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Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery
Wayne Powell and colleagues compare the different tools and approaches used by the plant breeding community versus the animal breeding community for crop and livestock improvement. They argue that the two disciplines can be united via adoption of genomic selection along with the exchange of resources and techniques between the two areas.
The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.
Journal Article
Plant Breeding with Genomic Selection: Gain per Unit Time and Cost
Advancements in genotyping are rapidly decreasing marker costs and increasing genome coverage. This is facilitating the use of marker-assisted selection (MAS) in plant breeding. Commonly employed MAS strategies, however, are not well suited for agronomically important complex traits, requiring extra time for field-based phenotyping to identify agronomically superior lines. Genomic selection (GS) is an emerging alternative to MAS that uses all marker information to calculate genomic estimated breeding values (GEBVs) for complex traits. Selections are made directly on GEBV without further phenotyping. We developed an analytical framework to (i) compare gains from MAS and GS for complex traits and (ii) provide a plant breeding context for interpreting results from studies on GEBV accuracy. We designed MAS and GS breeding strategies with equal budgets for a high-investment maize (Zea mays L.) program and a low-investment winter wheat (Triticum aestivum L.) program. Results indicate that GS can outperform MAS on a per-year basis even at low GEBV accuracies. Using a previously reported GEBV accuracy of 0.53 for net merit in dairy cattle, expected annual gain from GS exceeded that of MAS by about threefold for maize and twofold for winter wheat. We conclude that if moderate selection accuracies can be achieved, GS could dramatically accelerate genetic gain through its shorter breeding cycle.
Journal Article
Climate change and functional traits affect population dynamics of a long-lived seabird
1.Recent studies unravelled the effect of climate changes on populations through their impact on functional traits and demographic rates in terrestrial and freshwater ecosystems, but such understanding in marine ecosystems remains incomplete.2.Here, we evaluate the impact of the combined effects of climate and functional traits on population dynamics of a long-l ived migratory seabird breeding in the southern ocean: the black- browed albatross (Thalassarche melanophris, BBA). We address the following prospective question: “Of all the changes in the climate and functional traits, which would produce the biggest impact on the BBA population growth rate?”3.We develop a structured matrix population model that includes the effect of cli-mate and functional traits on the complete BBA life cycle. A detailed sensitivity analysis is conducted to understand the main pathway by which climate and func-tional trait changes affect the population growth rate.4.The population growth rate of BBA is driven by the combined effects of climate over various seasons and multiple functional traits with carry- over effects across seasons on demographic processes. Changes in sea surface temperature (SST) during late winter cause the biggest changes in the population growth rate, through their effect on juvenile survival. Adults appeared to respond to changes in winter climate conditions by adapting their migratory schedule rather than by modifying their at- sea foraging activity. However, the sensitivity of the population growth rate to SST affecting BBA migratory schedule is small. BBA foraging activ-ity during the pre- breeding period has the biggest impact on population growth rate among functional traits. Finally, changes in SST during the breeding season have little effect on the population growth rate.5.These results highlight the importance of early life histories and carry- over ef-fects of climate and functional traits on demographic rates across multiple sea-sons in population response to climate change. Robust conclusions about the roles of various phases of the life cycle and functional traits in population response to climate change rely on an understanding of the relationships of traits to demo-graphic rates across the complete life cycle.
Journal Article
The purebred Arabian horses of Iraq : myths and realities : the first study of Arabian horses from an Arabic and Islamic viewpoint
The history of Iraq's purebred Arabian Horses, told by a prominent Iraqi whose family has been breeding horses for generations.
Book
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers
Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker-based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes (“newly” developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confirmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefit greatly from using multienvironment GS models.
Journal Article
Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance
A substantial increase in grain yield potential is required, along with better use of water and fertilizer, to ensure food security and environmental protection in future decades. For improvements in photosynthetic capacity to result in additional wheat yield, extra assimilates must be partitioned to developing spikes and grains and/or potential grain weight increased to accommodate the extra assimilates. At the same time, improvement in dry matter partitioning to spikes should ensure that it does not increase stem or root lodging. It is therefore crucial that improvements in structural and reproductive aspects of growth accompany increases in photosynthesis to enhance the net agronomic benefits of genetic modifications. In this article, six complementary approaches are proposed, namely: (i) optimizing developmental pattern to maximize spike fertility and grain number, (ii) optimizing spike growth to maximize grain number and dry matter harvest index, (iii) improving spike fertility through desensitizing floret abortion to environmental cues, (iv) improving potential grain size and grain filling, and (v) improving lodging resistance. Since many of the traits tackled in these approaches interact strongly, an integrative modelling approach is also proposed, to (vi) identify any trade-offs between key traits, hence to define target ideotypes in quantitative terms. The potential for genetic dissection of key traits via quantitative trait loci analysis is discussed for the efficient deployment of existing variation in breeding programmes. These proposals should maximize returns in food production from investments in increased crop biomass by increasing spike fertility, grain number per unit area and harvest index whilst optimizing the trade-offs with potential grain weight and lodging resistance.
Journal Article