#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders


Autoři: Mette K. Andersen aff001;  Emil Jørsboe aff002;  Line Skotte aff003;  Kristian Hanghøj aff002;  Camilla H. Sandholt aff001;  Ida Moltke aff002;  Niels Grarup aff001;  Timo Kern aff001;  Yuvaraj Mahendran aff001;  Bolette Søborg aff003;  Peter Bjerregaard aff005;  Christina V. L. Larsen aff005;  Inger K. Dahl-Petersen aff005;  Hemant K. Tiwari aff007;  Bjarke Feenstra aff003;  Anders Koch aff003;  Howard W. Wiener aff009;  Scarlett E. Hopkins aff010;  Oluf Pedersen aff001;  Mads Melbye aff003;  Bert B. Boyer aff010;  Marit E. Jørgensen aff005;  Anders Albrechtsen aff002;  Torben Hansen aff001
Působiště autorů: Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark aff001;  The Bioinformatics Centre, Department of Biology, University of Copenhagen, Copenhagen, Denmark aff002;  Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark aff003;  PEPperPRINT GmbH, Heidelberg, Germany aff004;  National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark aff005;  Greenland Centre for Health Research, University of Greenland, Nuuk, Greenland aff006;  Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America aff007;  Department of Infectious Diseases, Rigshospitalet University Hospital, Copenhagen, Denmark aff008;  Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America aff009;  Department of Obstetrics and Gynecology, Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health & Science University, Portland, Oregon, United States of America aff010;  Center for Alaska Native Health Research, University of Alaska Fairbanks, Fairbanks, Alaska, United States of America aff011;  Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark aff012;  Department of Medicine, Stanford University School of Medicine, Stanford, California, United States of America aff013;  Steno Diabetes Center Copenhagen, Gentofte, Denmark aff014;  Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark aff015
Vyšlo v časopise: The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders. PLoS Genet 16(1): e32767. doi:10.1371/journal.pgen.1008544
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008544

Souhrn

The genetic architecture of the small and isolated Greenlandic population is advantageous for identification of novel genetic variants associated with cardio-metabolic traits. We aimed to identify genetic loci associated with body mass index (BMI), to expand the knowledge of the genetic and biological mechanisms underlying obesity. Stage 1 BMI-association analyses were performed in 4,626 Greenlanders. Stage 2 replication and meta-analysis were performed in additional cohorts comprising 1,058 Yup’ik Alaska Native people, and 1,529 Greenlanders. Obesity-related traits were assessed in the stage 1 study population. We identified a common variant on chromosome 11, rs4936356, where the derived G-allele had a frequency of 24% in the stage 1 study population. The derived allele was genome-wide significantly associated with lower BMI (beta (SE), -0.14 SD (0.03), p = 3.2x10-8), corresponding to 0.64 kg/m2 lower BMI per G allele in the stage 1 study population. We observed a similar effect in the Yup’ik cohort (-0.09 SD, p = 0.038), and a non-significant effect in the same direction in the independent Greenlandic stage 2 cohort (-0.03 SD, p = 0.514). The association remained genome-wide significant in meta-analysis of the Arctic cohorts (-0.10 SD (0.02), p = 4.7x10-8). Moreover, the variant was associated with a leaner body type (weight, -1.68 (0.37) kg; waist circumference, -1.52 (0.33) cm; hip circumference, -0.85 (0.24) cm; lean mass, -0.84 (0.19) kg; fat mass and percent, -1.66 (0.33) kg and -1.39 (0.27) %; visceral adipose tissue, -0.30 (0.07) cm; subcutaneous adipose tissue, -0.16 (0.05) cm, all p<0.0002), lower insulin resistance (HOMA-IR, -0.12 (0.04), p = 0.00021), and favorable lipid levels (triglyceride, -0.05 (0.02) mmol/l, p = 0.025; HDL-cholesterol, 0.04 (0.01) mmol/l, p = 0.0015). In conclusion, we identified a novel variant, where the derived G-allele possibly associated with lower BMI in Arctic populations, and as a consequence also leaner body type, lower insulin resistance, and a favorable lipid profile.

Klíčová slova:

Alaska – Body Mass Index – Europe – Fats – Genetic loci – Insulin resistance – Metaanalysis – Obesity


Zdroje

1. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518: 197–206. doi: 10.1038/nature14177 25673413

2. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27: 3641–3649. doi: 10.1093/hmg/ddy271 30124842

3. Andersen MK, Pedersen C-ET, Moltke I, Hansen T, Albrechtsen A, Grarup N. Genetics of Type 2 Diabetes: the Power of Isolated Populations. Curr Diab Rep. 2016;16: 65. doi: 10.1007/s11892-016-0757-z 27189761

4. Xue Y, Mezzavilla M, Haber M, McCarthy S, Chen Y, Narasimhan V, et al. Enrichment of low-frequency functional variants revealed by whole-genome sequencing of multiple isolated European populations. Nat Commun. 2017;8: 15927. doi: 10.1038/ncomms15927 28643794

5. Moltke I, Grarup N, Jørgensen ME, Bjerregaard P, Treebak JT, Fumagalli M, et al. A common Greenlandic TBC1D4 variant confers muscle insulin resistance and type 2 diabetes. Nature. 2014;512: 190–193. doi: 10.1038/nature13425 25043022

6. Andersen MK, Jørsboe E, Sandholt CH, Grarup N, Jørgensen ME, Færgeman NJ, et al. Identification of Novel Genetic Determinants of Erythrocyte Membrane Fatty Acid Composition among Greenlanders. Zeggirni E, editor. PLOS Genet. 2016;12: e1006119. doi: 10.1371/journal.pgen.1006119 27341449

7. Southam L, Gilly A, Süveges D, Farmaki A-E, Schwartzentruber J, Tachmazidou I, et al. Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits. Nat Commun. 2017;8: 15606. doi: 10.1038/ncomms15606 28548082

8. Huang K, Nair AK, Muller YL, Piaggi P, Bian L, Del Rosario M, et al. Whole exome sequencing identifies variation in CYB5A and RNF10 associated with adiposity and type 2 diabetes. Obesity (Silver Spring). 2014;22: 984–8. doi: 10.1002/oby.20647 24151200

9. Traurig MT, Orczewska JI, Ortiz DJ, Bian L, Marinelarena AM, Kobes S, et al. Evidence for a Role of LPGAT1 in Influencing BMI and Percent Body Fat in Native Americans. Obesity. 2012;21: 193–202. doi: 10.1038/oby.2012.161

10. Mercader JM, Liao RG, Bell AD, Dymek Z, Estrada K, Tukiainen T, et al. A Loss-of-Function Splice Acceptor Variant in IGF2 Is Protective for Type 2 Diabetes. Diabetes. 2017;66: 2903–2914. doi: 10.2337/db17-0187 28838971

11. Estrada K, Aukrust I, Bjørkhaug L, Burtt NP, Mercader JM, García-Ortiz H, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014;311: 2305–14. doi: 10.1001/jama.2014.6511 24915262

12. Williams AL, Jacobs SBR, Moreno-Macías H, Huerta-Chagoya A, Churchhouse C, et al. Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico. Nature. 2014;506: 97–101. doi: 10.1038/nature12828 24390345

13. Grarup N, Moltke I, Andersen MK, Bjerregaard P, Larsen CVL, Dahl-Petersen IK, et al. Identification of novel high-impact recessively inherited type 2 diabetes risk variants in the Greenlandic population. Diabetologia. 2018;61: 2005–2015. doi: 10.1007/s00125-018-4659-2 29926116

14. Minster RL, Hawley NL, Su C-T, Sun G, Kershaw EE, Cheng H, et al. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Nat Genet. 2016;48: 1049–1054. doi: 10.1038/ng.3620 27455349

15. Grarup N, Moltke I, Andersen MK, Dalby M, Vitting-Seerup K, Kern T, et al. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes. Nat Genet. 2018;50: 172–174. doi: 10.1038/s41588-017-0022-7 29311636

16. Jørgensen ME, Glümer C, Bjerregaard P, Gyntelberg F, Jørgensen T, Borch-Johnsen K, et al. Obesity and central fat pattern among Greenland Inuit and a general population of Denmark (Inter99): Relationship to metabolic risk factors. Int J Obes. 2003;27: 1507–1515. doi: 10.1038/sj.ijo.0802434 14634682

17. Larsen CVL, Koch A, Koch A. Befolkningsundersøgelsen i Grønland 2018 –Levevilkår, livsstil og helbred Oversigt over indikatorer for folkesundheden. 2018. Available: https://www.sdu.dk/da/sif/rapporter/2019/befolkningsundersoegelsen_i_groenland

18. WHO. Global Health Observatory (GHO) data—Overweight and obesity. 2016. Available: http://www.who.int/gho/ncd/risk_factors/overweight/en/

19. Skotte L, Jørsboe E, Korneliussen TS, Moltke I, Albrechtsen A. Ancestry‐specific association mapping in admixed populations. Genet Epidemiol. 2019;43: 506–521. doi: 10.1002/gepi.22200 30883944

20. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22: 1790–7. doi: 10.1101/gr.137323.112 22955989

21. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40: D930–4. doi: 10.1093/nar/gkr917 22064851

22. Andersen MK, Grarup N, Moltke I, Albrechtsen A, Hansen T. Genetic architecture of obesity and related metabolic traits-recent insights from isolated populations. Curr Opin Genet Dev. 2018;50: 74–78. doi: 10.1016/j.gde.2018.02.010 29510341

23. Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, et al. A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project. Biopreserv Biobank. 2015;13: 311–9. doi: 10.1089/bio.2015.0032 26484571

24. GTEx project maps wide range of normal human genetic variation: A unique catalog and follow-up effort associate variation with gene expression across dozens of body tissues. Am J Med Genet A. 2018;176: 263–264. doi: 10.1002/ajmg.a.38426 29334591

25. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536: 41–47. doi: 10.1038/nature18642 27398621

26. Klarin D, Damrauer SM, Cho K, Sun YV., Teslovich TM, Honerlaw J, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018;50: 1514–1523. doi: 10.1038/s41588-018-0222-9 30275531

27. Kim H-K, Anwar MA, Choi S. Association of BUD13-ZNF259-APOA5-APOA1-SIK3 cluster polymorphism in 11q23.3 and structure of APOA5 with increased plasma triglyceride levels in a Korean population. Sci Rep. 2019;9: 8296. doi: 10.1038/s41598-019-44699-x 31165758

28. Wang Z, Takemori H, Halder SK, Nonaka Y, Okamoto M. Cloning of a novel kinase (SIK) of the SNF1/AMPK family from high salt diet-treated rat adrenal. FEBS Lett. 1999;453: 135–9. Available: http://www.ncbi.nlm.nih.gov/pubmed/10403390 doi: 10.1016/s0014-5793(99)00708-5 10403390

29. Hardie DG, Sakamoto K. AMPK: A Key Sensor of Fuel and Energy Status in Skeletal Muscle. Physiology. 2006;21: 48–60. doi: 10.1152/physiol.00044.2005 16443822

30. Lanjuin A, Sengupta P. Regulation of chemosensory receptor expression and sensory signaling by the KIN-29 Ser/Thr kinase. Neuron. 2002;33: 369–81. Available: http://www.ncbi.nlm.nih.gov/pubmed/11832225 doi: 10.1016/s0896-6273(02)00572-x 11832225

31. Choi S, Lim D-S, Chung J. Feeding and Fasting Signals Converge on the LKB1-SIK3 Pathway to Regulate Lipid Metabolism in Drosophila. Taghert PH, editor. PLOS Genet. 2015;11: e1005263. doi: 10.1371/journal.pgen.1005263 25996931

32. Wang B, Moya N, Niessen S, Hoover H, Mihaylova MM, Shaw RJ, et al. A Hormone-Dependent Module Regulating Energy Balance. Cell. 2011;145: 596–606. doi: 10.1016/j.cell.2011.04.013 21565616

33. Teesalu M, Rovenko BM, Hietakangas V. Salt-Inducible Kinase 3 Provides Sugar Tolerance by Regulating NADPH/NADP+ Redox Balance. Curr Biol. 2017;27: 458–464. doi: 10.1016/j.cub.2016.12.032 28132818

34. Uebi T, Itoh Y, Hatano O, Kumagai A, Sanosaka M, Sasaki T, et al. Involvement of SIK3 in Glucose and Lipid Homeostasis in Mice. Lobaccaro J-MA, editor. PLoS One. 2012;7: e37803. doi: 10.1371/journal.pone.0037803 22662228

35. Itoh Y, Sanosaka M, Fuchino H, Yahara Y, Kumagai A, Takemoto D, et al. Salt-inducible Kinase 3 Signaling Is Important for the Gluconeogenic Programs in Mouse Hepatocytes. J Biol Chem. 2015;290: 17879–17893. doi: 10.1074/jbc.M115.640821 26048985

36. Kruglyak L. The road to genome-wide association studies. Nat Rev Genet. 2008;9: 314–8. doi: 10.1038/nrg2316 18283274

37. Moltke I, Fumagalli M, Korneliussen TS, Crawford JE, Bjerregaard P, Jørgensen ME, et al. Uncovering the Genetic History of the Present-Day Greenlandic Population. Am J Hum Genet. 2015;96: 54–69. doi: 10.1016/j.ajhg.2014.11.012 25557782

38. Service S, DeYoung J, Karayiorgou M, Roos JL, Pretorious H, Bedoya G, et al. Magnitude and distribution of linkage disequilibrium in population isolates and implications for genome-wide association studies. Nat Genet. 2006;38: 556–60. doi: 10.1038/ng1770 16582909

39. Jeppesen C, Jørgensen ME, Bjerregaard P. Assessment of consumption of marine food in Greenland by a food frequency questionnaire and biomarkers. Int J Circumpolar Health. 2012;71: 18361. Available: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3417470&tool=pmcentrez&rendertype=abstract doi: 10.3402/ijch.v71i0.18361 22663940

40. Fumagalli M, Moltke I, Grarup N, Racimo F, Bjerregaard P, Jorgensen ME, et al. Greenlandic Inuit show genetic signatures of diet and climate adaptation. Science. 2015;349: 1343–1347. doi: 10.1126/science.aab2319 26383953

41. Pedersen C-ET, Lohmueller KE, Grarup N, Bjerregaard P, Hansen T, Siegismund HR, et al. The Effect of an Extreme and Prolonged Population Bottleneck on Patterns of Deleterious Variation: Insights from the Greenlandic Inuit. Genetics. 2017;205: 787–801. doi: 10.1534/genetics.116.193821 27903613

42. Mohatt GV, Plaetke R, Klejka J, Luick B, Lardon C, Bersamin A, et al. The Center for Alaska Native Health Research Study: a community-based participatory research study of obesity and chronic disease-related protective and risk factors. Int J Circumpolar Health. 2007;66: 8–18. Available: http://www.ncbi.nlm.nih.gov/pubmed/17451130 doi: 10.3402/ijch.v66i1.18219 17451130

43. Bjerregaard P, Curtis T, Borch-Johnsen K, Mulvad G, Becker U, Andersen S, et al. Inuit health in Greenland: a population survey of life style and disease in Greenland and among Inuit living in Denmark. Int J Circumpolar Health. 2003;62 Suppl 1: 3–79. Available: http://www.ncbi.nlm.nih.gov/pubmed/14527126

44. Bjerregaard P. Inuit Health in Transition Greenland survey 2005–2010 Population sample and survey methods. 2011. Available: http://www.si-folkesundhed.dk/upload/inuit_health_in_transition_greenland_methods_5_2nd_revision.pdf

45. Skotte L, Koch A, Yakimov V, Zhou S, Søborg B, Andersson M, et al. CPT1AMissense Mutation Associated With Fatty Acid Metabolism and Reduced Height in Greenlanders. Circ Cardiovasc Genet. 2017;10: e001618. doi: 10.1161/CIRCGENETICS.116.001618 28611031

46. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28: 412–419. doi: 10.1007/bf00280883 3899825

47. Jørgensen ME, Borch-Johnsen K, Stolk R, Bjerregaard P. Fat distribution and glucose intolerance among Greenland Inuit. Diabetes Care. 2013;36: 2988–94. doi: 10.2337/dc12-2703 23656981

48. Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, et al. The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 2012;8: e1002793. doi: 10.1371/journal.pgen.1002793 22876189

49. Aslibekyan S, Vaughan LK, Wiener HW, Lemas DJ, Klimentidis YC, Havel PJ, et al. Evidence for novel genetic loci associated with metabolic traits in Yup’ik people. Am J Hum Biol. 2013;25: 673–80. doi: 10.1002/ajhb.22429 23907821

50. Delaneau O, Zagury J-F. Data Production and Analysis in Population Genomics. Pompanon F, Bonin A, editors. Methods in molecular biology (Clifton, N.J.). Totowa, NJ: Humana Press; 2012. doi: 10.1007/978-1-61779-870-2

51. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5: e1000529. doi: 10.1371/journal.pgen.1000529 19543373

52. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44: 821–4. doi: 10.1038/ng.2310 22706312

53. Petersen GM, Ward JI, Terasaki PI, Schanfield MS, Ferrell RE, Scott EM, et al. Genetic polymorphisms in southwest Alaskan Eskimos. Hum Hered. 1991;41: 236–47. doi: 10.1159/000154008 1783412

54. Lange K, Papp JC, Sinsheimer JS, Sripracha R, Zhou H, Sobel EM. Mendel: the Swiss army knife of genetic analysis programs. Bioinformatics. 2013;29: 1568–70. doi: 10.1093/bioinformatics/btt187 23610370

55. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26: 2190–1. doi: 10.1093/bioinformatics/btq340 20616382

56. Cochran WG. The Combination of Estimates from Different Experiments. 1954. Available: https://about.jstor.org/terms

57. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19: 1655–1664. doi: 10.1101/gr.094052.109 19648217

58. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. Available: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

59. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32: 3047–3048. doi: 10.1093/bioinformatics/btw354 27312411

60. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

61. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34: 525–527. doi: 10.1038/nbt.3519 27043002

62. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research. 2016;4: 1521. doi: 10.12688/f1000research.7563.2 26925227


Článek vyšel v časopise

PLOS Genetics


2020 Číslo 1
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

plice
INSIGHTS from European Respiratory Congress
nový kurz

Současné pohledy na riziko v parodontologii
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií
Autoři: prof. MUDr. Petr Němec, Ph.D.

Střevní příprava před kolonoskopií
Autoři: MUDr. Klára Kmochová, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#