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* Cooperative Research Centre for Cattle and Beef Quality, CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, Queensland 4067, Australia;
and
Food Science Australia, Tingalpa DC, Queensland D 4173, Australia
2 Correspondence—phone: +61-7-3214-2392; fax: +61-7-3214-2881; E-mail:
Tony.Reverter-Gomez{at}csiro.au.
The objective of this study is to explore aspects of the statistical analysis of gene expression response at the muscle tissue level to varying levels of energy and protein in the diet. Eleven Brahman and Brahman composite steers (weighing 302 ± 9.8 kg, on average) were allocated randomly into high- (HIGH), medium- (MED), and low- (LOW) quality forage diets for 27 d. After this period, a biopsy of the longissimus dorsi muscle was taken from each animal and total RNA was extracted to generate the labeled target for microarray experimentation. These targets were hybridized to a complementary DNA (cDNA) microarray of 9,274 probes from cattle muscle and subcutaneous fat cDNA libraries. After edits, 151,904 expression intensity levels of 4,747 genes were analyzed. Emphasis was given to the choice of power transformation of the intensity channel readings and to the consistency of readings within each diet quality group. The statistical approach to isolate differentially expressed genes was based on model-based clustering via a mixture of normal distributions estimated through maximal likelihood. The base-2 logarithm was found to be the optimal power transformation to normalize gene intensity levels. A two-sample t-statistic was defined as a measure of possible differential expression. For each of the three diet contrasts, HIGH vs. LOW, HIGH vs. MED, and MED vs. LOW, three clusters were found, two of which contained more than 94% genes with almost no altered gene expression levels, whereas the third cluster contained the remaining genes with a differential expression. Results from the HIGH vs. LOW contrast identified 27 genes with a greater than 95% posterior probability of belonging to the cluster of differentially expressed genes.
Key Words: Beef • Complementary DNA • Gene Expression • Maximum Likelihood • Statistical Analysis
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