1:30 pm - 2:30 pm
Event details: A graduate exam seminar is a presentation of the student’s final research project for their degree.
This is an ALES PhD Final Exam Seminar by Jiyuan Li. This seminar is open to the general public to attend.
Application of Multi-Omics Tools to Study the Genetic Background of Economically Relevant Traits in Commercial Beef Production
Meeting ID: 987 5463 0024 | Passcode: 420170
PhD with Dr. Graham Plastow
The sustainability and profitability of beef cattle production are largely associated with feed efficiency, carcass merit, and resistance to infectious diseases. These traits are difficult or expensive to measure on individual animals, which makes them suitable for genomic application. Currently accuracies of genomic prediction (a method that could predict genetic merit of animals based on DNA markers) for these traits are relatively low, hindering their uptake in beef cattle. The transcriptome and metabolome are intermediate, molecular phenotypes lying between genomic and phenotypic levels, which could be used to provide a better understanding of the genetic background of traits. They may therefore contribute to the development of more effective genomic selection strategies to further enhance genomic selection in beef cattle. In this thesis, integrative analyses of multi-omics data were applied to give insights into these questions.
In the first study the genetic architecture of blood metabolites was evaluated. Eleven metabolites with heritability estimates ranging from 0.09 ± 0.15 to 0.36 ± 0.15 were found. Several regions were identified that explained a small proportion of heritable genetic variation (0.62% – 4.21%). These results provided evidence for genetic variation of blood metabolites in beef cattle, and baseline information for research into the utilization of plasma metabolites for genetic improvement of beef cattle. Secondly, multiple metabolites were found to be associated with feed efficiency and carcass merit traits. Combining the results of metabolome-genome-wide association analysis identified many significant SNPs and candidate genes associated with these traits. Functional SNPs and genes are recommended to be included in SNP panels to improve the accuracy of genomic evaluation and prediction. Additionally, candidate genes were subjected to functional enrichment analyses. Several significant biological processes and networks such as lipid metabolism were identified to be associated with these important traits, which could assist preselection or prioritizing of SNPs used in genomic prediction models. In general, the integrative analysis of genomic and metabolomic data sheds light on how genes affect phenotypes by modifying the synthesis or degradation of related metabolites and improves understanding of genetic influence on phenotypes.
Lastly, transcriptomic and genotypic data were analyzed to study the genetics of bovine respiratory disease (BRD), the most common and costly infectious disease of beef cattle in North America. BRD susceptibility showed a moderate heritability (0.43 ± 0.51) in feedlot cattle. Two significant SNPs were identified to be associated with BRD susceptibility and 101 genes which were mainly involved in inflammatory response were differentially expressed (DE) in BRD and non-BRD animals. A total of 420 cis-expression quantitative loci (cis-eQTLs) and 144 trans-eQTLs were associated with the expression of the DE genes. Investigations into the relationship between different omics levels, revealed effect of genotype on gene expression and their roles in the host immune responses and disease susceptibility. Transcriptomic biomarkers with high accuracy and reliability to predict BRD status were identified which could be used to help diagnose BRD in feedlots.
In conclusion, this multi-omics integrative analysis exhibits advantages in the interpretation of previous GWAS results, identification of functional SNP and genetic mechanisms as well as understanding of biological processes associated with expression of beef cattle traits which could enhance genomic prediction and disease diagnosis.