/s00125-019-4969-z - PubMed
  47. Ng MCY, Graff M, Lu Y et al (2017) Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium. PLoS Genet 13(4):e1006719. https://doi.org/10.1371/journal.pgen.1006719 - PubMed
  48. Li YZ, Di Cristofano A, Woo M (2020) Metabolic role of PTEN in insulin signaling and resistance. Cold Spring Harb Perspect Med 10(8):a036137. https://doi.org/10.1101/cshperspect.a036137 - PubMed
  49. Spracklen CN, Horikoshi M, Kim YJ et al (2020) Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582(7811):240–245. https://doi.org/10.1038/s41586-020-2263-3 - PubMed
  50. Willems EL, Wan JY, Norden-Krichmar TM, Edwards KL, Santorico SA (2020) Transethnic meta-analysis of metabolic syndrome in a multiethnic study. Genet Epidemiol 44(1):16–25. https://doi.org/10.1002/gepi.22267 - PubMed
  51. Ying W, Wollam J, Ofrecio JM et al (2017) Adipose tissue B2 cells promote insulin resistance through leukotriene LTB4/LTB4R1 signaling. J Clin Invest 127(3):1019–1030. https://doi.org/10.1172/JCI90350 - PubMed
  52. Esmaili S, George J (2015) Ltb4r1 inhibitor: a pivotal insulin sensitizer? Trends Endocrinol Metab 26(5):221–222. https://doi.org/10.1016/j.tem.2015.03.007 - PubMed
  53. Li Q, Zhao Q, Zhang J et al (2019) The protein phosphatase 1 complex is a direct target of AKT that links insulin signaling to hepatic glycogen deposition. Cell Rep 28(13):3406–3422 e3407. https://doi.org/10.1016/j.celrep.2019.08.066 - PubMed
  54. Niazi RK, Sun J, Have CT et al (2019) Increased frequency of rare missense PPP1R3B variants among Danish patients with type 2 diabetes. PLoS One 14(1):e0210114. https://doi.org/10.1371/journal.pone.0210114 - PubMed
  55. Rose CS, Ek J, Urhammer SA et al (2005) A -30G>A polymorphism of the beta-cell-specific glucokinase promoter associates with hyperglycemia in the general population of whites. Diabetes 54(10):3026–3031. https://doi.org/10.2337/diabetes.54.10.3026 - PubMed
  56. Hwang JY, Sim X, Wu Y et al (2015) Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians. Diabetes 64(1):291–298. https://doi.org/10.2337/db14-0563 - PubMed
  57. Horikoshi M, Mgi R, van de Bunt M et al (2015) Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet 11(7):e1005230. https://doi.org/10.1371/journal.pgen.1005230 - PubMed
  58. Suzuki K, Akiyama M, Ishigaki K et al (2019) Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population. Nat Genet 51(3):379–386. https://doi.org/10.1038/s41588-018-0332-4 - PubMed
  59. Osbak KK, Colclough K, Saint-Martin C et al (2009) Update on mutations in glucokinase (GCK), which cause maturity-onset diabetes of the young, permanent neonatal diabetes, and hyperinsulinemic hypoglycemia. Hum Mutat 30(11):1512–1526. https://doi.org/10.1002/humu.21110 - PubMed
  60. Bell GI, Polonsky KS (2001) Diabetes mellitus and genetically programmed defects in beta-cell function. Nature 414(6865):788–791. https://doi.org/10.1038/414788a - PubMed
  61. Kim SH (2015) Maturity-onset diabetes of the young: what do clinicians need to know? Diabetes Metab J 39(6):468–477. https://doi.org/10.4093/dmj.2015.39.6.468 - PubMed
  62. Huffman JE (2018) Examining the current standards for genetic discovery and replication in the era of mega-biobanks. Nat Commun 9(1):5054. https://doi.org/10.1038/s41467-018-07348-x - PubMed
  63. Bodmer W, Bonilla C (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40(6):695–701. https://doi.org/10.1038/ng.f.136 - PubMed

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