Breeding schemes for the implementation of genomic selection in wheat ( Triticum spp . )

Bassi, Filippo M.; Bentley, Alison R.; Charmet, Gilles; Ortíz, Rodomiro; Crossa, J.

doi:10.1016/j.plantsci.2015.08.021

Cited by 319 publications

(272 citation statements)

References 79 publications

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“…These resources have generated interest in developing models predicting breeding values for tomato improvement (Duangjit et al 2016;Hernán-dez-Bautista et al 2016;Yamamoto et al 2016a, b). The potential for genomic selection has been investigated in a number of grain crops (Barabaschi et al 2016;Bassi et al 2016). Adoption of these approaches for vegetable crops has lagged for a number of reasons including small population sizes dictated by working with perishable crops, the lack of a tradition for collecting objective data, and the need to balance selection for a number of traits.…”

Section: Discussionmentioning

confidence: 99%

The use of historical datasets to develop multi-trait selection models in processing tomato

Liabeuf

Francis

2017

Euphytica

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Multi-trait indices (MTI) weigh traits based on their importance to facilitate selection in plant and animal improvement. In animal breeding, economic values are used to develop MTIs. For vegetables, economic data valuing traits are rarely available. We posit that varieties with traits valued by growers and processors achieve higher market share and longer life span. Our objective was to develop MTIs predicting success of tomato varieties. Historical data for the California processing tomato industry from 1992 to 2013 provided measurements for yield, soluble solids (Brix), color, pH, market share, and life span for 258 varieties. We used random models to estimate best linear unbiased predictors (BLUPs) for phenotypic traits of each variety, and evaluated trends over time. Yield has been increasing from 2006, while Brix stayed constant. Because yield and Brix are negatively correlated, this trend suggests that Brix influenced selection. The average number of resistances reported in varieties ranking in the top ten increased from 2 to 4.5 between 1992 and 2013. MTIs predicting success from phenotypic traits were developed with general linear models and tested using leave-one-out cross validation. MTIs weighing yield, Brix, pH and color were significantly correlated to success metrics and selected a significantly higher proportion of successful varieties relative to random sampling. The index multiplying yield and brix, suggested in the literature, was not significantly correlated with variety success. The MTIs suggested that fruit quality had less of an influence on variety success than yield. The MTIs developed could help improve gain under selection for quality traits in addition to yield.

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Section: Discussionmentioning

confidence: 99%

The use of historical datasets to develop multi-trait selection models in processing tomato

Liabeuf

Francis

2017

Euphytica

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“…Marker-assisted selection is used extensively in breeding programmes to monitor genomic loci that are linked to markers in breeding pedigrees and to assemble combinations of loci 76 . An approach called genomic selection is also being used to accelerate breeding programmes 77,78 . In this method, a test population that represents the genetic diversity of a larger breeding population is thoroughly genotyped and phenotyped and a breeding value that predicts phenotypic performance on the basis of marker frequencies is assigned.…”

Section: Systems For Crop Breedingmentioning

confidence: 99%

“…4) during breeding can be followed using sets of genetic markers that define the haplotypes under selection. To assemble multiple haplotypes, large numbers of F 2 progeny (more than 10,000) will need to be screened 77 , with several markers that span each haplotype for phasing and for selecting desired variants of each haplotype. Because current marker technologies are too expensive for use in this approach, innovative methods will need to be developed.…”

Section: Crop Breeding As a Dna-assembly Problemmentioning

confidence: 99%

Genomic innovation for crop improvement

Bevan

Uauy

Wulff

et al. 2017

Nature

341

240

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Crop production needs to increase to secure future food supplies, while reducing its impact on ecosystems. Detailed characterization of plant genome structure and genetic diversity is crucial for meeting these challenges. Advances in genome sequencing and assembly are being used to access to the large and complex genomes of crops and their wild relatives. Sequencing of wild crop relatives is identifying a wide spectrum of genetic variation, permitting the association of genetic diversity with diverse agronomic phenotypes. In combination with improved and automated phenotyping assays and functional genomic studies, genomics is providing new foundations for crop-breeding systems. In the twentieth century, famines caused by political crises, mismanagement of food production or genocide killed an estimated 70 million people and were second only to war as the greatest manmade cause of death 1 . The father of the Green Revolution, Norman Borlaug, summarized the importance of crops: "Without food, people perish, social and political organizations disintegrate, and civilizations collapse. " For thousands of years, people have dedicated considerable resources to securing food supplies; for example, grain supplies to ancient Rome were secured through an extensive network of long-distance transport, and the distribution of grain was coordinated and subsidized by the cura annonae ('care for the grain supply'), an important figure who contributed to the maintenance of political unity and power.During the past 10,000 years, a period known as the Holocene, Earth's environment has been unusually stable. This probably facilitated the domestication of crops from wild species, resulting in steadily improved yields and adaptation to new agricultural areas. The production of food is now carried out on a vast scale, with 38% of Earth's surface dedicated to agriculture 2 . This increased production is having a pervasive influence on ecosystems worldwide: nitrogen production for agriculture accounts for 1.2% of global energy consumption 3 ; photosynthesis can no longer maintain stable levels of atmospheric carbon dioxide; and food production consumes around 70% of freshwater supplies. The modelling of crop responses to increases in temperature predicts that there will be a considerable reduction in the yield of rice, an important crop around the world 4 . Climate change could also alter the dynamics of crop pathogenic agents by altering the range of vectors and by compromising the immune response of crops 5 .Crop production must therefore adapt to more variable environments and the substantial impact it has on the environment needs to be reduced. Productivity must also increase at a much greater rate than in the past to meet the needs of Earth's growing population 6 . Genetic improvements in crop performance continue to be crucial for increasing crop productivity, but current rates of improvement are unable to meet the demands of sustainability and food security 7 . Plant genomics has a central role in the improvement of crops, includi...

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“…This is especially important when breeding for quantitative traits (QT), such as yield and drought tolerance, as these traits usually show continuous phenotype variation due to their polygenic inheritance and environmental influence and, thus, need to be repeatedly measured during the life cycle of a plant in multi-environmental conditions (Bassi et al 2016;Desta and Rodomiro 2014;Cobb et al 2013). …”

Section: Introductionmentioning

confidence: 99%

The adoption of automated phenotyping by plant breeders

2018

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Phenomics or automated phenotyping (AP) is an emerging approach, identified as a priority for future crop breeding research. This approach promises to provide accurate, precise, fast, large-scale, and accumulated phenotyping data which when integrated with corresponding genomic and environmental data is expected to trigger a great leap forward in plant breeding. However, despite promising applications, AP adoption in plant breeding is still in its infancy. It is unclear to many plant breeders how or if much of the enormous volume, diversity, and velocity of imaging and remote-sensing data generated by AP is going to be usefully integrated into breeding programs. This paper develops an economical model of heterogeneous breeders' decisionmaking to examine adoption decisions regarding whether to adopt AP or continue using conventional phenotyping. The results of this model indicate that many interlocking factors, including genetic gain/expected return, variable and sunk costs, subsequent rate of technology improvement, and breeders' level of aversion to AP, are at work as breeders determine whether to adopt AP. This study also provides a numerical example to show the impact of breeders' aversion toward the adoption of a new technology (e.g., AP) on the expected return generated from breeding a new wheat variety.

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Breeding schemes for the implementation of genomic selection in wheat ( Triticum spp . )

Cited by 319 publications

References 79 publications

The use of historical datasets to develop multi-trait selection models in processing tomato

The use of historical datasets to develop multi-trait selection models in processing tomato

Genomic innovation for crop improvement

The adoption of automated phenotyping by plant breeders

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