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Genome-wide association studies identified novel loci for non-high-density lipoprotein cholesterol and its postprandial lipemic response

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Abstract

Non-high-density lipoprotein cholesterol(NHDL) is an independent and superior predictor of CVD risk as compared to low-density lipoprotein alone. It represents a spectrum of atherogenic lipid fractions with possibly a distinct genomic signature. We performed genome-wide association studies (GWAS) to identify loci influencing baseline NHDL and its postprandial lipemic (PPL) response. We carried out GWAS in 4,241 participants of European descent. Our discovery cohort included 928 subjects from the Genetics of Lipid-Lowering Drugs and Diet Network Study. Our replication cohorts included 3,313 subjects from the Heredity and Phenotype Intervention Heart Study and Family Heart Study. A linear mixed model using the kinship matrix was used for association tests. The best association signal was found in a tri-genic region at RHOQ-PIGF-CRIPT for baseline NHDL (lead SNP rs6544903, discovery p = 7e−7, MAF = 2 %; validation p = 6e−4 at 0.1 kb upstream neighboring SNP rs3768725, and 5e−4 at 0.7 kb downstream neighboring SNP rs6733143, MAF = 10 %). The lead and neighboring SNPs were not perfect surrogate proxies to each other (D′ = 1, r 2 = 0.003) but they seemed to be partially dependent (likelihood ration test p = 0.04). Other suggestive loci (discovery p < 1e−6) included LOC100419812 and LOC100288337 for baseline NHDL, and LOC100420502 and CDH13 for NHDL PPL response that were not replicated (p > 0.01). The current and first GWAS of NHDL yielded an interesting common variant in RHOQ-PIGF-CRIPT influencing baseline NHDL levels. Another common variant in CDH13 for NHDL response to dietary high-fat intake challenge was also suggested. Further validations for both loci from large independent studies, especially interventional studies, are warranted.

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References

  • Abecasis GR, Cherny SS, Cookson WO, Cardon LR (2001) GRR: graphical representation of relationship errors. Bioinformatics 17:742–743

    Article  CAS  PubMed  Google Scholar 

  • Agarwala R, Biesecker LG, Hopkins KA, Francomano CA, Schaffer AA (1998) Software for constructing and verifying pedigrees within large genealogies and an application to the old order Amish of Lancaster County. Genome Res 8:211–221

    Article  CAS  PubMed  Google Scholar 

  • Agarwala R, Biesecker LG, Tomlin JF, Schäffer AA (1999) Towards a complete North American Anabaptist genealogy: a systematic approach to merging partially overlapping genealogy resources. Am J Med Genet 86:156–161

    Article  CAS  PubMed  Google Scholar 

  • Agarwala R, Schaffer AA, Tomlin JF (2001) Towards a complete North American Anabaptist Genealogy II: analysis of inbreeding. Hum Biol 73:533–545

    Article  CAS  PubMed  Google Scholar 

  • Almasy L, Blangero J (1998) Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet 62:1198–1211

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Aslibekyan S, An P, Frazier-Wood AC, Kabagambe EK, Irvin MR, Straka RJ et al (2013) Preliminary evidence of genetic determinants of adiponectin response to fenofibrate in the genetics of lipid lowering drugs and diet network. Nutr Metab Cardiovasc Dis 23:987–994

    Article  CAS  PubMed  Google Scholar 

  • Ban HJ, Heo JY, Oh KS, Park KJ (2010) Identification of type 2 diabetes-associated combination of SNPs using support vector machine. BMC Genet 11:26

    Article  PubMed Central  PubMed  Google Scholar 

  • Beekman M, Heijmana BT, Martin NG, Pedersen NL, Whitfield JB, DeFaire U et al (2002) Heritabilities of apolipoprotein and lipid levels in three countries. Twin Res 5:87–97

    Article  PubMed  Google Scholar 

  • Chung CM, Lin TH, Chen JW, Leu HB, Yang HC, Ho HY et al (2011) A genome-wide association study reveals a quantitative trait locus of adiponectin on CDH13 that predicts cardiometabolic outcomes. Diabetes 60:2417–2423

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Corbett J, Kraja A, Borecki IB, Province MA (2003) Use of a random coefficient regression (RCR) model to estimate growth parameters. BMC Genet 4(Suppl 1):S5

    Article  PubMed Central  PubMed  Google Scholar 

  • Corey KE, Lai M, Gelrud LG, Misdraji J, Barlow LL, Zheng H et al (2012) Non-high-density lipoprotein cholesterol as a biomarker for nonalcoholic steatohepatitis. Clin Gastroenterol Hepatol 10:651–656

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Croteau-Chonka DC, Marvelle AF, Lange EM, Lee NR, Adair LS, Lange LA et al (2011) Genome-wide association study of anthropometric traits and evidence of interactions with age and study year in Filipino women. Obesity 19:1019–1027

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Emerging Risk Factors Collaboration (ERFC) Writing Committee (2009) Major lipids, apolipoproteins, and risk of vascular disease. JAMA 302:1993–2000

    Article  Google Scholar 

  • Fava C, Danese E, Montagnana M, Sjögren M, Almgren M, Guidi GC et al (2011) A variant upstream of the CDH13 adiponectin receptor gene and metabolic syndrome in Swedes. Am J Cardiol 108:1432–1437

    Article  CAS  PubMed  Google Scholar 

  • Feng S, Wang S, Chen C–C, Lan L (2011) GWAPower: a statistical power calculation software for genome-wide association studies with quantitative traits. BMC Genet 12:12

    Article  PubMed Central  PubMed  Google Scholar 

  • Fox CS, Heard-Costa N, Cupples LA, Dupuis J, Vasan RS, Atwood LD (2007) Genome-wide association to body mass index and waist circumference: the Framingham heart study 100 K project. BMC Med Genet 8(Suppl 1):S18

    Article  PubMed Central  PubMed  Google Scholar 

  • Gao X, Starmer J, Martin ER (2008) A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genet Epidemiol 32:361–369

    Article  PubMed  Google Scholar 

  • Heath SC (1997) Markov chain Monte Carlo segregation and linkage analysis for oligogenic models. Am J Hum Genet 61:748–760

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Hedrick P, Kumar S (2001) Mutations and linkage disequilibrium in human mtDNA. Eur J Hum Genet 9:969–972

    Article  CAS  PubMed  Google Scholar 

  • Higgins M, Province M, Heiss G, Eckfeldt J, Ellison RC, Folsom AR et al (1996) NHLBI family heart study: objectives and design. Am J Epidemiol 143:1219–1228

    Article  CAS  PubMed  Google Scholar 

  • Jee SH, Sull JW, Lee JE, Shin C, Park J, Kimm H et al (2010) Adiponectin concentration: a genome-wide association study. Am J Hum Genet 87:545–552

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Johansson A, Marroni F, Hayward C, Franklin CS, Kirichenko AV, Jonasson I et al (2010) Linkage and genome-wide association analysis of obesity-related phenotypes: association of weight with the MGAT1 gene. Obesity 18:803–808

    Article  CAS  PubMed  Google Scholar 

  • Kabagambe EK, Ordovas JM, Tsai MY, Borecki IB, Hopkins PN, Glasser SP et al (2009) Smoking, inflammatory patterns and postprandial hypertriglyceridemia. Atherosclerosis 203:633–639

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Kathiresan S, Manning AK, Demissie S, D’Agostino RB, Surti A, Guiducci C et al (2007) A genome-wide association study for blood lipid phenotypes in the Framingham heart study. BMC Med Genet 8(Suppl 1):S17

    Article  PubMed Central  PubMed  Google Scholar 

  • Korn JM, Kuruvilla FG, McCarroll SA, Wysoker A, Nemesh J, Cawley S et al (2008) Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat Genet 40:1253–1260

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Levy D, Larson MG, Benjamin EJ, Newton-Cheh C, Wang TJ, Hwang SJ et al (2007) Framingham heart study 100 K project: genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet 8(Suppl 1):S3

    Article  PubMed Central  PubMed  Google Scholar 

  • Li Y, Willer CJ, Sanna S, Abecasis GR (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10:387–406

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34:816–834

    Article  PubMed Central  PubMed  Google Scholar 

  • Ling H, Waterworth DM, Stirnadel HA, Pollin TI, Barter PJ, Kesäniemi YA et al (2009) Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study. Obesity 17:737–744

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Liu J, Sempos CT, Donahue RP, Dorn J, Trevisan M, Grundy SM (2006) Non-high-density lipoprotein and very-low-density lipoprotein cholesterol and their risk predictive values in coronary heart disease. Am J Cardiol 98:1363–1368

    Article  CAS  PubMed  Google Scholar 

  • Madsen KS, Miller JP, Province MA (1986) The use of an extended baseline period in the evaluation of treatment in a longitudinal Duchenne muscular dystrophy trial. Stat Med 5:231–241

    Article  CAS  PubMed  Google Scholar 

  • Mahajan N, Ference BA, Arora N, Madhavan R, Bhattacharya P, Sudhakar R et al (2012) Role of non-high-density lipoprotein cholesterol in predicting cerebrovascular events in patients following myocardial infarction. Am J Cardiol 109:1694–1699

    Article  CAS  PubMed  Google Scholar 

  • Marroni F, Pinosio S, Zaina G, Fogolari F, Felice N, Cattonaro F et al (2011) Nucleotide diversity and linkage disequilibrium in Populus nigra cinnamyl alcohol dehydrogenase (CAD4) gene. Tree Gen Genomes 7:1011–1023

    Article  Google Scholar 

  • Miller M, Ginsberg HN, Schaefer EJ (2008) Relative atherogenicity and predictive value of non high-density lipoprotein cholesterol for coronary heart disease. Am J Cardiol 101:1003–1008

    Article  CAS  PubMed  Google Scholar 

  • Mitchell BD, McArdle PF, Shen H, Rampersaud E, Pollin TI, Bielak LF et al (2008) The genetic response to short term interventions affecting cardiovascular function: rationale and design of the heredity and phenotype intervention (HAPI) heart study. Am Heart J 155:823–828

    Article  PubMed Central  PubMed  Google Scholar 

  • Muller JC (2004) Linkage disequilibrium for different scales and applications. Brief Bioinfo 5:355–364

    Article  Google Scholar 

  • Org E, Eyheramendy S, Juhanson P, Gieger C, Lichtner P, Klopp N et al (2009) Genome-wide scan identifies CDH13 as a novel susceptibility locus contributing to blood pressure determination in two European populations. Hum Mol Genet 18:2288–2296

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Patsch JR, Miesenbock G, Hopferwieser T, Mühlberger V, Knapp E, Dunn JK et al (1992) Relation of triglyceride metabolism and coronary artery disease. Studies in the postprandial state. Arterioscler Thromb 12:1336–1345

    Article  CAS  PubMed  Google Scholar 

  • Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  • Province MA (2001) The significance of not finding a gene. Am J Hum Genet 69:660–663

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Province MA, Borecki IB (2013) A correlated meta-analysis strategy for data mining ‘omic’ scans. Pac Symp Biocomput 18:236–246

    Google Scholar 

  • Psychiatric GWAS Consortium Bipolar Disorder Working Group (2011) Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 43:977–983

    Article  Google Scholar 

  • Robinson JG (2009) Are you targeting non-high-density lipoprotein cholesterol? J Am Coll Cardiol 55:42–44

    Article  PubMed  Google Scholar 

  • Robinson JG (2010) Low high-density lipoprotein cholesterol and chronic disease risk marker or causal? J Am Coll Cardiol 55:2855–2857

    Article  CAS  PubMed  Google Scholar 

  • Shia WC, Ku TH, Tsao YM, Hsia CH, Chang YM, Huang CH et al (2011) Genetic copy number variants in myocardial infarction patients with hyperlipidemia. BMC Genom 12(Suppl 3):S23

    Article  CAS  Google Scholar 

  • Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M et al (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–713

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Welcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678

    Article  Google Scholar 

  • Wojczynski MK, Gao G, Borecki IB, Hopkins PN, Parnell L, Lai CQ et al (2010) Apolipoprotein B genetic variants modify the response to fenofibrate: a GOLDN study. J Lipid Res 51:3316–3323

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Yang Q, Kathiresan S, Lin JP, Tofler GH, O’Donnell CJ (2007) Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study. BMC Med Genet 8(Suppl 1):S12

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

The authors thank the other investigators, the staff and the participants of the GOLDN Study, the HAPI Heart Study and the Family Heart Study. The GOLDN Study support was provided by the NHLBI grant U01 HL072524. The FamHS was supported by NIH Grants R01 HL087700 and R01 HL088215 (Michael A. Province, PI) from NHLBI, and R01 DK8925601 and R01 075681 (Ingrid B. Borecki, PI) from NIDDK. The investigators thank the GOLDN and FamHS participants and staff for their valuable contributions. The HAPI Heart Study was supported by NIH research Grants U01 HL072515, R01 HL104193, U01 HL084756 and the Mid Atlantic Nutrition and Obesity Research Center Grant P30 DK072488. We thank the staff at the Amish Research Clinic for their outstanding efforts and our Amish research volunteers for their long-standing partnership in research.

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Authors and Affiliations

  1. Department of Genetics Division of Statistical Genomics (Campus Box 8506), Washington University School of Medicine, 4444 Forest Park Blvd, St. Louis, MO, 63108, USA

    Ping An, Mary F. Feitosa, Mary K. Wojczynski, E. Warwick Daw, Michael A. Province & Ingrid B. Borecki

  2. Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN, USA

    Robert J. Straka

  3. Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Toni I. Pollin, Jeffrey R. O’Connell, Quince Gibson, Kathleen A. Ryan & Alan R. Shuldiner

  4. Geriatric Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA

    Alan R. Shuldiner

  5. Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT, USA

    Paul N. Hopkins

  6. Department of Laboratory Medicine and Pathology, School of Medicine, University of Minnesota, Minneapolis, MN, USA

    Michael Y. Tsai

  7. Nutrition and Genomics Laboratory, US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA

    Chao-Qiang Lai & Jose M. Ordovas

  8. IMDEA-Alimentacion, Madrid, Spain

    Jose M. Ordovas

  9. Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigacions Cardiovasculares, Madrid, Spain

    Jose M. Ordovas

  10. Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA

    Donna K. Arnett

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  1. Ping An
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Correspondence to Ping An.

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Ping An and Robert J. Straka contributed equally to this work.

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An, P., Straka, R.J., Pollin, T.I. et al. Genome-wide association studies identified novel loci for non-high-density lipoprotein cholesterol and its postprandial lipemic response. Hum Genet 133, 919–930 (2014). https://doi.org/10.1007/s00439-014-1435-3

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