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Quantitative genetic aspects of carcass composition, assessed by X-ray computer tomography, and meat quality traits in sheep

The overall aim of this thesis is to explore quantitative genetic aspects of carcass and meat quality traits in sheep and consider the utilization of such information in breeding programmes. The focus is to breed for improved lamb meat quality. This thesis investigated the use of <i>in vivo</i> composition traits to predict meat quality. Additionally, it investigated potential opportunities for genetically improving meat and carcass quality by using <i>in vivo </i>predictor traits and quantitative trait loci (QTL). Data for carcass composition and meat quality was collected over four years from a Blackface population. Within this population, a double backcrossed design created nine half-sib families, for QTL detection. Carcass composition was measured using <i>in vivo</i> computed tomography (CT) on equal number of males and females per year. This thesis provided considerable novel information of the genetic control of meat quality traits in sheep. In particular, three main contributions are (i) the quantification of heritabilities for a wide range of meat quality traits, (ii) genetic relationships between traits measured <i>in vivo</i>, such as CT-muscle density, and many of the meat quality traits, and (iii) QTL for a wide range of traits. Thus, selection to improve meat quality in sheep is possible, in principle, and this thesis provided tools which may pave the way towards implementation in practical breeding programmes. In particular, the use of CT-muscle density may be a means of making broad improvements in the perceived quality of sheep meat, and QTL that have been detected that, after independent validation, may allow improvement of specific meat quality attributes and alteration of the fatty acid profile.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:653210
Date January 2007
CreatorsKaramichou, Eleni
PublisherUniversity of Edinburgh
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://hdl.handle.net/1842/12104

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