Designing breeding programs in the genomic era
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Date
31/07/2021Item status
Restricted AccessEmbargo end date
31/07/2022Author
Powell, Owen
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Abstract
Increasing the rate of genetic gain of breeding programs is one route to achieve
sustainable increases in food production. Breeding has been responsible for ~50% of
the increases in agricultural productivity over the past 70 years. However, if we are to
sustainably feed a global population of 9 billion people in 2050, breeding programs
will need to double the rates of genetic gain that they deliver. Genomic selection is a
breeding technology that uses a prediction equation, based on estimated associations
between molecular markers and traits of interest, to estimate the genetic values of
individuals. Genomic selection makes it possible to directly improve three of the four
parameters of the breeder’s equation: (i) the generation interval, (ii) the selection
accuracy, and (iii) the selection intensity. Genomic selection can also overcome
historical scientific and infrastructural constraints that have limited the use of breeding
in different species and agricultural systems. Finally, genomic selection also provides
a platform to harness natural synergies between plant and animal breeding.
The aim of this thesis was to develop new breeding strategies using genomic
selection to increase the rates of genetic gain in both plant and animal breeding
programs.
Therefore, the first two research chapters of the thesis focused on the
deployment of genomic selection in low to middle-income country (LMIC)
smallholder dairy cattle genetic evaluations. Across a range of different breeding
strategies, genomic selection enabled accurate genetic evaluations. The use of genetic
markers was more powerful than pedigree information in capturing and utilising
genetic connectedness between smallholder herds. Further, modelling herd as a
random effect, in conjunction with genetic markers, enabled animals from very small
herds to be included in genetic evaluations.
However, the distribution of cattle across different LMIC smallholder dairy
herds is complex. Therefore, further simulations were undertaken of three breeding
scenarios that varied the distribution of sires and their use across different villages and
herds. Spatial modelling increased the accuracy of genetic evaluations by improving
the partitioning of the genetic and environmental effects. Therefore, the benefit of
spatial modelling increased with increased confounding between genetic and
environmental effects. Finally, spatial modelling provided larger increases in the
accuracy of genomic evaluations compared to pedigree-based genetic evaluations.
The third and fourth research chapters of the thesis focused on the deployment
of genomic selection in hybrid crop breeding programs. Hybrid crop breeding
programs using a two-part strategy produced the most genetic gain. The two-part
strategy used outbred parents to shorten the generation interval of hybrid crop breeding
programs. However, the shorter generation interval caused a higher loss of genetic
variance per unit of genetic gain. Therefore, a maximum avoidance of inbreeding
crossing scheme was required to managed genetic variation over time and increase
long-term genetic gain.
The complexity of hybrid crop breeding programs enables multiple ways
to reallocate resources to implement genomic selection, and the two-part strategy
offers even further opportunities. Therefore, further simulations were undertaken to
quantify the impact of different resource reallocation strategies on the genetic gain of
hybrid crop breeding programs. Two conclusions can be drawn from the results: (i)
under a fixed budget, the number of crosses per cycle should be prioritised over the
number of selection candidates per cross to maximise long-term genetic gain, and (ii)
genomic selection with large levels of resources can mitigate the trade-off between the
number of individuals and the number of field measurements per individual in hybrid
crop breeding programs.
The results from this thesis show that restructuring current breeding strategies
to exploit new technology can generate large increases in the rates of genetic gain in
both plant and animal breeding programs. Therefore, strategies outlined in this thesis
can provide a blueprint for plant and livestock breeding programs to help meet the
global demand for food of 9 billion people in 2050.