DOI QR코드

DOI QR Code

Effect of Genetic Predisposition on Blood Lipid Traits Using Cumulative Risk Assessment in the Korean Population

  • Go, Min-Jin (Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex) ;
  • Hwang, Joo-Yeon (Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex) ;
  • Kim, Dong-Joon (Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex) ;
  • Lee, Hye-Ja (Division of Metabolic Diseases, Center for Biomedical Sciences, Korea National Institute of Health) ;
  • Jang, Han-Byul (Division of Metabolic Diseases, Center for Biomedical Sciences, Korea National Institute of Health) ;
  • Park, Kyung-Hee (Department of Family Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine) ;
  • Song, Ji-Hyun (Division of Metabolic Diseases, Center for Biomedical Sciences, Korea National Institute of Health) ;
  • Lee, Jong-Young (Center for Genome Science, National Institute of Health, Osong Health Technology Administration Complex)
  • 투고 : 2012.04.10
  • 심사 : 2012.05.22
  • 발행 : 2012.06.30

초록

Dyslipidemia, mainly characterized by high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C) levels, is an important etiological factor in the development of cardiovascular disease (CVD). Considering the relationship between childhood obesity and CVD risk, it would be worthwhile to evaluate whether previously identified lipid-related variants in adult subjects are associated with lipid variations in a childhood obesity study (n = 482). In an association analysis for 16 genome-wide association study (GWAS)-based candidate loci, we confirmed significant associations of a genetic predisposition to lipoprotein concentrations in a childhood obesity study. Having two loci (rs10503669 at LPL and rs16940212 at LIPC) that showed the strongest association with blood levels of TG and HDL-C, we calculated a genetic risk score (GRS), representing the sum of the risk alleles. It has been observed that increasing GRS is significantly associated with decreased HDL-C (effect size, $-1.13{\pm}0.07$) compared to single nucleotide polymorphism combinations without two risk variants. In addition, a positive correlation was observed between allelic dosage score and risk allele (rs10503669 at LPL) on high TG levels (effect size, $10.89{\pm}0.84$). These two loci yielded consistent associations in our previous meta-analysis. Taken together, our findings demonstrate that the genetic architecture of circulating lipid levels (TG and HDL-C) overlap to a large extent in childhood as well as in adulthood. Post-GWAS functional characterization of these variants is further required to elucidate their pathophysiological roles and biological mechanisms.

키워드

참고문헌

  1. Meagher EA. Addressing cardiovascular disease in women: focus on dyslipidemia. J Am Board Fam Pract 2004;17:424-437. https://doi.org/10.3122/jabfm.17.6.424
  2. Kristiansson K, Perola M, Tikkanen E, Kettunen J, Surakka I, Havulinna AS, et al. Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits. Circ Cardiovasc Genet 2012;5:242-249. https://doi.org/10.1161/CIRCGENETICS.111.961482
  3. Tan A, Sun J, Xia N, Qin X, Hu Y, Zhang S, et al. A genome- wide association and gene-environment interaction study for serum triglycerides levels in a healthy Chinese male population. Hum Mol Genet 2012;21:1658-1664. https://doi.org/10.1093/hmg/ddr587
  4. Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet 2009;41:47-55. https://doi.org/10.1038/ng.269
  5. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 2008;40:189-197. https://doi.org/10.1038/ng.75
  6. Kathiresan S, Willer CJ, Peloso GM, Demissie S, Musunuru K, Schadt EE, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet 2009;41:56-65. https://doi.org/10.1038/ng.291
  7. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, Clarke R, et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 2008;40:161-169. https://doi.org/10.1038/ng.76
  8. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research, Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007;316:1331-1336. https://doi.org/10.1126/science.1142358
  9. Kim YJ, Go MJ, Hu C, Hong CB, Kim YK, Lee JY, et al. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet 2011;43:990-995. https://doi.org/10.1038/ng.939
  10. Bradfield JP, Taal HR, Timpson NJ, Scherag A, Lecoeur C, Warrington NM, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet 2012;44:526-531. https://doi.org/10.1038/ng.2247
  11. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci U S A 2012;109:1193-1198. https://doi.org/10.1073/pnas.1119675109
  12. Liu S, Song Y. Building genetic scores to predict risk of complex diseases in humans: is it possible? Diabetes 2010;59:2729-2731. https://doi.org/10.2337/db10-1081
  13. Qi L, Ma J, Qi Q, Hartiala J, Allayee H, Campos H. Genetic risk score and risk of myocardial infarction in Hispanics. Circulation 2011;123:374-380. https://doi.org/10.1161/CIRCULATIONAHA.110.976613
  14. Vaarhorst AA, Lu Y, Heijmans BT, Dollé ME, Böhringer S, Putter H, et al. Literature-based genetic risk scores for coronary heart disease: the Cardiovascular Registry Maastricht (CAREMA) prospective cohort study. Circ Cardiovasc Genet 2012;5:202-209. https://doi.org/10.1161/CIRCGENETICS.111.960708
  15. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534. https://doi.org/10.1038/ng.357
  16. Rabbee N, Speed TP. A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 2006;22:7-12. https://doi.org/10.1093/bioinformatics/bti741
  17. Yeung JM, Sham PC, Chan AS, Cherny SS. OpenADAM: an open source genome-wide association data management system for Affymetrix SNP arrays. BMC Genomics 2008;9:636. https://doi.org/10.1186/1471-2164-9-636
  18. Johnson R, McNutt P, MacMahon S, Robson R. Use of the Friedewald formula to estimate LDL-cholesterol in patients with chronic renal failure on dialysis. Clin Chem 1997;43:2183-2184.
  19. Malhotra A, Wolford JK; American Diabetes Association GENNID Study Group. Analysis of quantitative lipid traits in the genetics of NIDDM (GENNID) study. Diabetes 2005;54:3007-3014. https://doi.org/10.2337/diabetes.54.10.3007
  20. Elbein SC, Hasstedt SJ. Quantitative trait linkage analysis of lipid-related traits in familial type 2 diabetes: evidence for linkage of triglyceride levels to chromosome 19q. Diabetes 2002;51:528-535. https://doi.org/10.2337/diabetes.51.2.528
  21. Lloyd LJ, Langley-Evans SC, McMullen S. Childhood obesity and adult cardiovascular disease risk: a systematic review. Int J Obes (Lond) 2010;34:18-28. https://doi.org/10.1038/ijo.2009.61
  22. Beilin L, Huang RC. Childhood obesity, hypertension, the metabolic syndrome and adult cardiovascular disease. Clin Exp Pharmacol Physiol 2008;35:409-411. https://doi.org/10.1111/j.1440-1681.2008.04887.x
  23. Gaw A. HDL-C and triglyceride levels: relationship to coronary heart disease and treatment with statins. Cardiovasc Drugs Ther 2003;17:53-62. https://doi.org/10.1023/A:1024207925670
  24. Cornelis MC, Qi L, Zhang C, Kraft P, Manson J, Cai T, et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann Intern Med 2009;150:541-550. https://doi.org/10.7326/0003-4819-150-8-200904210-00008
  25. Franceschini N, Carty C, Buzkova P, Reiner AP, Garrett T, Lin Y, et al. Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the PAGE study. Circ Cardiovasc Genet 2011;4:661-672. https://doi.org/10.1161/CIRCGENETICS.111.960096
  26. Sarzynski MA, Jacobson P, Rankinen T, Carlsson B, Sjöström L, Carlsson LM, et al. Association of GWAS-based candidate genes with HDL-cholesterol levels before and after bariatric surgery in the Swedish obese subjects study. J Clin Endocrinol Metab 2011;96:E953-E957. https://doi.org/10.1210/jc.2010-2227
  27. Lettre G, Palmer CD, Young T, Ejebe KG, Allayee H, Benjamin EJ, et al. Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: the NHLBI CARe Project. PLoS Genet 2011;7:e1001300. https://doi.org/10.1371/journal.pgen.1001300
  28. Waterworth DM, Ricketts SL, Song K, Chen L, Zhao JH, Ripatti S, et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arterioscler Thromb Vasc Biol 2010;30:2264-2276. https://doi.org/10.1161/ATVBAHA.109.201020
  29. Munshi A, Babu MS, Kaul S, Rajeshwar K, Balakrishna N, Jyothy A. Association of LPL gene variant and LDL, HDL, VLDL cholesterol and triglyceride levels with ischemic stroke and its subtypes. J Neurol Sci 2012;318:51-54. https://doi.org/10.1016/j.jns.2012.04.006
  30. Need AC, Ge D, Weale ME, Maia J, Feng S, Heinzen EL, et al. A genome-wide investigation of SNPs and CNVs in schizophrenia. PLoS Genet 2009;5:e1000373. https://doi.org/10.1371/journal.pgen.1000373
  31. Ma X, Deng W, Liu X, Li M, Chen Z, He Z, et al. A genome-wide association study for quantitative traits in schizophrenia in China. Genes Brain Behav 2011;10:734-739. https://doi.org/10.1111/j.1601-183X.2011.00712.x
  32. Lewis CM, Levinson DF, Wise LH, DeLisi LE, Straub RE, Hovatta I, et al. Genome scan meta-analysis of schizophrenia and bipolar disorder, part II: Schizophrenia. Am J Hum Genet 2003;73:34-48. https://doi.org/10.1086/376549
  33. Xie C, Wang ZC, Liu XF, Wang L, Yang MS. Association between schizophrenia and single nucleotide polymorphisms in lipoprotein lipase gene in a Han Chinese population. Psychiatr Genet 2011;21:307-314. https://doi.org/10.1097/YPG.0b013e32834acc85
  34. Neale BM, Fagerness J, Reynolds R, Sobrin L, Parker M, Raychaudhuri S, et al. Genome-wide association study of advanced age-related macular degeneration identifies a role of the hepatic lipase gene (LIPC). Proc Natl Acad Sci U S A 2010;107:7395-7400. https://doi.org/10.1073/pnas.0912019107
  35. Harkewicz R, Du H, Tong Z, Alkuraya H, Bedell M, Sun W, et al. Essential role of ELOVL4 in very long chain fatty acid synthesis and retinal function. J Biol Chem 2011 Dec 24 [Epub]. http://dx.doi.org/10.1074/jbc.M111.256073.

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