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Summary
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 platform
The arrays were Agilent two color arrays.
About 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).
Citation
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