Epidemiology and Population Health

The combined effects of FADS gene variation and dietary fats in obesity-related traits in a population from the far north of Sweden: the GLACIER Study

Abstract

Background

Recent analyses in Greenlandic Inuit identified six genetic polymorphisms (rs74771917, rs3168072, rs12577276, rs7115739, rs174602 and rs174570) in the fatty acid desaturase gene cluster (FADS1-FADS2-FADS3) that are associated with multiple metabolic and anthropometric traits. Our objectives were to systematically assess whether dietary polyunsaturated fatty acid (PUFA) intake modifies the associations between genetic variants in the FADS gene cluster and cardiometabolic traits, and to functionally annotate top-ranking candidates to estimate their regulatory potential.

Methods

Data analyses consisted of the following: interaction analyses between the 6 candidate genetic variants and dietary PUFA intake; gene-centric joint analyses to detect interaction signals in the FADS region; haplotype-centric joint tests across 30 haplotype blocks in the FADS region to refine interaction signals; and functional annotation of top-ranking loci from the previous steps. These analyses were undertaken in Swedish adults from the GLACIER Study (N = 5,160); data on genetic variation and eight cardiometabolic traits were used.

Results

Interactions were observed between rs174570 and n-6 PUFA intake on fasting glucose (Pint = 0.005) and between rs174602 and n-3 PUFA intake on total cholesterol (Pint = 0.001). Gene-centric analyses demonstrated a statistically significant interaction effect for FADS and n-3 PUFA on triglycerides (Pint = 0.005) considering genetic main effects as random. Haplotype analyses revealed three blocks (Pint < 0.011) that could drive the interaction between FADS and n-3 PUFA on triglycerides; functional annotation of these regions showed that each block harbours a number of highly functional regulatory variants; FADS2 rs5792235 demonstrated the highest functionality score.

Conclusions

The association between FADS variants and triglycerides may be modified by PUFA intake. The intronic FADS2 rs5792235 variant is a potential causal variant in the region, having the highest regulatory potential. However, our results suggest that multiple haplotypes may harbour functional variants in a region, rather than a single causal variant.

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References

  1. 1.

    Marquardt A, Stohr H, White K, Weber BHF. CDNA cloning, genomic structure, and chromosomal localization of three members of the human fatty acid desaturase family. Genomics. 2000;66:175–83.

  2. 2.

    Mathias RA, Vergara C, Gao L, Rafaels N, Hand T, Campbell M, et al. FADS genetic variants and omega-6 polyunsaturated fatty acid metabolism in a homogeneous island population. J Lipid Res. 2010;51:2766–74.

  3. 3.

    Sergeant S, Hugenschmidt CE, Rudock ME, Ziegler JT, Ivester P, Ainsworth HC, et al. Differences in arachidonic acid levels and fatty acid desaturase (FADS) gene variants in African Americans and European Americans with diabetes or the metabolic syndrome. Br J Nutr. 2012;107:547–55.

  4. 4.

    Guan WH, Steffen BT, Lemaitre RN, Wu JHY, Tanaka T, Manichaikul A, et al. Genome-wide association study of plasma N6 polyunsaturated fatty acids within the cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. Circ Cardiovasc Genet. 2014;7:321–31.

  5. 5.

    Bokor S, Dumont J, Spinneker A, Gonzalez-Gross M, Nova E, Widhalm K, et al. Single nucleotide polymorphisms in the FADS gene cluster are associated with delta-5 and delta-6 desaturase activities estimated by serum fatty acid ratios. J Lipid Res. 2010;51:2325–33.

  6. 6.

    Malerba G, Schaeffer L, Xumerle L, Klopp N, Trabetti E, Biscuola M, et al. SNPs of the FADS gene cluster are associated with polyunsaturated fatty acids in a cohort of patients with cardiovascular disease. Lipids. 2008;43:289–99.

  7. 7.

    Andersen MK, Jorsboe E, Sandholt CH, Grarup N, Jorgensen ME, Faergeman NJ, et al. Identification of novel genetic determinants of erythrocyte membrane fatty acid composition among Greenlanders. PLoS Genet. 2016;12:e1006119.

  8. 8.

    Ralston JC, Matravadia S, Gaudio N, Holloway GP, Mutch DM. Polyunsaturated fatty acid regulation of adipocyte FADS1 and FADS2 expression and function. Obesity. 2015;23:725–8.

  9. 9.

    Martinelli N, Girelli D, Malerba G, Guarini P, Illig T, Trabetti E, et al. FADS genotypes and desaturase activity estimated by the ratio of arachidonic acid to linoleic acid are associated with inflammation and coronary artery disease. Am J Clin Nutr. 2008;88:941–9.

  10. 10.

    Baylin A, Ruiz-Narvaez E, Kraft P, Campos H. alpha-Linolenic acid, Delta6-desaturase gene polymorphism, and the risk of nonfatal myocardial infarction. Am J Clin Nutr. 2007;85:554–60.

  11. 11.

    Martinelli N, Girelli D, Malerba G, Guarini P, Illig T, Trabetti E, et al. FADS genotypes and desaturase activity estimated by the ratio of arachidonic acid to linoleic acid are associated with inflammation and coronary artery disease. Am J Clin Nutr. 2008;88:941–9.

  12. 12.

    Goodarzi MO, Guo X, Cui J, Jones MR, Haritunians T, Xiang AH, et al. Systematic evaluation of validated type 2 diabetes and glycaemic trait loci for association with insulin clearance. Diabetologia. 2013;56:1282–90.

  13. 13.

    Takkunen MJ, Schwab US, de Mello VD, Eriksson JG, Lindstrom J, Tuomilehto J, et al. Longitudinal associations of serum fatty acid composition with type 2 diabetes risk and markers of insulin secretion and sensitivity in the Finnish Diabetes Prevention Study. Eur J Nutr. 2016;55:967–79.

  14. 14.

    Global Lipids Genetics Consortium. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274–83.

  15. 15.

    Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk (vol 42, pg 105, 2010). Nat Genet. 2010;42:464.

  16. 16.

    Lu YC, Feskens EJM, Dolle MET, Imholz S, Verschuren WMM, Muller M, et al. Dietary n-3 and n-6 polyunsaturated fatty acid intake interacts with FADS1 genetic variation to affect total and HDL-cholesterol concentrations in the Doetinchem Cohort Study. Am J Clin Nutr. 2010;92:258–65.

  17. 17.

    Hellstrand S, Ericson U, Gullberg B, Hedblad B, Orho-Melander M, Sonestedt E. Genetic variation in FADS1 has little effect on the association between dietary PUFA intake and cardiovascular disease. J Nutr. 2014;144:1356–63.

  18. 18.

    Hellstrand S, Sonestedt E, Ericson U, Gullberg B, Wirfalt E, Hedblad B, et al. Intake levels of dietary long-chain PUFAs modify the association between genetic variation in FADS and LDL-C. J Lipid Res. 2012;53:1183–9.

  19. 19.

    Smith CE, Follis JL, Nettleton JA, Foy M, Wu JHY, Ma YY, et al. Dietary fatty acids modulate associations between genetic variants and circulating fatty acids in plasma and erythrocyte membranes: meta-analysis of nine studies in the CHARGE consortium. Mol Nutr Food Res. 2015;59:1373–83.

  20. 20.

    Zhu JW, Sun Q, Zong G, Si Y, Liu C, Qi QB, et al. Interaction between a common variant in FADS1 and erythrocyte polyunsaturated fatty acids on lipid profile in Chinese Hans. J Lipid Res. 2013;54:1477–83.

  21. 21.

    Cormier H, Rudkowska I, Thifault E, Lemieux S, Couture P, Vohl MC. Polymorphisms in fatty acid desaturase (FADS) gene cluster: effects on glycemic controls following an omega-3 polyunsaturated fatty acids (PUFA) supplementation. Genes. 2013;4:485–98.

  22. 22.

    Florez JC, Jablonski KA, McAteer JB, Franks PW, Mason CC, Mather K, et al. Effects of genetic variants previously associated with fasting glucose and insulin in the Diabetes Prevention Program. PLoS ONE. 2012;7:e44424.

  23. 23.

    Norris JM, Kroehl M, Fingerlin TE, Frederiksen BN, Seifert J, Wong R, et al. Erythrocyte membrane docosapentaenoic acid levels are associated with islet autoimmunity: the Diabetes Autoimmunity Study in the Young. Diabetologia. 2014;57:295–304.

  24. 24.

    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;349:1343–7.

  25. 25.

    Kurbasic A, Poveda A, Chen Y, Agren A, Engberg E, Hu FB, et al. Gene-lifestyle interactions in complex diseases: design and description of the GLACIER and VIKING studies. Curr Nutr Rep. 2014;3:400–11.

  26. 26.

    Hallmans G, Agren A, Johansson G, Johansson A, Stegmayr B, Jansson JH, et al. Cardiovascular disease and diabetes in the Northern Sweden Health and Disease Study Cohort - evaluation of risk factors and their interactions. Scand J Public Health Suppl. 2003;61:18–24.

  27. 27.

    Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499–502.

  28. 28.

    Wu J, Province MA, Coon H, Hunt SC, Eckfeldt JH, Arnett DK, et al. An investigation of the effects of lipid-lowering medications: genome-wide linkage analysis of lipids in the HyperGEN study. BMC Genet. 2007;8:60.

  29. 29.

    Varga TV, Sonestedt E, Shungin D, Koivula RW, Hallmans G, Escher SA, et al. Genetic determinants of long-term changes in blood lipid concentrations: 10-year follow-up of the GLACIER study. PLoS Genet. 2014;10:e1004388.

  30. 30.

    Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr. 1991;45:569–81.

  31. 31.

    Johansson I, Hallmans G, Wikman A, Biessy C, Riboli E, Kaaks R. Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr. 2002;5:487–96.

  32. 32.

    Wennberg M, Vessby B, Johansson I. Evaluation of relative intake of fatty acids according to the Northern Sweden FFQ with fatty acid levels in erythrocyte membranes as biomarkers. Public Health Nutr. 2009;12:1477–84.

  33. 33.

    Livsmedelsverket. The food database [Available from: http://www.livsmedelsverket.se/en/food-and-content/naringsamnen/livsmedelsdatabasen/.

  34. 34.

    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.

  35. 35.

    Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, et al. The structure of haplotype blocks in the human genome. Science. 2002;296:2225–9.

  36. 36.

    Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.

  37. 37.

    R Development Core Team. R: A language and environment for statistic computing. Vienna, Austria: R Foundation for Statistic Computing; 2015.

  38. 38.

    Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

  39. 39.

    Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:1220S–8S. 4 Suppldiscussion 9S-31S

  40. 40.

    Chen H, Meigs JB, Dupuis J. Incorporating gene-environment interaction in testing for association with rare genetic variants. Hum Hered. 2014;78:81–90.

  41. 41.

    Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89:82–93.

  42. 42.

    Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74:765–9.

  43. 43.

    Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012;9:215–6.

  44. 44.

    Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

  45. 45.

    Lu Y, Quan C, Chen H, Bo X, Zhang C. 3DSNP: a database for linking human noncoding SNPs to their three-dimensional interacting genes. Nucleic Acids Res. 2017;45:D643–D9. D1

  46. 46.

    Ward LD, Kellis M. HaploRegv4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016;44:D877–81. D1

  47. 47.

    Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 2015;43:D1049–56. Database issue

  48. 48.

    Claussnitzer M, Dankel SN, Kim KH, Quon G, Meuleman W, Haugen C, et al. FTO obesity variant circuitry and adipocyte browning in humans. N Engl J Med. 2015;373:895–907.

  49. 49.

    Murff HJ, Edwards TL Endogenous production of long-chain polyunsaturated fatty acids and metabolic disease risk. Curr Cardiovasc Risk Rep. 2014;8:418.

  50. 50.

    Corpeleijn E, Feskens EJ, Jansen EH, Mensink M, Saris WH, de Bruin TW, et al. Improvements in glucose tolerance and insulin sensitivity after lifestyle intervention are related to changes in serum fatty acid profile and desaturase activities: the SLIM study. Diabetologia. 2006;49:2392–401.

  51. 51.

    Cormier H, Rudkowska I, Thifault E, Lemieux S, Couture P, Vohl MC. Polymorphisms in fatty acid desaturase (FADS) gene cluster: effects on glycemic controls following an omega-3 polyunsaturated fatty acids (PUFA) supplementation. Genes (Basel). 2013;4:485–98.

  52. 52.

    Buckley MT, Racimo F, Allentoft ME, Jensen MK, Jonsson A, Huang H, et al. Selection in Europeans on fatty acid desaturases associated with dietary changes. Mol Biol Evol. 2017;34:1307–18.

  53. 53.

    Neel JV. Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? Am J Hum Genet. 1962;14:353–62.

  54. 54.

    Wu JHY, Marklund M, Imamura F, Tintle N, Ardisson Korat AV, de Goede J, et al. Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies. Lancet Diabetes Endocrinol. 2017;5:965–74.

  55. 55.

    Del Gobbo LC, Imamura F, Aslibekyan S, Marklund M, Virtanen JK, Wennberg M, et al. Omega-3 polyunsaturated fatty acid biomarkers and coronary heart disease: pooling project of 19 cohort studies. JAMA Intern Med. 2016;176:1155–66.

  56. 56.

    Ahmad S, Rukh G, Varga TV, Ali A, Kurbasic A, Shungin D, et al. Gene x physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry. PLoS Genet. 2013;9:e1003607.

  57. 57.

    Lemaitre RN, Tanaka T, Tang W, Manichaikul A, Foy M, Kabagambe EK, et al. Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. PLoS Genet. 2011;7:e1002193.

  58. 58.

    Mozaffarian D, Kabagambe EK, Johnson CO, Lemaitre RN, Manichaikul A, Sun Q, et al. Genetic loci associated with circulating phospholipid trans fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. Am J Clin Nutr. 2015;101:398–406.

  59. 59.

    Hwang JY, Sim X, Wu Y, Liang J, Tabara Y, Hu C, et al. Genome-wide association meta-analysis identifies novel variants associated with fasting plasma glucose in East Asians. Diabetes. 2015;64:291–8.

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Acknowledgements

This research was undertaken as part of a research program supported by the European Commission (CoG-2015_681742_NASCENT), Swedish Research Council (Vetenskapsrådet, Distinguished Young Researchers Award in Medicine, 542-2014-3529), Swedish Heart-Lung Foundation (Hjärt-Lungfonden, 20140776), and the Novo Nordisk Foundation (NNF17OC0026828), all grants to PWF. MK is funded by German Research Foundation (Deutsche Forschungsgemeinschaft) research fellowship (KE 2182/1-1). TVV is supported by the Novo Nordisk Foundation Postdoctoral Fellowship within Endocrinology/Metabolism at International Elite Research Environments via NNF16OC0020698. This study was supported by the Swedish Research Council, Strategic Research Area Exodiab, Dnr 2009-1039, the Swedish Foundation for Strategic Research Dnr IRC15-0067 and the Swedish Research Council, Linnaeus grant, Dnr 349-2006-237. We thank the participants, health professionals and data managers involved in the Västerbotten Intervention Project. We are also grateful to the staff of the Northern Sweden Biobank for preparing materials, and to K. Enqvist and T. Johansson (Västerbottens County Council, Umeå, Sweden) for DNA preparation. We thank Dr. Inês Barroso and colleagues at the Wellcome Trust Sanger Institute (Hinxton) for their work on genotyping the GLACIER cohort.

Author contibutions

YC, AK, PWF and TVV designed the study. YC and TVV performed the statistical analyses. ACE, MK and JDR undertook the functional annotations. AP and TVV conducted the literature review of the 3D interacting genes. YC, PWF and TVV drafted the manuscript. All authors critically revised and approved the manuscript. PWF and TVV have primary responsibility for final content.

Author information

Correspondence to Paul W. Franks or Tibor V. Varga.

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Conflict of interest

PWF has been a paid consultant in the design of a personalized nutrition trial (PREDICT) as part of a private–public partnership at Kings College London, UK, and has received research support from several pharmaceutical companies as part of European Union Innovative Medicines Initiative (IMI) projects. All other authors declare that they have no conflict of interest.

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