A statin-dependent QTL for GATM expression is associated with statin-induced myopathy

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.

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Figure 1: Simvastatin treatment alters transcript expression in LCLs.
Figure 2: Treatment-specific QTL associated with GATM expression.
Figure 3: GATM knockdown attenuated sterol-mediated induction of expression of SREBP-responsive genes.

Accession codes

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

    Cholesterol Treatment Trialists' (CTT) Collaborators 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)

    Article  Google Scholar 

  2. 2

    Simon, J. A. 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)

    CAS  Article  Google Scholar 

  3. 3

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

    CAS  Article  Google Scholar 

  4. 4

    Fernandez, G., Spatz, E. S., Jablecki, C. & Phillips, P. S. Statin myopathy: a common dilemma not reflected in clinical trials. Cleve. Clin. J. Med. 78, 393–403 (2011)

    Article  Google Scholar 

  5. 5

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

    CAS  Article  Google Scholar 

  6. 6

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

    CAS  Article  ADS  Google Scholar 

  7. 7

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

    Article  ADS  Google Scholar 

  8. 8

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

    CAS  Article  Google Scholar 

  9. 9

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

    CAS  Article  Google Scholar 

  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

    Brem, R. B., Storey, J. D., Whittle, J. & Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. Nature 436, 701–703 (2005)

    CAS  Article  ADS  Google Scholar 

  12. 12

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

    CAS  Article  Google Scholar 

  13. 13

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

    CAS  Article  Google Scholar 

  14. 14

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

    CAS  Article  Google Scholar 

  15. 15

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

    Article  Google Scholar 

  16. 16

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

    CAS  Article  Google Scholar 

  17. 17

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

    CAS  Article  ADS  Google Scholar 

  18. 18

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

    CAS  Article  Google Scholar 

  19. 19

    Caliskan, M., Cusanovich, D. A., Ober, C. & Gilad, Y. The effects of EBV transformation on gene expression levels and methylation profiles. Hum. Mol. Genet. 20, 1643–1652 (2011)

    CAS  Article  Google Scholar 

  20. 20

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

    Article  Google Scholar 

  21. 21

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

    CAS  Article  Google Scholar 

  22. 22

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

    CAS  Article  ADS  Google Scholar 

  23. 23

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

    CAS  Article  Google Scholar 

  24. 24

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

    Article  Google Scholar 

  25. 25

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

    CAS  Article  Google Scholar 

  26. 26

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

    Article  Google Scholar 

  27. 27

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

    CAS  Article  Google Scholar 

  28. 28

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

    CAS  Article  Google Scholar 

  29. 29

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

    CAS  Article  Google Scholar 

  30. 30

    Edvardson, S. 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)

    CAS  Article  Google Scholar 

  31. 31

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

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Mecham, B. H., Nelson, P. S. & Storey, J. D. Supervised normalization of microarrays. Bioinformatics 26, 1308–1315 (2010)

    CAS  Article  Google Scholar 

  33. 33

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

    MathSciNet  Article  Google Scholar 

  34. 34

    Subramanian, A., Kuehn, H., Gould, J., Tamayo, P. & Mesirov, J. P. GSEA-P: a desktop application for Gene Set Enrichment Analysis. Bioinformatics 23, 3251–3253 (2007)

    CAS  Article  Google Scholar 

  35. 35

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

    Article  Google Scholar 

  36. 36

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

    Article  Google Scholar 

  37. 37

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

    CAS  Article  Google Scholar 

  38. 38

    Hopewell, J. C. 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)

    CAS  Article  Google Scholar 

  39. 39

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

    CAS  Article  Google Scholar 

  40. 40

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

    CAS  Article  Google Scholar 

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.

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Authors

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.

Corresponding authors

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

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Competing interests

The authors declare no competing financial interests.

Supplementary information

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. (PDF 601 kb)

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. (XLS 749 kb)

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. (XLS 53 kb)

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. (XLS 169 kb)

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. (XLS 116 kb)

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Mangravite, L., Engelhardt, B., Medina, M. et al. A statin-dependent QTL for GATM expression is associated with statin-induced myopathy. Nature 502, 377–380 (2013). https://doi.org/10.1038/nature12508

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