Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/19756
Title: Use of Genetic Polymorphisms to Assess the Genetic Structure and Breed Composition of Crossbred Animals
Contributor(s): Weerasinghe, Shalanee (author); Gibson, John  (supervisor); Gondro, Cedric  (supervisor)orcid ; Jeyaruban, Gilbert (supervisor)
Conferred Date: 2016
Copyright Date: 2015
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/19756
Abstract: This thesis explores the accuracy of methods to estimate the breed composition of crossbred animals which have unknown pedigree. Herein I present the use of SNP technologies to estimate the breed composition of small-holder crossbred dairy cattle in developing countries for the first time. Before this could be done there was a need to determine: what are the accuracies of different methods for estimating breed composition? The genetic structure of animals, the design of reference populations, the number of SNP markers and the model selected has possible consequences for estimation of breed composition. Once the effect of the above factors on the accuracy of estimation of breed composition is identified, it is possible to estimate with confidence the breed composition of crossbred animals that have no recorded pedigree. The overall aim of this thesis was to investigate the use of high-density SNP data to understand the livestock breed's population structure and estimate the breed composition of crossbred animals.
Publication Type: Thesis Doctoral
Fields of Research (FoR) 2008: 080308 Programming Languages
070201 Animal Breeding
Fields of Research (FoR) 2020: 461204 Programming languages
300305 Animal reproduction and breeding
Socio-Economic Objective (SEO) 2008: 839899 Environmentally Sustainable Animal Production not elsewhere classified
830308 Pigs
830309 Poultry
Socio-Economic Objective (SEO) 2020: 100410 Pigs
100411 Poultry
Rights Statement: Copyright 2015 - Shalanee Weerasinghe
HERDC Category Description: T2 Thesis - Doctorate by Research
Appears in Collections:Thesis Doctoral

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