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Application of genomic tools for Irish pasture improvement
AROJJU, SAI KRISHNA
The conventional way to improve populations in perennial ryegrass (Lolium perenne), the most important forage grass in Ireland, is through recurrent selection. However, despite breeding for nearly a century, the rate of genetic improvement in perennial ryegrass is still in its infancy compared to cereals. This thesis investigates the use of molecular markers and genomic information to accelerate genetic gains for key traits in the forage species perennial ryegrass. Genomic prediction is one approach that shows promise to accelerate the rate of genetic gain in forage breeding. In genomic prediction all markers are simultaneously used to estimate allelic effects without significant testing. Genomic prediction was evaluated in this thesis in two scenarios, (i) for traits evaluated on diploid perennial ryegrass spaced plants, and (ii) traits evaluated using progeny based phenotyping in tetraploid perennial ryegrass. Genomic predictive ability for crown rust resistance (Puccinia coronata f. sp lolli) evaluated on spaced plants was high. However, much of the predictive ability resulted from markers capturing genetic relationship among families. Variable selection methods, such as the variable importance measure and GWAS were used to identify a small panel of markers that were able to achieve higher predictive ability than the same number of randomly selected markers. These markers were identified after correction for population structure and are likely in LD with QTL for crown rust resistance rather than simply capturing relationship among families. Accuracy due to genetic relationships will decay rapidly over generations whereas accuracy due to LD will persist, which is advantageous for practical breeding applications. Genomic prediction models were also developed for forage yield in tetraploid perennial ryegrass, which was evaluated as progeny means. Forage yield is by far the most important trait for perennial ryegrass. Genomic prediction for both yield under grazing (calculated as economic value of a plot) and yield under conservation management in tetraploid perennial ryegrass was promising. Using genomic prediction multiple cycles of indirect selection with DNA markers can be completed in the same time it takes to complete a single cycle of conventional selection. This will result in increased genetic gains.
Keyword(s): Genomic Selection
Publication Date:
2018
Type: Doctoral thesis
Peer-Reviewed: Yes
Language(s): English
Institution: Trinity College Dublin
Funder(s): Teagasc
Citation(s): AROJJU, SAI KRISHNA KRISHNA, Application of genomic tools for Irish pasture improvement, Trinity College Dublin.School of Natural Sciences.BOTANY, 2018
Publisher(s): Trinity College Dublin. School of Natural Sciences. Discipline of Botany
Supervisor(s): Hodkinson, Trevor
First Indexed: 2018-04-07 06:10:05 Last Updated: 2018-04-07 06:10:05