Hepatic Gene Expression Profiling to Predict Future Lactation Performance in Dairy Cattle

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Hepatic Gene Expression Profiling to Predict Future Lactation Performance in Dairy Cattle

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dc.contributor.advisor Cant, John
dc.contributor.author Doelman, John
dc.date 2011-10-04
dc.date.accessioned 2011-10-07T13:17:18Z
dc.date.available 2011-10-07T13:17:18Z
dc.date.issued 2011-10-07
dc.identifier.uri http://hdl.handle.net/10214/3062
dc.description.abstract An experiment was conducted to obtain a hepatic gene expression dataset from postpubertal dairy heifers that could be fit to a computational model capable of predicting future lactation performance values. The initial animal experiment was conducted to characterize the hepatic transcriptional response to 24-hour total feed withdrawal in one-hundred and two postpubertal Holstein dairy heifers using an 8329-gene oligonucleotide microarray in a randomized block design. Plasma concentration of non-esterified fatty acids was significantly higher, while levels of beta-hydroxybutyrate, triacylglycerol, and glucose were significantly lower with the 24-hour total feed withdrawal. In total, 505 differentially expressed genes were identified and microarray results were confirmed by real-time PCR. Upregulation of key gluconeogenic genes occurred despite diminished dietary substrate and lower hepatic glucose synthesis. Downregulation of ketogenic genes was contrary to the non-ruminant response to feed withdrawal, but was consistent with a lower ruminal supply of short-chain fatty acids as precursors. Following the microarray experiment, the first series of regression analyses was employed to identify relationships between gene expression signal and lactation performance measurements taken over the first lactation of 81 of the subjects from the original study. Regression models were evaluated using mean square prediction error (MSPE) and concordance correlation coefficient (CCC) analysis. The strongest validated stepwise regression models were constructed for milk protein percentage (r = 0.04) and lactation persistency (r = 0.09). To determine if another type of regression analysis would better predict lactation performance, partial least squares (PLS) regression analysis was then applied. Selection of gene expression data was based on an assessment of the linear dependence of all genes in normalized datasets for 81 subjects against 18 dairy herd index (DHI) variables using Pearson correlation analysis. Results were distributed into two lists based on correlation coefficient. Each gene expression dataset was used to construct PLS models for the purpose of predicting lactation performance. The strongest predictive models were generated for protein percentage (r = 0.46), 305-d milk yield (r = 0.44), and 305-d protein yield (r = 0.47). These results demonstrate the suitability of using hepatic gene expression in young animals to quantitatively predict future lactation performance. en_US
dc.description.sponsorship Ontario Centre for Agricultural Genomics, NSERC Canada, and the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA) en_US
dc.language.iso en en_US
dc.subject microarray en_US
dc.subject gene expression en_US
dc.subject dairy cattle en_US
dc.subject lactation en_US
dc.subject partial least squares en_US
dc.subject stepwise regression en_US
dc.subject multiple linear regression en_US
dc.title Hepatic Gene Expression Profiling to Predict Future Lactation Performance in Dairy Cattle en_US
dc.type Thesis en_US
dc.degree.programme Animal and Poultry Science en_US
dc.degree.name Doctor of Philosophy en_US
dc.degree.department Department of Animal and Poultry Science en_US


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