Abstract

Statins are prescribed widely to lower plasma low-density lipoprotein (LDL) concentrations and cardiovascular disease risk1 and have been shown to have beneficial effects in a broad range of patients2,3. However, statins are associated with an increased risk, albeit small, of clinical myopathy4 and type 2 diabetes5. Despite evidence for substantial genetic influence on LDL concentrations6, pharmacogenomic trials have failed to identify genetic variations with large effects on either statin efficacy7,8,9 or toxicity10, and have produced little information regarding mechanisms that modulate statin response. Here we identify a downstream target of statin treatment by screening for the effects of in vitro statin exposure on genetic associations with gene expression levels in lymphoblastoid cell lines derived from 480 participants of a clinical trial of simvastatin treatment7. This analysis identified six expression quantitative trait loci (eQTLs) that interacted with simvastatin exposure, including rs9806699, a cis-eQTL for the gene glycine amidinotransferase (GATM) that encodes the rate-limiting enzyme in creatine synthesis. We found this locus to be associated with incidence of statin-induced myotoxicity in two separate populations (meta-analysis odds ratio = 0.60). Furthermore, we found that GATM knockdown in hepatocyte-derived cell lines attenuated transcriptional response to sterol depletion, demonstrating that GATM may act as a functional link between statin-mediated lowering of cholesterol and susceptibility to statin-induced myopathy.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Gene Expression Omnibus

Data deposits

The gene expression data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE36868 and in Synapse (https://www.synapse.org/) under accession number syn299510. Code and analytical output complementary to this analysis are also provided through Synapse (https://www.synapse.org/#!Synapse:syn299510). The genotype data have been deposited in the database for genotypes and phenotypes (dbGaP, http://www.ncbi.nlm.nih.gov/gap) under accession number phs000481. The full set of eQTLs identified in our study is available at http://eqtl.uchicago.edu.

References

  1. 1.

    Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90,056 participants in 14 randomised trials of statins. Lancet 366, 1267–1278 (2005)

  2. 2.

    et al. Phenotypic predictors of response to simvastatin therapy among African-Americans and Caucasians: the Cholesterol and Pharmacogenetics (CAP) Study. Am. J. Cardiol. 97, 843–850 (2006)

  3. 3.

    et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N. Engl. J. Med. 359, 2195–2207 (2008)

  4. 4.

    , , & Statin myopathy: a common dilemma not reflected in clinical trials. Cleve. Clin. J. Med. 78, 393–403 (2011)

  5. 5.

    et al. Statin therapy and risk of developing type 2 diabetes: a meta-analysis. Diabetes Care 32, 1924–1929 (2009)

  6. 6.

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

  7. 7.

    et al. Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS ONE 5, e9763 (2010)

  8. 8.

    et al. Pharmacogenetic study of statin therapy and cholesterol reduction. J. Am. Med. Assoc. 291, 2821–2827 (2004)

  9. 9.

    et al. Replication of LDL GWAs hits in PROSPER/PHASE as validation for future (pharmaco)genetic analyses. BMC Med. Genet. 12, 131 (2011)

  10. 10.

    The. SEARCH Collaborative Group SLCO1B1 variants and statin-induced myopathy–a genomewide study. N. Engl. J. Med. 359, 789–799 (2008)

  11. 11.

    , , & Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005)

  12. 12.

    et al. Global analysis of the impact of environmental perturbation on cis-regulation of gene expression. PLoS Genet. 7, e1001279 (2011)

  13. 13.

    et al. Systems genetics analysis of gene-by-environment interactions in human cells. Am. J. Hum. Genet. 86, 399–410 (2010)

  14. 14.

    et al. Interactions between glucocorticoid treatment and cis-regulatory polymorphisms contribute to cellular response phenotypes. PLoS Genet. 7, e1002162 (2011)

  15. 15.

    et al. Genetic variation in radiation-induced cell death. Genome Res. 332–339 (2011)

  16. 16.

    & The SREBP pathway: regulation of cholesterol metabolism by proteolysis of a membrane-bound transcription factor. Cell 89, 331–340 (1997)

  17. 17.

    et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004)

  18. 18.

    et al. Population genomics of human gene expression. Nature Genet. 39, 1217–1224 (2007)

  19. 19.

    , , & The effects of EBV transformation on gene expression levels and methylation profiles. Hum. Mol. Genet. 20, 1643–1652 (2011)

  20. 20.

    et al. Genetic analysis of human traits in vitro: drug response and gene expression in lymphoblastoid cell lines. PLoS Genet. 4, e1000287 (2008)

  21. 21.

    et al. Combined influence of LDLR and HMGCR sequence variation on lipid-lowering response to simvastatin. Arterioscler. Thromb. Vasc. Biol. 30, 1485–1492 (2010)

  22. 22.

    et al. Coordinately regulated alternative splicing of genes involved in cholesterol biosynthesis and uptake. PLoS ONE 6, e19420 (2011)

  23. 23.

    & Bayesian statistical methods for genetic association studies. Nature Rev. Genet. 10, 681–690 (2009)

  24. 24.

    & Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2007)

  25. 25.

    et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet. 7, e1002078 (2011)

  26. 26.

    et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nature Genet. 41, 712–717 (2009)

  27. 27.

    et al. Use of an electronic medical record to characterize cases of intermediate statin-induced muscle toxicity. Prev. Cardiol. 12, 88–94 (2009)

  28. 28.

    et al. l-arginine:glycine amidinotransferase deficiency protects from metabolic syndrome. Hum. Mol. Genet. 22, 110–123 (2013)

  29. 29.

    et al. GAMT, a p53-inducible modulator of apoptosis, is critical for the adaptive response to nutrient stress. Mol. Cell 36, 379–392 (2009)

  30. 30.

    et al. l-arginine:glycine amidinotransferase (AGAT) deficiency: clinical presentation and response to treatment in two patients with a novel mutation. Mol. Genet. Metab. 101, 228–232 (2010)

  31. 31.

    & Epstein–Barr virus transformation of cryopreserved lymphocytes: prolonged experience with technique. Am. J. Hum. Genet. 49, 467 (1991)

  32. 32.

    , & Supervised normalization of microarrays. Bioinformatics 26, 1308–1315 (2010)

  33. 33.

    A direct approach to false discovery rates. J. R. Stat. Soc. 64, 479–498 (2002)

  34. 34.

    , , , & GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics 23, 3251–3253 (2007)

  35. 35.

    & Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 3, e114 (2007)

  36. 36.

    & Practical issues in imputation-based association mapping. PLoS Genet. 4, e1000279 (2008)

  37. 37.

    et al. Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nature Rev. Drug Discov. 6, 904–916 (2007)

  38. 38.

    et al. Impact of common genetic variation on response to simvastatin therapy among 18 705 participants in the Heart Protection Study. Eur. Heart J. 34, 982–992 (2013)

  39. 39.

    & ChromHMM: automating chromatin-state discovery and characterization. Nature Methods 9, 215–216 (2012)

  40. 40.

    et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002)

Download references

Acknowledgements

This project was funded by a grant from the US National Institutes of Health (NIH), U01 HL69757. B.E.E. was funded through the Bioinformatics Research Development Fund, supported by K. and G. Gould and NIH grant K99/R00 HG006265. M.S. was funded by NIH grant HG002585. We acknowledge the efforts of T. Kitchner and R. Mareedu for case validation in the Marshfield cohort. SEARCH was supported by the Medical Research Council, British Heart Foundation, National Health Service Genetic Knowledge Park, Centre National de Génotypage and Merck. The Heart Protection Study was funded by grants from the Medical Research Council, British Heart Foundation, Roche Vitamins and Merck. J.C.H. acknowledges support from the BHF Centre of Research Excellence, Oxford, UK. Genetic analysis in JUPITER was funded by a grant from AstraZeneca to D.I.C. and P.M.R.

Author information

Author notes

    • Lara M. Mangravite
    •  & Barbara E. Engelhardt

    These authors contributed equally to this work.

    • Barbara E. Engelhardt
    •  & Mark J. Rieder

    Present addresses: Biostatistics and Bioinformatics Department and Department of Statistical Science, Duke University, Durham, North Carolina, USA (B.E.E.); Adaptive Biotechnologies, Seattle, Washington, USA (M.J.R.).

Affiliations

  1. Sage Bionetworks, 1100 Fairview Avenue North, Seattle, Washington 98109, USA

    • Lara M. Mangravite
    •  & Brigham H. Mecham
  2. Department of Human Genetics, University of Chicago, 920 East 58th Street, Chicago, Illinois 60637, USA

    • Barbara E. Engelhardt
    • , Bryan Howie
    • , Heejung Shim
    •  & Matthew Stephens
  3. Children’s Hospital Oakland Research Institute, 5700 Martin Luther King Jr Way, Oakland, California 94609, USA

    • Marisa W. Medina
    • , Devesh Naidoo
    •  & Ronald M. Krauss
  4. Department of Genome Sciences, University of Washington, 3720 15th Avenue Northeast, Seattle, Washington 98195, USA

    • Joshua D. Smith
    • , Mark J. Rieder
    •  & Deborah A. Nickerson
  5. Department of Genetics, University of Pennsylvania, 415 Curie Boulevard, Philadelphia, Pennsylvania 19103, USA

    • Christopher D. Brown
  6. Center for Cardiovascular Disease Prevention, Division of Preventative Medicine, Brigham and Women’s Hospital, 900 Commonwealth Drive, Boston, Massachusetts 02115, USA

    • Daniel I. Chasman
    •  & Paul M. Ridker
  7. Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University Medical Center, 23rd Avenue South, Nashville, Tennessee 37232, USA

    • QiPing Feng
    •  & Russell A. Wilke
  8. Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, California 90502, USA

    • Yii.-Der I. Chen
    •  & Jerome I. Rotter
  9. Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Richard Doll Building, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK

    • Jemma C. Hopewell
    • , Sarah Parish
    • , Jane Armitage
    •  & Rory Collins
  10. Department of Statistics, Eckhart Hall, 5734 South University Avenue, University of Chicago, Chicago, Illinois 60637, USA

    • Matthew Stephens

Authors

  1. Search for Lara M. Mangravite in:

  2. Search for Barbara E. Engelhardt in:

  3. Search for Marisa W. Medina in:

  4. Search for Joshua D. Smith in:

  5. Search for Christopher D. Brown in:

  6. Search for Daniel I. Chasman in:

  7. Search for Brigham H. Mecham in:

  8. Search for Bryan Howie in:

  9. Search for Heejung Shim in:

  10. Search for Devesh Naidoo in:

  11. Search for QiPing Feng in:

  12. Search for Mark J. Rieder in:

  13. Search for Yii.-Der I. Chen in:

  14. Search for Jerome I. Rotter in:

  15. Search for Paul M. Ridker in:

  16. Search for Jemma C. Hopewell in:

  17. Search for Sarah Parish in:

  18. Search for Jane Armitage in:

  19. Search for Rory Collins in:

  20. Search for Russell A. Wilke in:

  21. Search for Deborah A. Nickerson in:

  22. Search for Matthew Stephens in:

  23. Search for Ronald M. Krauss in:

Contributions

L.M.M. designed experiment and analyses, generated samples, performed analyses, and wrote the manuscript. B.E.E. designed and performed analyses and wrote the manuscript. C.D.B. performed analyses of ENCODE data. B.H.M. designed and performed correlation analyses. J.D.S., M.J.R. and D.A.N. generated expression and genotype data. M.W.M. and D.N. designed, performed and analysed functional experiments. B.H. and H.S. developed and performed the imputation methodology. R.A.W, Q.F., J.D.S., M.J.R. and D.A.N. collected and genotyped the myopathy cohort from the Marshfield clinic and performed association analyses. J.C.H., S.P., J.A. and R.C. collected and genotyped myopathy cohort from the SEARCH trial and performed association analyses in that cohort along with the Heart Protection Study. J.I.R. and Y.-D.I.C. measured creatine kinase in CAP. D.I.C. and P.M.R. measured creatine kinase and performed related analyses in JUPITER. M.S. supervised, designed and contributed to analyses, and participated in manuscript development. R.M.K. supervised the project and participated in experimental design and manuscript development. M.S. and R.M.K. co-directed this project.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Lara M. Mangravite or Matthew Stephens or Ronald M. Krauss.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Figures 1-5, Supplementary Methods, Supplementary Data, Supplementary Tables 2, 4, 7, 8, 9 and additional references. See separate files for Supplementary Tables 1, 3, 5 and 6.

Excel files

  1. 1.

    Supplementary Table 1

    Stable cis-eQTLs identified in association with gene expression following simvastatin exposure (treated, T), control exposure (control, C), or averaged across both exposures (averaged, S). Top eQTL listed for each gene. Significance threshold set at log10BF=3.24.

  2. 2.

    Supplementary Table 3

    Supplementary Table 3 Stable trans-eQTLs identified in association with gene expression following simvastatin exposure (treated, T), control exposure (control, C), or averaged across both exposures (averaged, S). Significance threshold set at log10BF=7.20.

  3. 3.

    Supplementary Table 5

    Differential cis-eQTLs identified by univariate analysis to be in association with gene expression following simvastatin exposure (treated, T), control exposure (control, C), or averaged across both exposures (averaged, S). Top eQTL listed for each gene with log10BF>2.0. Significance threshold set at log10BF=4.9.

  4. 4.

    Supplementary Table 6

    Differential trans-eQTLs identified by univariate analysis as associated with gene expression following simvastatin exposure (treated, T), control exposure (control, C), or averaged across both exposures (averaged, S). Top eQTL listed for each gene with log10BF>5.0. Significance threshold set at log10BF=7.20.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature12508

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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