ArticleBMC genomics2007
Combining mouse mammary gland gene expression and comparative mapping for the identification of candidate genes for QTL of milk production traits in cattle.
Article in BMC genomics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 45 citations in OpenAlex.
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- Sequencing the transcriptome of milk production: milk trumps mammary tissue.BMC genomics · 2013Article
- Comparative transcriptome analyses reveal conserved and distinct mechanisms in ovine and bovine lactation.Functional & integrative genomics · 2013Article
- From QTL to QTN: candidate gene set approach and a case study in porcine IGF1-FoxO pathway.PloS one · 2013Article
- Genetic effects and correlations between production and fertility traits and their dependency on the lactation-stage in Holstein Friesians.BMC genetics · 2012Article
- Whole-genome resequencing of two elite sires for the detection of haplotypes under selection in dairy cattle.Proceedings of the National Academy of Sciences of the United States of America · 2012Article
- QTL and gene expression analyses identify genes affecting carcass weight and marbling on BTA14 in Hanwoo (Korean Cattle).Mammalian genome : official journal of the International Mammalian Genome Society · 2011Article
- Evolution of major milk proteins in Mus musculus and Mus spretus mouse species: a genoproteomic analysis.BMC genomics · 2011Article
- The bisecting GlcNAc on N-glycans inhibits growth factor signaling and retards mammary tumor progression.Cancer research · 2010Article
- Database of cattle candidate genes and genetic markers for milk production and mastitis.Animal genetics · 2009Article
- Genomewide analysis of secretory activation in mouse models.Journal of mammary gland biology and neoplasia · 2007Article
- Candidate gene identification approach: progress and challenges.International journal of biological sciences · 2007Review
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany studies have found segregating quantitative trait loci (QTL) for milk production traits in different dairy cattle populations. However, even for relatively large effects with a saturated marker map the confidence interval for QTL location by linkage analysis spans tens of map units, or hundreds of genes. Combining mapping and arraying has been suggested as an approach to identify candidate genes. Thus, gene expression analysis in the mammary gland of genes positioned in the confidence interval of the QTL can bridge the gap between fine mapping and quantitative trait nucleotide (QTN) determination.
resultsWe hybridized Affymetrix microarray (MG-U74v2), containing 12,488 murine probes, with RNA derived from mammary gland of virgin, pregnant, lactating and involuting C57BL/6J mice in a total of nine biological replicates. We combined microarray data from two additional studies that used the same design in mice with a total of 75 biological replicates. The same filtering and normalization was applied to each microarray data using GeneSpring software. Analysis of variance identified 249 differentially expressed probe sets common to the three experiments along the four developmental stages of puberty, pregnancy, lactation and involution. 212 genes were assigned to their bovine map positions through comparative mapping, and thus form a list of candidate genes for previously identified QTLs for milk production traits. A total of 82 of the genes showed mammary gland-specific expression with at least 3-fold expression over the median representing all tissues tested in GeneAtlas.
conclusionThis work presents a web tool for candidate genes for QTL (cgQTL) that allows navigation between the map of bovine milk production QTL, potential candidate genes and their level of expression in mammary gland arrays and in GeneAtlas. Three out of four confirmed genes that affect QTL in livestock (ABCG2, DGAT1, GDF8, IGF2) were over expressed in the target organ. Thus, cgQTL can be used to determine priority of candidate genes for QTN analysis based on differential expression in the target organ.
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