Genomic selection for traits of economic importance in sheep : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Animal Breeding and Genetics at Massey University, Manawatū, New Zealand
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Date
2014
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Massey University
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Abstract
The main objective of this thesis was to analyse the inclusion of genomic information
of production traits into a multitrait sheep breeding programme evaluated for 20 years
using deterministic and stochastic simulation models. The breeding objective was to
reduce faecal egg score (FES), decrease yearling weight (YW) and increase 160 days
lamb carcass weight (CW). The selection criteria included 160 days live weight
(instead of CW) plus YW and FES. The first study developed a stochastic model
selecting animals based on their individual breeding values estimated using best linear
unbiased predictor (BLUP) procedure with a multitrait animal model. The model was
validated using a deterministic multitrait selection index; obtaining similar prediction
responses for breeding objective and selection criteria traits. The second study
deterministically evaluated the inclusion of genomic information explaining different
proportions of CW and YW genetic variances into a selection index. Under the same
selection scheme a selection index having only genomic information obtained lower
accuracies and genetic gains compared to a selection index considering phenotypic
information. If shorter generation intervals are implemented, a selection index
including phenotypic and genomic information explaining low proportions of the trait's
genetic variance could achieve higher genetic and economic gains. The third study
evaluated genetic responses of a stochastically modelled breeding flock selecting ewes
based on BLUP estimated breeding values and selecting rams based on genomic
breeding values (GBV) for CW. The fourth study evaluated accuracy of prediction of
CW GBV using the same simulated model. Carcass weight GBVs were calculated in a
validation population using single nucleotide polymorphism (SNP) effects from a
training population. The further apart the genetic relationship between these two
populations, lower the GBV accuracy. Resultant accuracies depended on the
proportion of total genetic variance explained by genomic information and also the
variance accounted by each SNP, therefore an appropriate GBV estimating method has
to be chosen to achieve accuracies as high as possible. Stochastic models proved to be
more versatile for managing data, also showing variation of the genetic responses. In
contrast, deterministic models were faster regarding computer processing times. The
study proved that a breeding programme combining GBV and BLUP estimated
breeding values can increase genetic responses by selecting animals at early stages of
life.
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Keywords
Sheep breeding, Sheep genetics, Sheep breeding models, Livestock simulation models