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United States Department of Agriculture

Agricultural Research Service

Research Project: GENOMICS AND ENGINEERING OF STRESS-TOLERANT MICROBES FOR LOWER COST PRODUCTION OF BIOFUELS AND BIOPRODUCTS

Location: Crop Bioprotection Research

Title: Gene expression profiling for genetic merit in dairy cattle

Authors
item Rabel, Chanaka - UNIV. IL, CHAMPAIGN, IL
item Everts-Vam Der Womd, Annelie - UNIV. IL, CHAMPAIGN, IL
item Everts, Robin - UNIV. IL, CHAMPAIGN, IL
item Band, Mark - UNIV. IL, CHAMPAIGN, IL
item Wallace, Richard - UNIV. IL, CHAMPAIGN, IL
item Liu, Zonglin
item Rodriguez-Zas, Sandra - UNIV. IL, CHAMPAIGN, IL
item Lewin, Harris - UNIV. IL, CHAMPAIGN, IL

Submitted to: Plant and Animal Genome VX Conference Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: January 27, 2007
Publication Date: January 13, 2007
Citation: Rabel, C., Everts-Vam Der Womd, A., Everts, R., Band, M.R., Wallace, R.L., Liu, Z., Rodriguez-Zas, S., Lewin, H.A. 2007. Gene expression profiling for genetic merit in dairy cattle [abstract]. Plant and Animal Genome VX Conference Abstracts.

Technical Abstract: Gene expression patterns have been shown to be a heritable trait in dairy cattle. Thus, the pattern of gene expression in many selected tissues may serve as a biomarker for genetic stature or physiological condition. Our laboratory has conducted a 5-year study on the use of gene expression patterns to predict genetic merit for milk production in dairy cattle. A microarray consisting of 13,257 oligonucleotides designed from cattle ESTs was used to identify differences in gene expression in leukocyte RNA collected from 19 age-matched Holstein-Friesian heifers grouped according to their predicted transmitting ability (PTA) for milk production. The records on their first lactation were subsequently obtained for 15/19 animals, and 305-2x-ME milk production (ME) was used to confirm the PTA predictions and also as a variable for analyzing the gene expression data in an independent analysis. Only one animal was ranked differently on the basis of ME data. A multivariate ANOVA was used to analyze the gene expression data. The high PTA group (N=10) had 1,363 genes that differed in their gene expression level (FDR corrected P< 0.3) as compared with the low PTA group (N=9), of which 144 were >two-fold different. When classified by ME, the overlap between the PTA and the ME gene lists was 61%. Our results provide an excellent set of candidate genes for milk yield and suggest that it is possible to create a "phenomic index" of gene expression values that can be used to predict genetic milk yield prior to a cow’s first lactation.

Last Modified: 7/31/2014