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Cited by 921

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Mapping the Genetic Architecture of Gene Expression in Human Liver

Open access
Schadt, Eric E, Molony, Cliona, Chudin, Eugene, Hao, Ke, Yang, Xia, Lum, Pek Y, Kasarskis, Andrew, Zhang, Bin, Wang, Susanna, Suver, Christine, Zhu, Jun, Millstein, Joshua, Sieberts, Solveig, Lamb, John, GuhaThakurta, Debraj, Derry, Jonathan, Storey, John D, Avila-Campillo, Iliana, Kruger, Mark J, Johnson, Jason M, Rohl, Carol A, van Nas, Atila, Mehrabian, Margarete, Drake, Thomas A, Lusis, Aldons J, Smith, Ryan C, Guengerich, F. Peter, Strom, Stephen C, Schuetz, Erin, Rushmore, Thomas H, Ulrich, Roger·2008·PLoS Biology
DOI 10.1371/journal.pbio.0060107
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Abstract

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

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Cites

Show all 22 references

Research works printed but not linked: 47

  1. Edwards, 2005, Science, 308, 421
  2. Haines, 2005, Science, 308, 419
  3. Klein, 2005, Science, 308, 385
  4. Helgadottir, 2007, Science, 316, 1491
  5. McPherson, 2007, Science, 316, 1488
  6. Fellay, 2007, Science, 317, 944
  7. Grant, 2006, Nat Genet, 38, 320
  8. Sladek, 2007, Nature, 445, 881
  9. Herbert, 2006, Science, 312, 279
  10. Peacock, 1993, Neurology, 43, 1254
  11. Cheung, 2005, Nature, 437, 1365
  12. Monks, 2004, Am J Hum Genet, 75, 1094
  13. Morley, 2004, Nature, 430, 743
  14. Todd, 2007, Nat Genet, 39, 857
  15. Samani, 2007, N Engl J Med, 357, 443
  16. Kathiresan, 2008, Nat Genet, 40, 189
  17. Willer, 2008, Nat Genet, 40, 161
  18. Doss, 2005, Genome Res, 15, 681
  19. Dixon, 2007, Nat Genet, 39, 1202
  20. Emilsson, 2008, Nature, 452, 423
  21. Moffatt, 2007, Nature, 448, 470
  22. Myers, 2007, Nat Genet, 39, 1494
  23. Rieder, 2005, N Engl J Med, 352, 2285
  24. Schadt, 2005, Nat Genet, 37, 710
  25. Bleasby, 2006, Xenobiotica, 36, 963
  26. Raymond, 2004, J Biol Chem, 279, 46234
  27. Zhu, 2004, Cytogenet Genome Res, 105, 363
  28. Zhu, 2008, Nat Genet
  29. Zhu, 2007, PLoS Comput Biol, 3
  30. Helgadottir, 2004, Nat Genet, 36, 233
  31. Kubota, 1999, Mol Cell, 4, 597
  32. Mehrabian, 2005, Nat Genet, 37, 1224
  33. Chen, 2008, Nature, 452, 429
  34. Saxena, 2007, Science, 316, 1331
  35. Kiss, 2007, Clin Rev Allergy Immunol, 32, 37
  36. Ozcan, 2006, Science, 313, 1137
  37. Gregor, 2007, J Lipid Res, 48, 1905
  38. Nakatani, 2006, Diabet Med, 23, 216
  39. Gibson, 2006, Xenobiotica, 36, 857
  40. Yang, 2006, Genome Res, 16, 995
  41. He, 2003, Bioinformatics, 19, 956
  42. Hughes, 2000, Cell, 102, 109
  43. Roberts, 2000, Science, 287, 873
  44. Pritchard, 2000, Genetics, 155, 945
  45. Zou, 2005, J R Stat Soc, 76, 301
  46. Hughes, 2001, Nat Biotechnol, 19, 342
  47. Hao, 2008, PLoS Genet

Cited by

Metadata

Authors
Schadt, E. E., Molony, C., Chudin, E., Hao, K., Yang, X., Lum, P. Y., Kasarskis, A., Zhang, B., Wang, S., Suver, C., Zhu, J., Millstein, J., Sieberts, S., Lamb, J., GuhaThakurta, D., Derry, J., Storey, J. D., Avila-Campillo, I., Kruger, M. J., Johnson, J. M., Rohl, C. A., van Nas, A., Mehrabian, M., Drake, T. A., Lusis, A. J., Smith, R. C., Guengerich, F. P., Strom, S. C., Schuetz, E., Rushmore, T. H., & Ulrich, R.
Venue
PLoS Biology
Publisher
Public Library of Science (PLoS)
Published
2008-05-06
Volume
6
Issue
5
Pages
e107
ISSN
  • 1544-9173 (ISSN-L)
  • 1545-7885
DOI
10.1371/journal.pbio.0060107
OpenAlex
W2043842514
Cited by
921
References
22
FWCI
50.10
Type
journal article
Language
en
Open access
gold
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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