Identification of functional genes for feed efficiency traits via transcriptome analyses to enhance the genomic prediction accuracy in beef cattle

• Author / Creator

  Robert Mukiibi

• Residual feed intake (RFI), a measure of feed efficiency, and its component traits including average daily gain (ADG), dry matter intake (DMI) and metabolic weight (MWT) are traits of great economic importance to the beef industry. The genetic improvement of these traits can improve the industry’s profitability as well as reduce the environmental footprints of beef production, thus leading to more sustainable beef production. However, these traits are difficult and expensive to measure on individual animals, making them good candidates for genomic prediction, a method that predicts animal’s genetic potential based on DNA markers. However, the accuracies of genomic prediction on these traits are general low in beef cattle and it is believed that incorporation of information on genetic mechanisms controlling these traits will improve the accuracy of genomic prediction. Therefore, in the current project we aimed to use RNAseq technology to identify candidate functional genes associated with RFI and its component traits in beef cattle. We further investigated the potential of improving genomic prediction accuracy of RFI and its component traits by utilizing the functional gene information.
  
  In the first study we analyzed whole liver transcriptome RNAseq data between six (n = 6) high and six (n = 6) low-RFI steer groups from three beef cattle breeds including Angus, Charolais and Kinsella Composite (KC) raised together. Similar analyses were performed in the second study between the steer groups with divergent component trait phenotypes from the three breeds. In total we identified 253, 252, 375 and 206 differentially expressed (DE) genes associated with RFI, ADG, DMI and MWT, respectively. For each trait the majority (82 – 88%) of the DE genes were breed specific. Functional enrichment analyses revealed that the identified DE genes are mainly involved in metabolism of lipids, carbohydrates, amino acids, molecular transport, cellular movement and cell-to-cell signaling.
  In the third study we explored differential micro RNA (miRNA) expression between six (n = 6) high and six (n = 6) low-RFI steers in the three beef breeds considered in studies I and II. Likewise, in the fourth study we investigated the association of miRNA expression with ADG, DMI and MWT in the three beef breeds. We identified 39 DE miRNAs associated with RFI, 36 DE miRNAs associated with ADG and 46 miRNAs were identified as associated with both DMI and MWT. Consistent with the DE genes findings in the first and second studies, DE miRNAs were also majorly breed specific. Additionally, DE miRNAs were predicted to target 55 – 76% of the identified DE genes which are involved in key molecular and cellular functions such as metabolism of lipids, carbohydrates, protein and amino acids as well as cell proliferation, and cell death and survival.
  
  To explore the possibility of improving genomic prediction accuracy through integration of transcriptomic information in the fifth study, we compiled a functional single nucleotide polymorphisms (SNP) panel from a total of 3,735 DE and miRNA target genes from this project and from literature (coding and miRNA precursor genes), and compared it to a commercial 50K SNP panel and a randomly selected (Random) SNP panel. Genomic prediction accuracies for RFI and its component traits were estimated for 7,372 beef animals from six beef breed populations including Angus, Charolais, KC, Elora, PG1 and TX. Results from this study indicated that generally the functional panel did not significantly yield higher genomic prediction accuracies than the other two panels. However, it had slightly higher accuracy for all the four traits for within Charolais evaluation.
  In conclusion, results from this PhD thesis project contribute to the understanding of genetic architectures of feed efficiency and its component traits. The results also demonstrated the potential to enhance genomic prediction accuracy through integration of functional information. However, further research on the utilization of functional information is required to enhance genomic prediction accuracy of feed efficiency and its related traits in beef cattle.

• Subjects / Keywords

  • MicroRNA
  • RNAseq
  • Genomic prediction
  • Feed efficiency
  • Transcriptomics
  • RFI

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-d13p-cq94

• License

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