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Data Set Group: UNC Agilent G4121A Liver Database (Jul04) modify this page

Data Set: UNC Agilent G4121A Liver LOWESS Stanford (Jan06) Both Sexes modify this page
GN Accession: GN105
GEO Series: No Geo series yet
Organism: Mouse (mm10)
Group: None
Tissue: Liver mRNA
Dataset Status: Public
Platforms: Agilent Mouse Toxicogenomics G4121A (GPL891)
Normalization: LOESS
Contact Information
Ivan Rusyn
The University of North Carolina at Chapel Hill
0031 Hooker Research Lab 135 Dauer Drive Campus Box 7431
Chapel Hill, NC 27599 USA
Tel. 919-843-2596
Download datasets and supplementary data files

Specifics of this Data Set:


Genome-level analysis of genetic regulation of liver gene expression networks

Gatti D, Maki A, Chesler EJ, Kirova R, Kosyk O, Lu L, Manly KF, Williams RW, Perkins A, Langston MA, Threadgill DW, Rusyn I

Hepatology. 2007 Aug;46(2):548-57

Source: Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA

The liver is the primary site for the metabolism of nutrients, drugs, and chemical agents. Although metabolic pathways are complex and tightly regulated, genetic variation among individuals, reflected in variations in gene expression levels, introduces complexity into research on liver disease. This study dissected genetic networks that control liver gene expression through the combination of large-scale quantitative mRNA expression analysis with genetic mapping in a reference population of BXD recombinant inbred mouse strains for which extensive single-nucleotide polymorphism, haplotype, and phenotypic data are publicly available. We profiled gene expression in livers of naive mice of both sexes from C57BL/6J, DBA/2J, B6D2F1, and 37 BXD strains using Agilent oligonucleotide microarrays. These data were used to map quantitative trait loci (QTLs) responsible for variations in the expression of about 19,000 transcripts. We identified polymorphic local and distant QTLs, including several loci that control the expression of large numbers of genes in liver, by comparing the physical transcript position with the location of the controlling QTL. CONCLUSION: The data are available through a public web-based resource (www.genenetwork.org) that allows custom data mining, identification of coregulated transcripts and correlated phenotypes, cross-tissue, and cross-species comparisons, as well as testing of a broad array of hypotheses.

About the cases used to generate this set of data:

About the tissue used to generate this set of data:

About the array platform:

The arrays were Agilent two color arrays.

About data values and data processing:

We used a mix of C57BL/6J RNA as the reference (liver, kidney, lung, brain and spleen). Each measurement is the ratio of sample intensity over the reference intensity. The data was normalized using a robust LOWESS smoothing method that adjusts for non-linearity between the two color channels (see Yang Yee Hwa et. al., Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation, Nucl. Acid Res., 2002). We then took the log2 of these ratios. A value of 1 indicates that the expression of that gene in the sample is twice that of the reference. Since the reference, includes liver, the range of values is more modest than some other scales of gene expression. (From Dan Gatti, Sept 2012).


Experiment Type:



Genome-level analysis of genetic regulation of liver gene expression networks. Gatti D, Maki A, Chesler EJ, Kirova R, Kosyk O, Lu L, Manly KF, Williams RW, Perkins A, Langston MA, Threadgill DW, Rusyn I. Hepatology. 2007 Aug;46(2):548-57. PMID: 17542012

Data source acknowledgment:

Study Id:

CITG Web services initiated January, 1994 as Portable Dictionary of the Mouse Genome; June 15, 2001 as WebQTL; and Jan 5, 2005 as GeneNetwork. This site is currently operated by Rob Williams, Pjotr Prins, Zachary Sloan, Arthur Centeno. Design and code by Pjotr Prins, Zach Sloan, Arthur Centeno, Danny Arends, Christian Fischer, Sam Ockman, Lei Yan, Xiaodong Zhou, Christian Fernandez, Ning Liu, Rudi Alberts, Elissa Chesler, Sujoy Roy, Evan G. Williams, Alexander G. Williams, Kenneth Manly, Jintao Wang, and Robert W. Williams, colleagues. Python Powered Registered with Nif
GeneNetwork support from:
  • The UT Center for Integrative and Translational Genomics
  • NIGMS Systems Genetics and Precision Medicine project (R01 GM123489, 2017-2021)
  • NIDA NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017-2022)
  • NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013-2018)
  • NIAAA Integrative Neuroscience Initiative on Alcoholism (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017)
  • NIDA, NIMH, and NIAAA (P20-DA 21131, 2001-2012)
  • NCI MMHCC (U01CA105417), NCRR, BIRN, (U24 RR021760)
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