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Knowledge-Driven Analysis Identifies a Gene–Gene Interaction Affecting High-Density Lipoprotein Cholesterol Levels in Multi-Ethnic Populations


Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene–gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein–protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected Pc = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene–gene interaction in the European American cohorts from the Framingham Heart Study (Pc = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; Pc = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (Pc = 0.004) and in the Hispanic American sample from MESA (Pc = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene–gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations.


Vyšlo v časopise: Knowledge-Driven Analysis Identifies a Gene–Gene Interaction Affecting High-Density Lipoprotein Cholesterol Levels in Multi-Ethnic Populations. PLoS Genet 8(5): e32767. doi:10.1371/journal.pgen.1002714
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1002714

Souhrn

Total cholesterol, low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) levels are among the most important risk factors for coronary artery disease. We tested for gene–gene interactions affecting the level of these four lipids based on prior knowledge of established genome-wide association study (GWAS) hits, protein–protein interactions, and pathway information. Using genotype data from 9,713 European Americans from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and a locus near LIPC in their effect on HDL-C levels (Bonferroni corrected Pc = 0.002). Using an adaptive locus-based validation procedure, we successfully validated this gene–gene interaction in the European American cohorts from the Framingham Heart Study (Pc = 0.002) and the Multi-Ethnic Study of Atherosclerosis (MESA; Pc = 0.006). The interaction between these two loci is also significant in the African American sample from ARIC (Pc = 0.004) and in the Hispanic American sample from MESA (Pc = 0.04). Both HMGCR and LIPC are involved in the metabolism of lipids, and genome-wide association studies have previously identified LIPC as associated with levels of HDL-C. However, the effect on HDL-C of the novel gene–gene interaction reported here is twice as pronounced as that predicted by the sum of the marginal effects of the two loci. In conclusion, based on a knowledge-driven analysis of epistasis, together with a new locus-based validation method, we successfully identified and validated an interaction affecting a complex trait in multi-ethnic populations.


Zdroje

1. HindorffLASethupathyPJunkinsHARamosEMMehtaJP 2009 Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106 9362 9367

2. ManolioTACollinsFSCoxNJGoldsteinDBHindorffLA 2009 Finding the missing heritability of complex diseases. Nature 461 747 753

3. FrazerKAMurraySSSchorkNJTopolEJ 2009 Human genetic variation and its contribution to complex traits. Nat Rev Genet 10 241 251

4. MaherB 2008 Personal genomes: The case of the missing heritability. Nature 456 18 21

5. EichlerEEFlintJGibsonGKongALealSM 2010 Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11 446 450

6. TeslovichTMMusunuruKSmithAVEdmondsonACStylianouIM 2010 Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466 707 713

7. CheverudJMRoutmanEJ 1995 Epistasis and its contribution to genetic variance components. Genetics 139 1455 1461

8. CockerhamCC 1954 An Extension of the Concept of Partitioning Hereditary Variance for Analysis of Covariances among Relatives When Epistasis Is Present. Genetics 39 859 882

9. ZukOHechterESunyaevSRLanderES 2012 The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci doi:10.1073/pnas.1119675109

10. HuntSCHasstedtSJKuidaHStultsBMHopkinsPN 1989 Genetic Heritability and Common Environmental Components of Resting and Stressed Blood Pressures, Lipids, and Body-Mass Index in Utah Pedigrees and Twins. American Journal of Epidemiology 129 625 638

11. BatesonWSERPRCHCC 1905 Reports to the Evolution Committee of the Royal Society, Report II London, UK Harrison and Sons

12. MartinMPGaoXLeeJHNelsonGWDetelsR 2002 Epistatic interaction between KIR3DS1 and HLA-B delays the progression to AIDS. Nature genetics 31 429 434

13. WeiWHHemaniGGyeneseiAVitartVNavarroP 2012 Genome-wide analysis of epistasis in body mass index using multiple human populations. European Journal of Human Genetics doi:10.1038/ejhg.2012.17

14. ShimomuraKLow-ZeddiesSSKingDPSteevesTDLWhiteleyA 2001 Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. Genome research 11 959 980

15. CarlborgÖKerjeSSchützKJacobssonLJensenP 2003 A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome research 13 413 421

16. CaicedoALStinchcombeJROlsenKMSchmittJPuruggananMD 2004 Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proceedings of the National Academy of Sciences of the United States of America 101 15670

17. CarlborgOHaleyCS 2004 Epistasis: too often neglected in complex trait studies? Nature Reviews Genetics 5 618-U614

18. CordellHJ 2009 Detecting gene-gene interactions that underlie human diseases. Nature Reviews Genetics 10 392 404

19. MooreJHWilliamsSM 2009 Epistasis and Its Implications for Personal Genetics. American Journal of Human Genetics 85 309 320

20. GaoHGrankaJMFeldmanMW 2010 On the Classification of Epistatic Interactions. Genetics 184 827 U351

21. MaLRuneshaHBDvorkinDGarbeJRDaY 2008 Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies. BMC bioinformatics 9 315

22. MarchiniJDonnellyPCardonLR 2005 Genome-wide strategies for detecting multiple loci that influence complex diseases. Locus 2 0.0

23. JiaPZhengSLongJZhengWZhaoZ 2011 dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks. Bioinformatics 27 95

24. SunYVKardiaSLR 2010 Identification of epistatic effects using a protein–protein interaction database. Human Molecular Genetics 19 4345

25. WuXDongHLuoLZhuYPengG 2010 A Novel Statistic for Genome-Wide Interaction Analysis. PLoS Genet 6 e1001131 doi:10.1371/journal.pgen.1001131

26. MaLYangJRuneshaHBTanakaTFerrucciL 2010 Genome-wide association analysis of total cholesterol and high-density lipoprotein cholesterol levels using the Framingham Heart Study data. BMC Medical Genetics 11 55 doi:10.1186/1471-2350-11-55

27. HeJWangKEdmondsonACRaderDJLiC 2011 Gene-based interaction analysis by incorporating external linkage disequilibrium information. European Journal of Human Genetics 19 164 172

28. LiuYXuHChenSChenXZhangZ 2011 Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases. PLoS Genet 7 e1001338 doi:10.1371/journal.pgen.1001338

29. WilliamsOD 1989 The Atherosclerosis Risk in Communities (Aric) Study - Design and Objectives. American Journal of Epidemiology 129 687 702

30. CordellHJ 2002 Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Human Molecular Genetics 11 2463 2468

31. CockerhamCCZengZB 1996 Design III with marker loci. Genetics 143 1437 1456

32. LiMXGuiHSKwanJSHShamPC 2011 GATES: A Rapid and Powerful Gene-Based Association Test Using Extended Simes Procedure. American Journal of Human Genetics 88 283 293

33. DawberTRMeadorsGFMooreFE 1951 Epidemiological Approaches to Heart Disease: The Framingham Study. American Journal of Public Health and the Nations Health 41 279 286

34. BildDEBluemkeDABurkeGLDetranoRRouxAVD 2002 Multi-ethnic study of atherosclerosis: Objectives and design. American Journal of Epidemiology 156 871 881

35. KeatingBJTischfieldSMurraySSBhangaleTPriceTS 2008 Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies. PLoS ONE 3 e3583 doi:10.1371/journal.pone.0003583

36. ClarkAGBoerwinkleEHixsonJSingCF 2005 Determinants of the success of whole-genome association testing. Genome research 15 1463 1467

37. DumitrescuLCartyCLTaylorKSchumacherFRHindorffLA 2011 Genetic Determinants of Lipid Traits in Diverse Populations from the Population Architecture using Genomics and Epidemiology (PAGE) Study. PLoS Genet 7 e1002138 doi:10.1371/journal.pgen.1002138

38. DicksonSPWangKKrantzIHakonarsonHGoldsteinDB 2010 Rare Variants Create Synthetic Genome-Wide Associations. PLoS Biol 8 e1000294 doi:10.1371/journal.pbio.1000294

39. LuskeyKLStevensB 1985 Human 3-Hydroxy-3-Methylglutaryl Coenzyme-a Reductase - Conserved Domains Responsible for Catalytic Activity and Sterol-Regulated Degradation. Journal of Biological Chemistry 260 271 277

40. Santamarina-FojoSHaudenschildCAmarM 1998 The role of hepatic lipase in lipoprotein metabolism and atherosclerosis. Current Opinion in Lipidology 9 211 219

41. GoldsteinJLBrownMS 1990 Regulation of the mevalonate pathway. Nature 343 425 430

42. EisenbergS 1984 High density lipoprotein metabolism. J Lipid Res 25 1017 1058

43. AnnemaWTietgeUJF 2011 Role of Hepatic Lipase and Endothelial Lipase in High-Density Lipoprotein-Mediated Reverse Cholesterol Transport. Current Atherosclerosis Reports 13 257 265

44. KrajaATVaidyaDPankowJSGoodarziMOAssimesTL 2011 A Bivariate Genome-Wide Approach to Metabolic Syndrome. Diabetes 60 1329 1339

45. XinXSrinivasanSRChenWBoerwinkleEBerensonGS 2003 Interaction effect of Serine447Stop variant of the lipoprotein lipase gene and C-514T variant of the hepatic lipase gene on serum triglyceride levels in young adults: The Bogalusa heart study. Metabolism-Clinical and Experimental 52 1337 1342

46. IsaacsAAulchenkoYSHofmanASijbrandsEJGSayed-TabatabaeiFA 2007 Epistatic effect of cholesteryl ester transfer protein and hepatic lipase on serum high-density lipoprotein cholesterol levels. Journal of Clinical Endocrinology & Metabolism 92 2680 2687

47. Rodriguez-RodriguezEMateoIInfanteJLlorcaJGarcia-GorostiagaI 2009 Interaction between HMGCR and ABCA1 cholesterol-related genes modulates Alzheimer's disease risk. Brain Research 1280 166 171

48. MailmanMDFeoloMJinYKimuraMTrykaK 2007 The NCBI dbGaP database of genotypes and phenotypes. Nature genetics 39 1181 1186

49. KathiresanSManningAKDemissieSD'AgostinoRBSurtiA 2007 A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study. BMC Medical Genetics 8 Suppl 1 S17 doi:10.1186/1471-2350-8-S1-S17

50. HowieBNDonnellyPMarchiniJ 2009 A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5 e1000529 doi:10.1371/journal.pgen.1000529

51. AltshulerDMGibbsRAPeltonenLSchaffnerSFYuF 2010 Integrating common and rare genetic variation in diverse human populations. Nature 467 52 58

52. AltshulerDLDurbinRMAbecasisGRBentleyDRChakravartiA 2010 A map of human genome variation from population-scale sequencing. Nature 467 1061 1073

53. PriceALPattersonNJPlengeRMWeinblattMEShadickNA 2006 Principal components analysis corrects for stratification in genome-wide association studies. Nature genetics 38 904 909

54. KangHMSulJHServiceSKZaitlenNAKongSY 2010 Variance component model to account for sample structure in genome-wide association studies. Nature genetics 42 348 U110

55. KempthorneO 1954 The Correlation between Relatives in a Random Mating Population. Proceedings of the Royal Society of London Series B-Biological Sciences 143 103 113

56. PurcellSNealeBTodd-BrownKThomasLFerreiraMAR 2007 PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics 81 559 575

57. BarrettJCFryBMallerJDalyMJ 2005 Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21 263 265

58. LemaitreRNTanakaTTangWManichaikulAFoyM 2011 Genetic Loci Associated with Plasma Phospholipid n-3 Fatty Acids: A Meta-Analysis of Genome-Wide Association Studies from the CHARGE Consortium. PLoS Genet 7 e1002193 doi:10.1371/journal.pgen.1002193

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