The objective of this thesis was to investigate the longitudinal aspect of the genetics of egg laying in two heavy female turkeys and explore whether considering time features of laying may improve selection efficiency. The dataset consisted of records from two commercial lines, although only one had longitudinal data. The genetic correlation between body weight and total egg number was estimated to be -0.7±0.1 and -0.5±0.1 in the two lines studied. Both estimates were highly negative and larger in magnitude than in traditional and lighter lines, suggesting that the continuous selection for growth hinders genetic progress for egg production. Heritability estimates for body weight were high (around 0.4), while being lower for total egg number (around 0.2 for egg records on which Box-Cox transformation has been applied to reduce their deviation from normality). Since heavier birds tended to lay fewer eggs within a specific period, this implied reduced rates of lay. In order to explore this consequence of the selection for antagonistic traits, a time-to-event trait was formulated that corresponded to the days required for a hen to lay 82 eggs (the average egg production in the dataset). It was shown that the Weibull distribution could satisfactorily serves the baseline function under a survival analysis context. So, a frailty model was constructed to perform a genetic analysis of the time trait and it was found that its heritability estimate was between that for the transformed and untransformed total egg number. Random regression (RR) models were also considered for the longitudinal analysis of egg production. The detailed covariance structure obtained on a longitudinal basis can be used to target more effectively the selection pressure on the most informative stages of laying and thus, to maximise the output of breeding programmes.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:653557 |
| Date | January 2007 |
| Creators | Kranis, Andreas |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/11012 |
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