Across breed genomic evaluation in cattle
View/ Open
Brown2017.docx (9.065Mb)
Date
08/07/2017Author
Brown, Alexandra
Metadata
Abstract
Genomic evaluation techniques have been a huge success in the dairy cattle industry,
as they allow accurate enough estimation of breeding values at a young age to allow
selection decisions to be made at an earlier stage, thereby increasing the rate of
genetic progress per annum. The success of genomic selection techniques relies on
the existence of linkage disequilibrium (LD) between markers and quantitative trait
loci (QTL) across the population of interest; LD persists across larger distances
within breeds than across breeds. Therefore, most success so far has been for
selection within breeds, but the industry is keen for “across breed” evaluations to be
developed, both in a multi-breed scenario which would allow evaluations for breeds
that are numerically too small to carry out evaluations within breeds, and also for the
evaluation of crossbred animals.
This thesis investigates the potential for applying genomic selection techniques in
both the multi-breed and crossbred scenarios. Chapter 2 examines the potential for a
multi-breed reference population to improve the accuracy of genomic evaluation for
a numerically small breed, for a range of production and non-production traits. The
results provide evidence that forming a multi-breed reference population for two
closely related breeds (Holstein and Friesian) results in a higher accuracy of GEBVs
for the smaller breed, particularly when more phenotypic records are added via the
single-step GBLUP method, and when a higher density SNP chip is used. Chapter 3
examines the crossbred scenario, whereby GEBVs are calculated for crossbred
individuals based on a crossbred reference population. The population used for
analysis was a highly crossbred African population, and GEBVs were calculated for
three groups of animals chosen according to whether they had a high or low
proportion of imported dairy genetics. Accuracy of prediction was higher than
expected, and provided proof of concept for applying genomic selection techniques
in crossbred African cattle populations. Chapter 4 investigates the potential for using
novel SNPs derived from sequence data in order to estimate genomic relationships
across cattle breeds, deploying data from two closely related breeds, Fleckvieh and
Simmental, and a further distant European breed, the Brown Swiss. Novel SNPs
were selected from sequence based on their putative impact on the genome, with
impacts being inferred by SNP annotation software snpEff. Results showed that
genomic relationships calculated using novel SNPs have a high correlation with
genomic relationships calculated using SNPs common to the Illumina BovineHD
SNP chip, though between-breed correlations were lower than those within breeds.
The results presented in this thesis demonstrate that utilising a multi-breed reference
population can improve the accuracy of prediction for a numerically small breed, and
that genomic prediction of highly crossbred individuals is also feasible. However,
differences between breeds and also types of crossbred animal suggest that no one
solution can be used for all across-breed evaluations, and further research will be
needed to allow commercial implementation in further populations.
The following license files are associated with this item: