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Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease

Nature Genetics volume 37, pages 243–253 (2005)Cite this article

Abstract

Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.

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Figure 1: Expression values of parental and RI strains for 12 transcripts in kidney and fat.
Figure 2: Locations of cis-acting eQTLs and previously mapped SHR pQTLs.
Figure 3: Location of trans-acting eQTLs and previously mapped SHR pQTLs.

References

  1. Cheung, V.G. et al. Natural variation in human gene expression assessed in lymphoblastoid cells. Nat. Genet. 33, 422–425 (2003).

    Article  CAS  Google Scholar 

  2. Brem, R.B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002).

    Article  CAS  Google Scholar 

  3. Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat. Genet. 35, 57–64 (2003).

    Article  CAS  Google Scholar 

  4. Schadt, E.E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Jacob, H.J. & Kwitek, A.E. Rat genetics: attaching physiology and pharmacology to the genome. Nat. Rev. Genet. 3, 33–42 (2002).

    Article  CAS  Google Scholar 

  7. Wallace, C.A. & Aitman, T.J. The rat comes clean. Nat. Genet. 36, 441–442 (2004).

    Article  CAS  Google Scholar 

  8. Gibbs, R.A. et al. Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428, 493–521 (2004).

    Article  CAS  Google Scholar 

  9. Okamoto, K. Spontaneous Hypertension: Its Pathogenesis and Complications (Springer, Berlin, 1972).

    Book  Google Scholar 

  10. Rao, R.H. Insulin resistance in spontaneously hypertensive rats: difference in interpretation based on insulin infusion rate or on plasma insulin in glucose clamp studies. Diabetes 42, 1364–1371 (1993).

    Article  CAS  Google Scholar 

  11. Hulman, S., Falkner, B. & Freyvogel, N. Insulin resistance in the conscious spontaneously hypertensive rat: euglycemic hyperinsulinemic clamp study. Metabolism 42, 14–18 (1993).

    Article  CAS  Google Scholar 

  12. Aitman, T.J. et al. Quantitative trait loci for cellular defects in glucose and fatty acid metabolism in hypertensive rats. Nat. Genet. 16, 197–201 (1997).

    Article  CAS  Google Scholar 

  13. Grundy, S.M., Brewer, H.B. Jr., Cleeman, J.I., Smith, S.C. Jr. & Lenfant, C. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 109, 433–438 (2004).

    Article  Google Scholar 

  14. Pravenec, M., Klir, P., Kren, V., Zicha, J. & Kunes, J. An analysis of spontaneous hypertension in spontaneously hypertensive rats by means of new recombinant inbred strains. J. Hypertens. 7, 217–221 (1989).

    Article  CAS  Google Scholar 

  15. Pravenec, M. et al. Genetic analysis of metabolic syndrome in the spontaneously hypertensive rat. Physiol. Res. 53 (Suppl 1), 15–23 (2004).

    Google Scholar 

  16. Silver, L.M. Mouse Genetics: Concepts and Applications (Oxford University Press, New York, 1995).

    Google Scholar 

  17. Cowley, A.W. Jr., Roman, R.J. & Jacob, H.J. Application of chromosomal substitution techniques in gene–function discovery. J. Physiol. 554, 46–55 (2004).

    Article  CAS  Google Scholar 

  18. Singer, J.B. et al. Genetic dissection of complex traits with chromosome substitution strains of mice. Science 304, 445–448 (2004).

    Article  CAS  Google Scholar 

  19. Belknap, J.K., Mitchell, S.R., O'Toole, L.A., Helms, M.L. & Crabbe, J.C. Type I and type II error rates for quantitative trait loci (QTL) mapping studies using recombinant inbred mouse strains. Behav. Genet. 26, 149–160 (1996).

    Article  CAS  Google Scholar 

  20. Zimdahl, H. et al. A SNP map of the rat genome generated from cDNA sequences. Science 303, 807 (2004).

    Article  CAS  Google Scholar 

  21. Glazier, A.M., Nadeau, J.H. & Aitman, T.J. Finding genes that underlie complex traits. Science 298, 2345–2349 (2002).

    Article  CAS  Google Scholar 

  22. Abiola, O. et al. The nature and identification of quantitative trait loci: a community's view. Nat. Rev. Genet. 4, 911–916 (2003).

    PubMed  Google Scholar 

  23. Aitman, T.J. et al. Identification of Cd36(Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat. Genet. 21, 76–83 (1999).

    Article  CAS  Google Scholar 

  24. Berge, K.E. et al. Accumulation of dietary cholesterol in sitosterolemia caused by mutations in adjacent ABC transporters. Science 290, 1771–1775 (2000).

    Article  CAS  Google Scholar 

  25. Lawn, R.M. et al. The Tangier disease gene product ABC1 controls the cellular apolipoprotein–mediated lipid removal pathway. J. Clin. Invest. 104, R25–R31 (1999).

    Article  CAS  Google Scholar 

  26. Karp, C.L. et al. Identification of complement factor 5 as a susceptibility locus for experimental allergic asthma. Nat. Immunol. 1, 221–226 (2000).

    Article  CAS  Google Scholar 

  27. Glazier, A.M., Scott, J. & Aitman, T.J. Molecular basis of the Cd36 chromosomal deletion underlying SHR defects in insulin action and fatty acid metabolism. Mamm. Genome 13, 108–113 (2002).

    Article  CAS  Google Scholar 

  28. Jansen, R.C. & Nap, J.-P. Genetical Genomics: the added value from segregation. Trends Genet. 17, 388–391 (2001).

    Article  CAS  Google Scholar 

  29. Frantz, S.A. et al. Successful isolation of a rat chromosome 1 blood pressure quantitative trait locus in reciprocal congenic strains. Hypertension 32, 639–646 (1998).

    Article  CAS  Google Scholar 

  30. Hubner, N., Lee, Y.A., Lindpaintner, K., Ganten, D. & Kreutz, R. Congenic substitution mapping excludes Sa as a candidate gene locus for a blood pressure quantitative trait locus on rat chromosome 1. Hypertension 34, 643–648 (1999).

    Article  CAS  Google Scholar 

  31. Frantz, S., Clemitson, J.R., Bihoreau, M.T., Gauguier, D. & Samani, N.J. Genetic dissection of region around the Sa gene on rat chromosome 1: evidence for multiple loci affecting blood pressure. Hypertension 38, 216–221 (2001).

    Article  CAS  Google Scholar 

  32. Kovacs, P., Voigt, B. & Kloting, I. Novel quantitative trait loci for blood pressure and related traits on rat chromosomes 1, 10, and 18. Biochem. Biophys. Res. Commun. 235, 343–348 (1997).

    Article  CAS  Google Scholar 

  33. Pravenec, M., Zidek, V., Kren, V., St Lezin, E. & Kurtz, T.W. Genetic isolation of a quantitative trait locus on chromosome 18 associated with blood pressure and salt sensitivity in the SHR. Am. J. Hypertens. 14, 82A (2001).

    Article  Google Scholar 

  34. Cambien, F. et al. Deletion polymorphism in the gene for angiotensin–converting enzyme is a potent risk factor for myocardial infarction. Nature 359, 641–644 (1992).

    Article  CAS  Google Scholar 

  35. Lindpaintner, K. et al. A prospective evaluation of an angiotensin–converting–enzyme gene polymorphism and the risk of ischemic heart disease. N. Engl. J. Med. 332, 706–711 (1995).

    Article  CAS  Google Scholar 

  36. Pravenec, M. et al. Transgenic rescue of defective Cd36 ameliorates insulin resistance in spontaneously hypertensive rats. Nat. Genet. 27, 156–158 (2001).

    Article  CAS  Google Scholar 

  37. McBride, M.W. et al. Microarray analysis of rat chromosome 2 congenic strains. Hypertension 41, 847–853 (2003).

    Article  CAS  Google Scholar 

  38. Rice, T. et al. Genome-wide linkage analysis of systolic and diastolic blood pressure: the Quebec Family Study. Circulation 102, 1956–1963 (2000).

    Article  CAS  Google Scholar 

  39. Jirout, M. et al. A new framework marker-based linkage map and SDPs for the rat HXB/BXH strain set. Mamm. Genome 14, 537–546 (2003).

    Article  CAS  Google Scholar 

  40. Kato, N. et al. Complete genome searches for quantitative trait loci controlling blood pressure and related traits in four segregating populations derived from Dahl hypertensive rats. Mamm. Genome 10, 259–265 (1999).

    Article  CAS  Google Scholar 

  41. Pravenec, M. et al. Mapping of quantitative trait loci for blood pressure and cardiac mass in the rat by genome scanning of recombinant inbred strains. J. Clin. Invest. 96, 1973–1978 (1995).

    Article  CAS  Google Scholar 

  42. Bottger, A. et al. Quantitative trait loci influencing cholesterol and phospholipid phenotypes map to chromosomes that contain genes regulating blood pressure in the spontaneously hypertensive rat. J. Clin. Invest. 98, 856–862 (1996).

    Article  CAS  Google Scholar 

  43. Printz, M.P., Jirout, M., Jaworski, R., Alemayehu, A. & Kren, V. Genetic Models in Applied Physiology. HXB/BXH rat recombinant inbred strain platform: a newly enhanced tool for cardiovascular, behavioral, and developmental genetics and genomics. J. Appl. Physiol. 94, 2510–2522 (2003).

    Article  CAS  Google Scholar 

  44. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  Google Scholar 

  45. Lander, E.S. et al. MAPMAKER–an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1, 174–181 (1987).

    Article  CAS  Google Scholar 

  46. Pravenec, M. et al. A genetic linkage map of the rat derived from recombinant inbred strains. Mamm. Genome 7, 117–127 (1996).

    Article  CAS  Google Scholar 

  47. Pravenec, M. et al. HXB/Ipcv and BXH/Cub recombinant inbred strains of the rat: strain distribution patterns of 632 alleles. Folia Biol. (Praha) 45, 203–215 (1999).

    CAS  Google Scholar 

  48. Churchill, G.A. & Doerge, R.W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

  50. Krushkal, J. et al. Genome-wide linkage analyses of systolic blood pressure using highly discordant siblings. Circulation 99, 1407–1410 (1999).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank H. Banks, N. Cooley, F. Rahman, M. Gerhardt, H. Kistel, S. Blachut and R. Sarwar for technical assistance; K. Manly for providing the eQTL Reaper software; and Affymetrix for donation of microarrays. We acknowledge funding to T.J.A. from the MRC Clinical Sciences Centre, from the British Heart Foundation and from a Wellcome Trust Cardiovascular Functional Genomics initiative; to N.H. from the German Ministry for Science and Education (National Genome Research Network); to M.P. and to V.K. from the Grant Agency of the Czech Republic; to M.P. and T.J.A. from the Wellcome Trust Collaborative Research Initiative Grant; to T.W.K. from the US National Institutes of Health; and to T.W.K. and M.P. from a Fogarty International Research Collaboration Award. M. Pravenec is an International Research Scholar of the Howard Hughes Medical Institute.

Author information

Author notes

  1. Caroline A Wallace, Heike Zimdahl and Enrico Petretto: These authors contributed equally to this work.

Authors and Affiliations

  1. Max-Delbrück-Center for Molecular Medicine, Berlin-Buch, 13125, Germany

    Norbert Hubner, Heike Zimdahl, Herbert Schulz, Oliver Hummel, Jan Monti, Gabriele Born, Sabine Schmidt & Anita Müller

  2. MRC Clinical Sciences Centre, Faculty of Medicine, Imperial College, London, W12 0NN, UK

    Caroline A Wallace, Enrico Petretto, Fiona Maciver, Michael Mueller, Helen Causton, Laurence Game, Stuart A Cook & Timothy J Aitman

  3. Institute of Physiology, Czech Academy of Sciences and Centre for Applied Genomics, 142 20 Prague 4, Czech Republic

    Vaclav Zidek, Alena Musilova, Vladimir Kren & Michal Pravenec

  4. Institute of Biology and Medical Genetics, Charles University, 120 00 Prague 2, Czech Republic

    Vladimir Kren & Michal Pravenec

  5. University of California, San Francisco, 94143-0134, California, USA

    Theodore W Kurtz

  6. Department of Epidemiology and Public Health, Imperial College, London, W2 1PG, UK

    John Whittaker

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Correspondence to Michal Pravenec or Timothy J Aitman.

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Supplementary information

Supplementary Fig. 1

Validation of microarray gene expression data by RT-PCR. (PDF 91 kb)

Supplementary Fig. 2

Removal of redundancy resulting from linkage of expression values from an individual probe set to multiple adjacent markers. (PDF 117 kb)

Supplementary Fig. 3

Variation of number of defined cis-acting eQTLs with window-size. (PDF 195 kb)

Supplementary Fig. 4

Sequence analysis of Pik3c3 5′-upstream region, exons and exon-intron boundaries. (PDF 69 kb)

Supplementary Table 1

Number of linkages detected in fat and kidney data sets and estimated false discovery rate for different significance thresholds. (PDF 42 kb)

Supplementary Table 2

Number of linkages identified in common by eQTL Reaper and Wilcoxon-Mann-Whitney test. (PDF 51 kb)

Supplementary Table 3

Comparison of linkage results in RI strains using microarray data and quantitative real-time PCR. (PDF 54 kb)

Supplementary Table 4

Physiological SHR QTLs mapped in previous genome screens. (XLS 57 kb)

Supplementary Table 5a

cis-acting eQTLs in fat and kidney tissue mapped at P<10−4. (XLS 57 kb)

Supplementary Table 5b (XLS 76 kb)

Supplementary Table 6

trans-acting eQTLs (P<10−2). (XLS 57 kb)

Supplementary Table 7

Description of genetic map of 1,011 markers generated for the RI panel. (PDF 61 kb)

Supplementary Table 8

Detailed information on the comparative analysis of rat and human blood pressure QTLs. (XLS 264 kb)

Supplementary Note (PDF 67 kb)

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Hubner, N., Wallace, C., Zimdahl, H. et al. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37, 243–253 (2005). https://doi.org/10.1038/ng1522

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