Genome‐level analysis of genetic regulation of liver gene expression networks†

Abstract

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 singlenucleotide 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. (HEPATOLOGY 2007;46:548-557.) T he maturation of gene expression technology has opened the door to the exploration of the genetics of gene expression. 1-3 Microarrays allow for the concurrent measurement of thousands of transcripts, with the resultant genomic data being increasingly used to improve the biological interpretation of data from mechanistic research. Phenotypic anchoring of observed phenotypes to gene expression changes has proven useful in uncovering the molecular mechanisms that lead to liver injury. 4,5 Such experiments connect the variation in the transcript expression to phenotypes. However, they do not lead to detailed gene expression networks in which the expression of 1 gene is found to control the expression of another.Recombinant inbred (RI) mice are created by the crossing of 2 parental strains followed by sib-mating for over 20 generations. 6 Strains created in this way have the advantage of being homozygous at almost every location along the genome. Each representative of an RI line will have limited phenotypic variation within that line, but the variation between lines is usually vast. RI panels are widely used to determine genotype-phenotype associations with quantitative trait locus (QTL) mapping techniques. 7 The relationships between the phenotypes and genotypes are calculated with a likelihood ratio statistic (LRS), which is a measure of the probability that a given genetic marker explains the variation in the phenotype. When mRNA levels are used as the phenotype, regions of the genome with a high LRS are likely to contain genes that control the expression of t...

Moderation of breastfeeding effects on the IQ by genetic variation in fatty acid metabolism

“…GDS2106) show that FADS1 gene expression correlates 0.78 with FADS2 gene expression. Similarly, in mouse BXD strains, FADS1 and FADS2 gene expression correlate 0.61 (55).…”

Peroxisomal L-bifunctional enzyme (Ehhadh) is essential for the production of medium-chain dicarboxylic acids

“…search for clues on the role of Ehhadh, we generated gene coexpression subnetworks using published liver microarray data. We selected three independent mouse experiments: an F 2 intercross between C57BL/6 and BTBR mouse strains in which all cases are homozygous for the obese mutation in the leptin gene (the B6BTBRF2 cross [ 25 ]), an intercross between C57BL/6 and DBA/2J (the BxD genetic reference population [ 26,27 ]), and an F 2 intercross with C57BL/6J x C3H/ HeJ on an ApoE null background (BHF2 [ 28 ]). These experiments make use of genetic variation between four inbred strains (C57BL/6, BTBR, DBA, and C3H) and thereby enable the study of the effect of this genetic variation on mutant backgrounds known to greatly infl uence metabolism (ApoE null and ob/ob backgrounds).…”

“…We selected liver microarray data from three independently generated panels of genetically heterogeneous mice, an F 2 intercross between C57BL/6 and BTBR mouse strains in which all cases are homozygous for the obese mutation in the leptin gene (the B6BTBRF2 cross; n = 41) ( 25 ), an intercross between C57BL/6 and DBA/2J (the BxD genetic reference population; n = 60 male mice) ( 26,27 ), and an F 2 intercross with C57BL/6J x C3H/HeJ on an ApoE null background (BHF2) ( 28 ). We used the (B6 x BTBR)F2-ob/ob Liver mRNA M430 RMA, UNC Agilent G4121A Liver Orig LOWESS Stanford (male mice), and UCLA BHF2 Liver Male mlratio datasets, respectively.…”

Sex-specific gene expression in the BXD mouse liver

“…Even though sex-independent polymorphic local and distant QTLs, including several loci that control expression of large numbers of genes, were identified with the eQTL approach in the BXD panel (19) and confirmed in an independent cohort of inbred strains (20), relatively little is known about the extent of genetic control of sex differences in liver gene expression networks in the mouse, or in other species. We hypothesize that sex differences in liver xenobiotic metabolizing functions are largely due to variation in the expression of steroid metabolizing enzymes.…”