Genetics of gene expression and its effect on disease (original) (raw)

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Gene Expression Omnibus

Data deposits

All the gene expression data generated for this study have been deposited into the GEO database under accession numbers GSE7965 and GPL3991. The authors declare competing financial interests: details accompany the full-text HTML version of the paper at www.nature.com/nature.

References

  1. Schadt, E. E. et al. Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302 (2003)
    Article ADS CAS Google Scholar
  2. Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
    Article CAS Google Scholar
  3. Johnson, J. M. et al. Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science 302, 2141–2144 (2003)
    Article ADS CAS Google Scholar
  4. Shoemaker, D. D. et al. Experimental annotation of the human genome using microarray technology. Nature 409, 922–927 (2001)
    Article ADS CAS Google Scholar
  5. Welsh, J. B. et al. Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer. Proc. Natl Acad. Sci. USA 98, 1176–1181 (2001)
    Article ADS CAS Google Scholar
  6. Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genet. 37, 710–717 (2005)
    Article CAS Google Scholar
  7. Schadt, E. E., Sachs, A. & Friend, S. Embracing complexity, inching closer to reality. Sci. STKE 2005, pe40 (2005)
    Google Scholar
  8. Zhu, J. et al. An integrative genomics approach to the reconstruction of gene networks in segregating populations. Cytogenet. Genome Res. 105, 363–374 (2004)
    Article CAS Google Scholar
  9. Brem, R. B., Yvert, G., Clinton, R. & Kruglyak, L. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002)
    Article ADS CAS Google Scholar
  10. Bystrykh, L. et al. Uncovering regulatory pathways that affect hematopoietic stem cell function using ‘genetical genomics’. Nature Genet. 37, 225–232 (2005)
    Article CAS Google Scholar
  11. Chesler, E. J. et al. Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genet. 37, 233–242 (2005)
    Article CAS Google Scholar
  12. Monks, S. A. et al. Genetic inheritance of gene expression in human cell lines. Am. J. Hum. Genet. 75, 1094–1105 (2004)
    Article CAS Google Scholar
  13. Morley, M. et al. Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747 (2004)
    Article ADS CAS Google Scholar
  14. Mehrabian, M. et al. Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits. Nature Genet. 37, 1224–1233 (2005)
    Article CAS Google Scholar
  15. 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)
    Article ADS CAS Google Scholar
  16. Cheung, V. G. et al. Mapping determinants of human gene expression by regional and genome-wide association. Nature 437, 1365–1369 (2005)
    Article ADS CAS Google Scholar
  17. Ranganathan, P. et al. Expression profiling of genes regulated by TGF-β: differential regulation in normal and tumour cells. BMC Genom. 8 98 doi: 10.1186/1471-2164-8-98 (2007)
    Article CAS Google Scholar
  18. Brem, R. B. & Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. Proc. Natl Acad. Sci. USA 102, 1572–1577 (2005)
    Article ADS CAS Google Scholar
  19. Hubbard, T. et al. Ensembl 2005. Nucleic Acids Res. 33, D447–D453 (2005)
    Article CAS Google Scholar
  20. Whitney, A. R. et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl Acad. Sci. USA 100, 1896–1901 (2003)
    Article ADS CAS Google Scholar
  21. Storey, J. D. & Tibshirani, R. Statistical methods for identifying differentially expressed genes in DNA microarrays. Methods Mol. Biol. 224, 149–157 (2003)
    CAS PubMed Google Scholar
  22. Di Gregorio, G. B. et al. Expression of CD68 and macrophage chemoattractant protein-1 genes in human adipose and muscle tissues: association with cytokine expression, insulin resistance, and reduction by pioglitazone. Diabetes 54, 2305–2313 (2005)
    Article CAS Google Scholar
  23. Lumeng, C. N., Bodzin, J. L. & Saltiel, A. R. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J. Clin. Invest. 117, 175–184 (2007)
    Article CAS Google Scholar
  24. Neels, J. G. & Olefsky, J. M. Inflamed fat: what starts the fire? J. Clin. Invest. 116, 33–35 (2006)
    Article CAS Google Scholar
  25. Wellen, K. E. & Hotamisligil, G. S. Obesity-induced inflammatory changes in adipose tissue. J. Clin. Invest. 112, 1785–1788 (2003)
    Article CAS Google Scholar
  26. Steemers, F. J. & Gunderson, K. L. Illumina, Inc. Pharmacogenomics 6, 777–782 (2005)
    Article Google Scholar
  27. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005)
    Article MathSciNet Google Scholar
  28. Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N. & Barabasi, A. L. Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
    Article ADS CAS Google Scholar
  29. Ghazalpour, A. et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet 2, e130 (2006)
    Article Google Scholar
  30. Lum, P. Y. et al. Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes. J. Neurochem. 97 (suppl. 1). 50–62 (2006)
    Article CAS Google Scholar
  31. Chen, Y. et al. Variations in DNA elucidate molecular networks that cause disease. Nature doi: 10.1038/nature06757 (this issue)
  32. Gulcher, J. R., Kristjansson, K., Gudbjartsson, H. & Stefansson, K. Protection of privacy by third-party encryption in genetic research in Iceland. Eur. J. Hum. Genet. 8, 739–742 (2000)
    Article CAS Google Scholar
  33. He, Y. D. et al. Microarray standard data set and figures of merit for comparing data processing methods and experiment designs. Bioinformatics 19, 956–965 (2003)
    Article CAS Google Scholar
  34. Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000)
    Article CAS Google Scholar
  35. Kong, A. et al. A high-resolution recombination map of the human genome. Nature Genet. 31, 241–247 (2002)
    Article CAS Google Scholar
  36. Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998)
    Article CAS Google Scholar
  37. Gudbjartsson, D. F., Jonasson, K., Frigge, M. L. & Kong, A. Allegro, a new computer program for multipoint linkage analysis. Nature Genet. 25, 12–13 (2000)
    Article CAS Google Scholar
  38. Kong, A. & Cox, N. J. Allele-sharing models: LOD scores and accurate linkage tests. Am. J. Hum. Genet. 61, 1179–1188 (1997)
    Article CAS Google Scholar
  39. Badner, J. A., Gershon, E. S. & Goldin, L. R. Optimal ascertainment strategies to detect linkage to common disease alleles. Am. J. Hum. Genet. 63, 880–888 (1998)
    Article CAS Google Scholar
  40. Amos, C. I. Robust variance-components approach for assessing genetic linkage in pedigrees. Am. J. Hum. Genet. 54, 535–543 (1994)
    CAS PubMed PubMed Central Google Scholar
  41. Churchill, G. A. & Doerge, R. W. Empirical threshold values for quantitative trait mapping. Genetics 138, 963–971 (1994)
    CAS PubMed PubMed Central Google Scholar

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Acknowledgements

The authors acknowledge the participating families and the staff at the Clinical Research Centre for their cooperation. Genotyping service was provided at the deCode Genetics genotyping facilities.

Author Contributions V.E., E.E.S., K.S. and G.T. wrote the paper. G.T., E.E.S., A.K., D.G. and F.Z. performed statistical analysis. Tissue sampling and/or molecular profiling was carried out by H.G.G., T.S., B.G.L., G.H.E., S.C., M.M., Aslaug Jonasdottir, Adalbjorg Jonasdottir, G.B. and K.K. V.E., J.Z., U.T., A.S.L., A.H., B.Z., G.B.W., S. Gunnarsdottir, S. Gretarsdottir, K.P.M., V.S., I.R., A.H., U.S., H.S., R.F., J.R.G., K.S., M.L.R. and J.R.L. performed the genetic analysis and/or data-mining. K.S. and E.E.S. contributed equally to this work.

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Author notes

  1. Eric E. Schadt and Kari Stefansson: These authors contributed equally to this work.

Authors and Affiliations

  1. deCODE genetics, 101 Reykjavik, Iceland
    Valur Emilsson, Gudmar Thorleifsson, Florian Zink, Agnar Helgason, G. Bragi Walters, Steinunn Gunnarsdottir, Magali Mouy, Valgerdur Steinthorsdottir, Gudrun H. Eiriksdottir, Gyda Bjornsdottir, Inga Reynisdottir, Daniel Gudbjartsson, Anna Helgadottir, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Unnur Styrkarsdottir, Solveig Gretarsdottir, Kristinn P. Magnusson, Hreinn Stefansson, Ragnheidur Fossdal, Kristleifur Kristjansson, Unnur Thorsteinsdottir, Jeffrey R. Gulcher, Augustine Kong & Kari Stefansson
  2. Rosetta Inpharmatics, LLC, 401 Terry Ave N, Seattle, Washington 98109, USA,
    Valur Emilsson, Bin Zhang, Amy S. Leonardson, Jun Zhu, Sonia Carlson, John R. Lamb & Eric E. Schadt
  3. Department of Surgery, National University Hospital, 101 Reykjavik, Iceland
    Hjortur G. Gislason, Tryggvi Stefansson & Bjorn G. Leifsson
  4. Merck Research Laboratories, Rahway, New Jersey 07065, USA,
    Marc L. Reitman

Authors

  1. Valur Emilsson
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  2. Gudmar Thorleifsson
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  3. Bin Zhang
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  4. Amy S. Leonardson
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  5. Florian Zink
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  6. Jun Zhu
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  7. Sonia Carlson
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  8. Agnar Helgason
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  9. G. Bragi Walters
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  10. Steinunn Gunnarsdottir
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  11. Magali Mouy
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  12. Valgerdur Steinthorsdottir
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  13. Gudrun H. Eiriksdottir
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  14. Gyda Bjornsdottir
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  15. Inga Reynisdottir
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  16. Daniel Gudbjartsson
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  17. Anna Helgadottir
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  18. Aslaug Jonasdottir
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  19. Adalbjorg Jonasdottir
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  20. Unnur Styrkarsdottir
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  21. Solveig Gretarsdottir
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  22. Kristinn P. Magnusson
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  23. Hreinn Stefansson
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  24. Ragnheidur Fossdal
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  25. Kristleifur Kristjansson
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  26. Hjortur G. Gislason
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  27. Tryggvi Stefansson
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  28. Bjorn G. Leifsson
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  29. Unnur Thorsteinsdottir
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  30. John R. Lamb
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  31. Jeffrey R. Gulcher
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  32. Marc L. Reitman
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  33. Augustine Kong
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  34. Eric E. Schadt
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  35. Kari Stefansson
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Corresponding authors

Correspondence toEric E. Schadt or Kari Stefansson.

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Competing interests

The following authors own stocks in either deCode Genetics, Inc. or Merck & Co., Inc.: V.E., E.E.S., G.T., G.B., S.C., M.M., J.Z., Aslaug Jonasdottir, Adalbjorg Jonasdottir, K.K., U.T., A.S.L., A.H., B.Z., G.B.W., F.Z., S. Gunnarsdottir, S. Gretarsdottir, K.P.M., V.S., I.R., D.G., A.H., U.S. H.S., R.F., A.K., K.S., J.R.G., M.L.R. and J.R.L.

Supplementary information

Supplementary Information

The file contains Supplementary Results with additional references, Supplementary Tables 1-7 and Supplementary Figures 1-7 with Legends. The file contains Supplementary Results on the probes overlapping SNPs, distribution of cis eSNPs as regards the location of probes, detection of trans eQTLs and eQTL hotspots, comparison of expression linkage and association results and finally the additional information regarding genes in the mouse MEMN that have been shown to be causal for metabolic diseases. Tables include a summary of cohort description (Supplementary Table 1), results on the re-sequencing of array probes (Supplementary Table 2), expression trait vs. clinical trait correlations (Supplementary Table 3), heritability and eQTL results (Supplementary Tables 4 and 5), detection of significant trans eQTLs and eQTL hotspots (Supplementary Table 6) and finally the pathway enrichment for the gene sets in the MEMN module (Supplementary Table 7). The figures show chromosomal distribution of eQTLs in IFB in the real data and a simulated dataset (Supplementary Figure 1), distribution of BMI, gene expression vs. BMI correlations and heritability in the IFA cohort (Supplementary Figure 2), the agreement between the linkage and association data (Supplementary Figure 3), distribution of cis eSNPs as regards the location of probes (Supplementary Figure 4), the localization and specificity of the association signal (Supplementary Figure 5), the gene set overlap between the male and female specific MEMN module in humans (Supplementary Figure 6) and finally the comparison between the connectivity structure of the MEMN genes in between the females and males. (PDF 828 kb)

Supplementary description

The file contains description of the RNA sample processing, the design of the arrays, quality controlling and processing of the probe hybridization. Here, appropriate references and Figure 1 (as an example of the data display) are provided as well. (PDF 179 kb)

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Emilsson, V., Thorleifsson, G., Zhang, B. et al. Genetics of gene expression and its effect on disease.Nature 452, 423–428 (2008). https://doi.org/10.1038/nature06758

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