Submitted to: Journal of Animal Science Supplement
Publication Type: Abstract Only
Publication Acceptance Date: 4/3/2017
Publication Date: 8/10/2017
Citation: Keel, B.N., Zarek, C.M., Keele, J.W., Kuehn, L.A., Snelling, W.M., Oliver, W.T., Freetly, H.C., Lindholm-Perry, A.K. 2017. Meta-analysis of RNA-Seq data across cohorts in a multi-season feed efficiency study of crossbred beef steers accounts for biological and technical variability within season [abstract]. Journal of Animal Science Supplement. 95(Suppl4):98. doi:10.2527/asasann.2017/198.
Technical Abstract: High-throughput sequencing is often used for studies of the transcriptome, particularly for comparisons between experimental conditions. Due to sequencing costs, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. Moreover, validation of transcriptomic data is likely to suffer from poor reproducibility from study to study due to the large amount of variation from sources, such as breed and season. The major aim of this study was to identify genes differentially expressed in the muscle of beef cattle associated with gain and intake that will be robust across a large segment of the cattle industry, regardless of breed of origin, season, or year of study. To avoid differences in gene expression due to environment and breed of origin, crossbred steers representing 19 different breeds were selected from fall and spring seasons over 3 years. RNA sequencing (RNA-Seq) was performed on longissimus dorsi muscle from 80 steers (5 cohorts, each with 16 animals) selected with the greatest distance from the bivariate means of gain and feed intake within season. Initial analysis of data from all 80 steers identified a strong seasonal effect in differentially expressed genes (DEG). Although selection of animals in each season was performed using the same procedure, there was a clear segregation of gain and intake phenotypes between seasons. In order to estimate the overall difference in expression between phenotypes, differential expression analysis was performed independently for each season, and a P-value combination technique was employed to identify robust DEGs across the seasons. We identified a total of 148 DEGs for the main effect of gain, 1,738 DEGs for intake, and 59 DEGs for the gain x intake interaction. Moreover, nine of the genes associated with gain and twelve of the genes associated with intake shared the same gene expression directionality across all five groups of steers. The integration of data from multiple experiments (seasons) may enable extraction of deeper biological insights compared to what is achieved through analyzing a single experiment by allowing efficient elimination of false-positive findings pertaining to experimental and design conditions.