Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Whole-genome sequence–based analysis of high-density lipoprotein cholesterol


We describe initial steps for interrogating whole-genome sequence data to characterize the genetic architecture of a complex trait, levels of high-density lipoprotein cholesterol (HDL-C). We report whole-genome sequencing and analysis of 962 individuals from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies. From this analysis, we estimate that common variation contributes more to heritability of HDL-C levels than rare variation, and screening for mendelian variants for dyslipidemia identified individuals with extreme HDL-C levels. Whole-genome sequencing analyses highlight the value of regulatory and non-protein-coding regions of the genome in addition to protein-coding regions.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Distribution of HDL-C levels for carriers of identified mendelian variation.
Figure 2: Survey of the genomic landscape using Lachesis.


  1. Roach, J.C. et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science 328, 636–639 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lupski, J.R. et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N. Engl. J. Med. 362, 1181–1191 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Castelli, W.P. et al. HDL cholesterol and other lipids in coronary heart disease. The cooperative lipoprotein phenotyping study. Circulation 55, 767–772 (1977).

    Article  CAS  PubMed  Google Scholar 

  4. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Stenson, P.D. et al. Human Gene Mutation Database: towards a comprehensive central mutation database. J. Med. Genet. 45, 124–126 (2008).

    Article  CAS  PubMed  Google Scholar 

  7. Brousseau, M.E. et al. Novel mutations in the gene encoding ATP-binding cassette 1 in four tangier disease kindreds. J. Lipid Res. 41, 433–441 (2000).

    CAS  PubMed  Google Scholar 

  8. Rhyne, J., Mantaring, M.M., Gardner, D.F. & Miller, M. Multiple splice defects in ABCA1 cause low HDL-C in a family with hypoalphalipoproteinemia and premature coronary disease. BMC Med. Genet. 10, 1 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Weers, P.M. et al. Novel N-terminal mutation of human apolipoprotein A-I reduces self-association and impairs LCAT activation. J. Lipid Res. 52, 35–44 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Papp, A.C. et al. Cholesteryl ester transfer protein (CETP) polymorphisms affect mRNA splicing, HDL levels, and sex-dependent cardiovascular risk. PLoS ONE 7, e31930 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dachet, C., Poirier, O., Cambien, F., Chapman, J. & Rouis, M. New functional promoter polymorphism, CETP/–629, in cholesteryl ester transfer protein (CETP) gene related to CETP mass and high density lipoprotein cholesterol levels: role of Sp1/Sp3 in transcriptional regulation. Arterioscler. Thromb. Vasc. Biol. 20, 507–515 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Li, Y., Sidore, C., Kang, H.M., Boehnke, M. & Abecasis, G.R. Low-coverage sequencing: implications for design of complex trait association studies. Genome Res. 21, 940–951 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Romeo, S. et al. Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL. Nat. Genet. 39, 513–516 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Montgomery, S.B. et al. ORegAnno: an open access database and curation system for literature-derived promoters, transcription factor binding sites and regulatory variation. Bioinformatics 22, 637–640 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Purcell, S. et al. PLINK: a toolset for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wu, M.C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Griffith, O.L. et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res. 36, D107–D113 (2008).

    Article  CAS  PubMed  Google Scholar 

  21. Cabili, M.N. et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev. 25, 1915–1927 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Pruitt, K.D., Tatusova, T., Klimke, W. & Maglott, D.R. NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Res. 37, D32–D36 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Gordon, A., Glazko, G., Qiu, X. & Yakolev, A. Control of the mean number of false discoveries, Bonferroni and stability of multiple testing. Ann. Appl. Stat. 1, 179–190 (2007).

    Article  Google Scholar 

Download references


Atherosclerosis Risk in Communities (ARIC) Study: This ARIC study is carried out as a collaborative study supported by NHLBI contracts HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C. The authors thank the staff and participants of the ARIC study for their important contributions. Ancillary study support has been provided by NHLBI-sponsored project RC2HL102419-02.

Cardiovascular Health Study (CHS): This CHS research was supported by NHLBI contracts N01-HC-85239, N01-HC-85079 through N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, N01-HC-75150, N01-HC-45133 and HHSN268201200036C and by NHLBI grants HL080295, HL087652 and HL105756, with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG-023629, AG-15928, AG-20098 and AG-027058 from the National Institute on Aging (NIA). See also

Framingham Heart Study (FHS) of the NHLBI of the US National Institutes of Health and Boston University School of Medicine: This work was supported by the NHLBI FHS (contract N01-HC-25195).

Author information

Authors and Affiliations



J.Y., D.M., F.Y., E.B. and R.G. were responsible for the design and implementation of the whole-genome sequencing and variant calling. A.L. and K.R. contributed to the analysis of mendelian variation. X.L. and C.Z. contributed to the estimation of heritability. A.C.M., A.V. and A.D.J. performed statistical analysis of the whole-genome sequence and phenotype data. G.H., C.J.O. and B.M.P. were involved in participant recruitment, consenting and examination. A.C.M., A.V., A.D.J., X.L., J.B., G.H., C.J.O., B.M.P., L.A.C., R.G. and E.B. jointly conceived the study and contributed to preparation and editing of the manuscript.

Corresponding author

Correspondence to Eric Boerwinkle.

Ethics declarations

Competing interests

B.M.P. serves on the Data and Safety Monitoring Board for a clinical trial of a device funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Medtronic.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11, Supplementary Tables 1–7, Supplementary Note (PDF 412 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium. Whole-genome sequence–based analysis of high-density lipoprotein cholesterol. Nat Genet 45, 899–901 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing