Study of genetic by environmental interactions at transcriptomic and genomic levels

• Main Author: Serao, Nicola Vergara Lopes
• Format: Dissertation
• Language: English
• Published: ProQuest Dissertations & Theses 01-01-2012
• Subjects: Animal sciences; Genetics; Statistics
• ISBN: 132113827X; 9781321138276

In the first study, general, breed- and diet-dependent associations for feed efficiency were identified using SNPs and haplotypes. The traits evaluated were: the two-step feed efficiency indicators residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG), and two complementary one-step indicators of feed efficiency, efficiency of intake (EI) and efficiency of gain (EG). These two novel indicators were developed to account for the total variation removed in the one-step indicators. In addition, a multi-SNP model was developed to assess the predictive power of several SNPs. Network and functional analyses of genes associated with feed efficiency aided in the interpretation of the results. Thirty-one, 40, and 25 SNPs (P-value < 0.0001), and six, ten, and nine haplotypes (P-value < 0.001) were significantly associated with feed efficiency on a general, breed-dependent, and diet-dependent manners, respectively. The associations of 17 SNPs and 7 haplotypes were confirmed (P-value < 0.05) on the validation data set. Overlapping of 20 SNPs and six haplotype associations between RFI and EI, and five SNPs and one haplotype associations between RADG and EG, confirmed the complementary value of the one and two-step indicators. A total of 89 SNPs were included (P-value < 0.0001) in the multi-SNP models, and offered a precise prediction of the five feed efficiency indicators. Thirteen molecular functions and six biological processes were identified (P-value < 0.001) in the functional analysis, including ion channel activity, nucleotide binding, and passive transmembrane transporter activity. These Gene Ontology categories were overrepresented among the genes harboring SNPs associated with feed efficiency. The breed- and diet-dependent associations between SNPs and feed efficiency suggest that further refinement of variant panels require the consideration of the breed and management practices. To conclude, the unique genomic variants associated with the one- and two-step indicators suggest that both types of indicators offer complementary description of feed efficiency. In the second study, the feed efficiency components of average daily gain (ADG) and dry matter intake (DMI) adjusted for the maintenance requirements were used for SNP associations. Univariate and bivariate analyses were performed in order to assess feed efficiency in the training population. As in the first study, a multi-SNP model was developed, as well as functional and network analyses were performed. The bivariate model identified 11 significant associations (P-value < 0.0001), whereas the univariate analyses of ADG and DMI resulted in eight and nine associations, respectively. Of these, six SNPs were confirmed in the validation data set. The final multi-SNP model included seven, nine, and eight SNPs for the bivariate, and univariate ADG and DMI analyses, respectively. These models showed low drop in the model adequacy in the validation data set, amounting for 19.4, 11.68, and 7.21% compared to the training data set, respectively. Six Gene Ontology categories were (P-value < 0.001) identified for the SNPs associated (P-value < 0.001) in the bivariate model. These were all represented by molecular functions related to ion transport activity. The bivariate analysis of ADG and DMI helped in the identification of SNP that have beneficial associations with both components of feed efficiency and can be used for genome-enabled improvement of feed efficiency in feedlot beef cattle. These studies showed the importance of considering environmental factors, such as therapies and diets, interacting at transcriptomics and genomic levels in human and livestock research. These findings may have direct use in human health, through the development of gene-based therapies in GBM, and in beef cattle production, and by the incorporation of specific SNP panels in different production systems. (Abstract shortened by UMI.)