Genome-wide association study of fish oil supplementation on lipid traits in 81,246 individuals reveals new gene-diet interaction loci
Autoři:
Michael Francis aff001; Changwei Li aff002; Yitang Sun aff003; Jingqi Zhou aff003; Xiang Li aff002; J. Thomas Brenna aff004; Kaixiong Ye aff001
Působiště autorů:
Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
aff001; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America
aff002; Department of Genetics, University of Georgia, Athens, Georgia, United States of America
aff003; Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America
aff004; Department of Pediatrics, Dell Pediatric Research Institute, University of Texas at Austin, Austin, Texas, United States of America
aff005; Departments of Nutrition and Chemistry, University of Texas at Austin, Austin, Texas, United States of America
aff006
Vyšlo v časopise:
Genome-wide association study of fish oil supplementation on lipid traits in 81,246 individuals reveals new gene-diet interaction loci. PLoS Genet 17(3): e1009431. doi:10.1371/journal.pgen.1009431
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1009431
Souhrn
Fish oil supplementation is widely used for reducing serum triglycerides (TAGs) but has mixed effects on other circulating cardiovascular biomarkers. Many genetic polymorphisms have been associated with blood lipids, including high- and low-density-lipoprotein cholesterol (HDL-C, LDL-C), total cholesterol, and TAGs. Here, the gene-diet interaction effects of fish oil supplementation on these lipids were analyzed in a discovery cohort of up to 73,962 UK Biobank participants, using a 1-degree-of-freedom (1df) test for interaction effects and a 2-degrees-of-freedom (2df) test to jointly analyze interaction and main effects. Associations with P < 1×10−6 in either test (26,157; 18,300 unique variants) were advanced to replication in up to 7,284 participants from the Atherosclerosis Risk in Communities (ARIC) Study. Replicated associations reaching 1df P < 0.05 (2,175; 1,763 unique variants) were used in meta-analyses. We found 13 replicated and 159 non-replicated (UK Biobank only) loci with significant 2df joint tests that were predominantly driven by main effects and have been previously reported. Four novel interaction loci were identified with 1df P < 5×10−8 in meta-analysis. The lead variant in the GJB6-GJB2-GJA3 gene cluster, rs112803755 (A>G; minor allele frequency = 0.041), shows exclusively interaction effects. The minor allele is significantly associated with decreased TAGs in individuals with fish oil supplementation, but with increased TAGs in those without supplementation. This locus is significantly associated with higher GJB2 expression of connexin 26 in adipose tissue; connexin activity is known to change upon exposure to omega-3 fatty acids. Significant interaction effects were also found in three other loci in the genes SLC12A3 (HDL-C), ABCA6 (LDL-C), and MLXIPL (LDL-C), but highly significant main effects are also present. Our study identifies novel gene-diet interaction effects for four genetic loci, whose effects on blood lipids are modified by fish oil supplementation. These findings highlight the need and possibility for personalized nutrition.
Klíčová slova:
Blood – Genetic loci – Genome-wide association studies – Cholesterol – Lipids – Metaanalysis – Oils – Single nucleotide polymorphisms
Zdroje
1. Thaipitakwong T, Aramwit P. A Review of the Efficacy, Safety, and Clinical Implications of Naturally Derived Dietary Supplements for Dyslipidemia. Am J Cardiovasc Drug. 2017;17(1):27–35. doi: 10.1007/s40256-016-0191-2 27637494
2. Scicchitano P, Cameli M, Maiello M, Modesti PA, Muiesan ML, Novo S, et al. Nutraceuticals and dyslipidaemia: Beyond the common therapeutics. J Funct Food. 2014;6:11–32. https://doi.org/10.1016/j.jff.2013.12.006.
3. Eslick GD, Howe PRC, Smith C, Priest R, Bensoussan A. Benefits of fish oil supplementation in hyperlipidemia: a systematic review and meta-analysis. Int J Cardiol. 2009;136(1):4–16. doi: 10.1016/j.ijcard.2008.03.092 18774613
4. Lombardo YB, Chicco AG. Effects of dietary polyunsaturated n-3 fatty acids on dyslipidemia and insulin resistance in rodents and humans. A review. J Nutr Biochem. 2006;17(1):1–13. doi: 10.1016/j.jnutbio.2005.08.002 16214332
5. Goldberg RB, Sabharwal AK. Fish oil in the treatment of dyslipidemia. Curr Opin Endocrinol, Diabetes and Obesity. 2008;15(2):167–74. doi: 10.1097/MED.0b013e3282f76728 18316953
6. Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, et al. A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet. 2018;50(3):401–13. Epub 2018/03/05. doi: 10.1038/s41588-018-0064-5 29507422
7. 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(11):1514–23. Epub 2018/10/03. doi: 10.1038/s41588-018-0222-9 30275531
8. Madden J, Williams CM, Calder PC, Lietz G, Miles EA, Cordell H, et al. The Impact of Common Gene Variants on the Response of Biomarkers of Cardiovascular Disease (CVD) Risk to Increased Fish Oil Fatty Acids Intakes. Annu Rev Nutr. 2011;31(1):203–34. doi: 10.1146/annurev-nutr-010411-095239 21568708
9. 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(11):1514–23. Epub 2018/10/01. doi: 10.1038/s41588-018-0222-9 30275531
10. Aung T, Halsey J, Kromhout D, Gerstein HC, Marchioli R, Tavazzi L, et al. Associations of Omega-3 Fatty Acid Supplement Use With Cardiovascular Disease Risks: Meta-analysis of 10 Trials Involving 77 917 IndividualsMeta-analysis of Associations of Omega-3 Fatty Acids and Cardiovascular RiskMeta-analysis of Associations of Omega-3 Fatty Acids and Cardiovascular Risk. JAMA Cardiol. 2018;3(3):225–33.
11. Zheng J, Huang T, Yu Y, Hu X, Yang B, Li D. Fish consumption and CHD mortality: an updated meta-analysis of seventeen cohort studies. Public Health Nutr. 2012;15(4):725–37. Epub 2011/09/15. doi: 10.1017/S1368980011002254 21914258
12. 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(4):941–9. Epub 2008/10/10. doi: 10.1093/ajcn/88.4.941 18842780
13. 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(8):2325–33. Epub 2010/04/30. doi: 10.1194/jlr.M006205 20427696
14. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–9. doi: 10.1038/s41586-018-0579-z 30305743
15. Bush WS, Moore JH. Chapter 11: Genome-wide association studies. PLOS Comput Biol. 2012;8(12):e1002822. doi: 10.1371/journal.pcbi.1002822 23300413
16. Ye K, Gao F, Wang D, Bar-Yosef O, Keinan A. Dietary adaptation of FADS genes in Europe varied across time and geography. Nat Ecol Evol. 2017;1(7):0167. doi: 10.1038/s41559-017-0167 29094686
17. Kothapalli KSD, Ye K, Gadgil MS, Carlson SE, O’Brien KO, Zhang JY, et al. Positive Selection on a Regulatory Insertion-Deletion Polymorphism in FADS2 Influences Apparent Endogenous Synthesis of Arachidonic Acid. Mol Biol Evol. 2016;33(7):1726–39. Epub 2016/03/29. doi: 10.1093/molbev/msw049 27188529
18. Manning AK, LaValley M, Liu C-T, Rice K, An P, Liu Y, et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol. 2011;35(1):11–8. doi: 10.1002/gepi.20546 21181894
19. Rao DC, Sung YJ, Winkler TW, Schwander K, Borecki I, Cupples LA, et al. Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale. Circ Cardiovasc Genet. 2017;10(3). Epub 2017/06/18. doi: 10.1161/CIRCGENETICS.116.001649 28620071
20. Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2(1):73–80. Epub 2009/12/25. doi: 10.1161/CIRCGENETICS.108.829747 20031568
21. Sung YJ, de las Fuentes L, Winkler TW, Chasman DI, Bentley AR, Kraja AT, et al. A multi-ancestry genome-wide study incorporating gene–smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure. Hum Mol Genet. 2019;28(15):2615–33. doi: 10.1093/hmg/ddz070 31127295
22. Noordam R, Bos MM, Wang H, Winkler TW, Bentley AR, Kilpeläinen TO, et al. Multi-ancestry sleep-by-SNP interaction analysis in 126,929 individuals reveals lipid loci stratified by sleep duration. Nat Comm. 2019;10:5121.
23. Figueroa V, Saez PJ, Salas JD, Salas D, Jara O, Martinez AD, et al. Linoleic acid induces opening of connexin26 hemichannels through a PI3K/Akt/Ca(2+)-dependent pathway. Biochim Biophys Acta. 2013;1828(3):1169–79. Epub 2012/12/25. doi: 10.1016/j.bbamem.2012.12.006 23261389
24. Dlugosova K, Weismann P, Bernatova I, Sotnikova R, Slezak J, Okruhlicova L. Omega-3 fatty acids and atorvastatin affect connexin 43 expression in the aorta of hereditary hypertriglyceridemic rats. Can J Physiol Pharmacol. 2009;87(12):1074–82. Epub 2009/12/24. doi: 10.1139/Y09-104 20029544
25. Brisset AC, Isakson BE, Kwak BR. Connexins in vascular physiology and pathology. Antioxid Redox Signal. 2009;11(2):267–82. doi: 10.1089/ars.2008.2115 18834327
26. Kooner JS, Chambers JC, Aguilar-Salinas CA, Hinds DA, Hyde CL, Warnes GR, et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat Genet. 2008;40(2):149–51. doi: 10.1038/ng.2007.61 18193046
27. Zeng X-N, Yin R-X, Huang P, Huang K-K, Wu J, Guo T, et al. Association of the MLXIPL/TBL2 rs17145738 SNP and serum lipid levels in the Guangxi Mulao and Han populations. Lipids Health Dis. 2013;12(1):156. doi: 10.1186/1476-511X-12-156 24160749
28. Iizuka K. The transcription factor carbohydrate-response element-binding protein (ChREBP): A possible link between metabolic disease and cancer. BBA-Mol Basis Dis. 2017;1863(2):474–85. doi: 10.1016/j.bbadis.2016.11.029 27919710
29. Jump DB, Tripathy S, Depner CM. Fatty acid-regulated transcription factors in the liver. Annu Rev Nutr. 2013;33:249–69. Epub 2013/03/22. doi: 10.1146/annurev-nutr-071812-161139 23528177
30. de Vries PS, Brown MR, Bentley AR, Sung YJ, Winkler TW, Ntalla I, et al. Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions. Am J Epidemiol. 2019;188(6):1033–54. doi: 10.1093/aje/kwz005 30698716
31. Kaminski WE, Wenzel JJ, Piehler A, Langmann T, Schmitz G. ABCA6, a novel a subclass ABC transporter. Biochem Biophys Res Commun. 2001;285(5):1295–301. Epub 2001/08/02. doi: 10.1006/bbrc.2001.5326 11478798
32. Harris WS. Fish oils and plasma lipid and lipoprotein metabolism in humans: a critical review. J Lipid Res. 1989;30(6):785–807. Epub 1989/06/01. 2677200
33. Innes JK, Calder PC. The Differential Effects of Eicosapentaenoic Acid and Docosahexaenoic Acid on Cardiometabolic Risk Factors: A Systematic Review. Int J Mol Sci. 2018;19(2). Epub 2018/02/10. doi: 10.3390/ijms19020532 29425187
34. Greenwood DC, Gilthorpe MS, Cade JE. The impact of imprecisely measured covariates on estimating gene-environment interactions. BMC Med Res Methodol. 2006 4;6:21. doi: 10.1186/1471-2288-6-21 16674808
35. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19(8):491–504. doi: 10.1038/s41576-018-0016-z 29844615
36. Cundiff DK, Lanou AJ, Nigg CR. Relation of omega-3 Fatty Acid intake to other dietary factors known to reduce coronary heart disease risk. Am J Cardiol. 2007 1;99(9):1230–3. doi: 10.1016/j.amjcard.2006.12.032 17478148
37. Rodgers GP, Collins FS. Precision Nutrition—the Answer to “What to Eat to Stay Healthy”. JAMA. 2020;324(8):735–736. doi: 10.1001/jama.2020.13601 32766768
38. The National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health. Manual 8: Lipid and Lipoprotein Determinations. ARIC Protocol. 1987.
39. Cladis DP, Kleiner AC, Freiser HH, Santerre CR. Fatty Acid Profiles of Commercially Available Finfish Fillets in the United States. Lipids. 2014;49(10):1005–18. doi: 10.1007/s11745-014-3932-5 25108414
40. Bostock J, McAndrew B, Richards R, Jauncey K, Telfer T, Lorenzen K, et al. Aquaculture: global status and trends. Philos T R Soc B. 2010;365(1554):2897–912. doi: 10.1098/rstb.2010.0170 20713392
41. Henriques J, Dick JR, Tocher DR, Bell JG. Nutritional quality of salmon products available from major retailers in the UK: content and composition of n-3 long-chain PUFA. Brit J Nutr. 2014;112(6):964–75. Epub 2014/07/14. doi: 10.1017/S0007114514001603 25017007
42. Sprague M, Dick JR, Tocher DR. Impact of sustainable feeds on omega-3 long-chain fatty acid levels in farmed Atlantic salmon, 2006–2015. Sci Rep-UK. 2016;6(1):21892. doi: 10.1038/srep21892 26899924
43. Tur JA, Bibiloni MM, Sureda A, Pons A. Dietary sources of omega 3 fatty acids: public health risks and benefits. Brit J Nutr. 2012;107 Suppl 2:S23–52. Epub 2012/05/25. doi: 10.1017/S0007114512001456 22591897
44. Bradbury KE, Young HJ, Guo W, Key TJ. Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire. J Nutr Sci. 2018;7:e6–e. doi: 10.1017/jns.2017.66 29430297
45. Liu B, Young H, Crowe FL, Benson VS, Spencer EA, Key TJ, et al. Development and evaluation of the Oxford WebQ, a low-cost, web-based method for assessment of previous 24 h dietary intakes in large-scale prospective studies. Public health Nutr. 2011;14(11):1998–2005. Epub 2011/07/07. doi: 10.1017/S1368980011000942 21729481
46. Purcell S, Chang CC. PLINK 1.9-beta3. www.cog-genomics.org/plink/1.9/.
47. Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4(1).
48. Purcell S, ChangCC. PLINK 2.3 alpha. 2020. www.cog-genomics.org/plink/2.0/.
49. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393 26432245
50. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48(10):1284–7. Epub 2016/08/29. doi: 10.1038/ng.3656 27571263
51. Kutalik Z, Johnson T, Bochud M, Mooser V, Vollenweider P, Waeber G, et al. Methods for testing association between uncertain genotypes and quantitative traits. Biostatistics. 2010;12(1):1–17. doi: 10.1093/biostatistics/kxq039 20543033
52. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–1. doi: 10.1093/bioinformatics/btq340 20616382
53. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. Epub 2017/12/01. doi: 10.1038/s41467-017-01261-5 29184056
54. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005–d12. Epub 2018/11/18. doi: 10.1093/nar/gky1120 30445434
55. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. bioRxiv. 2014:005165. doi: 10.1101/005165
56. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26(18):2336–7. Epub 2010/07/17. doi: 10.1093/bioinformatics/btq419 20634204
57. Team R Consortium. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2019.
58. Battle A, Brown CD, Engelhardt BE, Montgomery SB. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–13. Epub 2017/10/13. doi: 10.1038/nature24277 29022597
Článek vyšel v časopise
PLOS Genetics
2021 Číslo 3
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- „Jednohubky“ z klinického výzkumu – 2024/44
Nejčtenější v tomto čísle
- DNA polymerase theta suppresses mitotic crossing over
- IKAROS is required for the measured response of NOTCH target genes upon external NOTCH signaling
- activin-2 is required for regeneration of polarity on the planarian anterior-posterior axis
- The etiology of Down syndrome: Maternal MCM9 polymorphisms increase risk of reduced recombination and nondisjunction of chromosome 21 during meiosis I within oocyte