Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

Crossa, José; Pérez‐Rodríguez, Paulino; Cuevas, Jaime; Montesinos‐López, Osval A.; Jarquín, Diego; Campos, Gustavo de los; Burgueño, Juan; González-Camacho, Juan Manuel; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi P.; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K.

doi:10.1016/j.tplants.2017.08.011

Cited by 1,046 publications

(900 citation statements)

References 79 publications

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“…Contrary to these techniques, genomic selection (GS) uses all molecular markers for genomic prediction of performance of candidate genotypes. GS combines the molecular and phenotypic data to get the genomic estimated breeding values for individual genotypes (Crossa et al., ). Velu et al.…”

Section: Strategies For Zn Biofortification Of Maizementioning

confidence: 99%

Zinc biofortification of maize (Zea mays L.): Status and challenges

Maqbool¹,

Beshir²

2018

Plant Breeding

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Zn deficiency is one of the leading health problems in children and women of developing countries. Different interventions could be used to overcome malnutrition, but biofortification is most impactful, convenient, sustainable and acceptable intervention. Maize is one of the major crops grown and consumed in the regions with prevalent Zn malnutrition; therefore, this is suitable target for Zn biofortification. Zn biofortification of maize could be achieved through agronomic and genetic approaches. Discussion of agronomic approaches with genetic approaches is prerequisite because soils in developing countries are deficit of Zn and availability of Zn in soils is mandatory for estimating the genetic responses of maize genotypes through genetic approaches. Seed priming, foliar and soil applications are agronomic tools for biofortification, but solo and combined applications of these treatments have different effects on Zn enrichment. Genetic approaches include the increase of Zn bioavailability or increase of kernel Zn concentration. Zn bioavailability could be increased by reducing the anti‐nutritional factors or by increasing the bioavailability enhancers. Kernel Zn concentration could be improved through hybridization and selections, whereas genetically engineered attempts for improving Zn uptake from soil, loading in xylem, remobilization in grains and sequestration in endosperm can further improve the kernel Zn concentration. Key challenges associated with dissemination of Zn biofortified maize are also under discussion in this draft. Current review emphasized all of above‐mentioned contents to provide roadmap for the development of Zn biofortified maize genotypes to curb the global Zn malnutrition.

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Section: Strategies For Zn Biofortification Of Maizementioning

confidence: 99%

Zinc biofortification of maize (Zea mays L.): Status and challenges

Maqbool¹,

Beshir²

2018

Plant Breeding

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“…These values are still far from the estimated necessary annual gain of 2% in crop productivity that are required to achieve the food and bioresource demand for the projected global population in 2050 (Li et al, ). Genomics‐enabled breeding approaches in combination with precision phenotyping tools are expected to contribute substantially to enhance the prediction accuracy and to accelerate genetic gains by shortening breeding cycles (Crossa et al, ). Indeed, the use of precision phenotyping tools to obtain a detailed and accurate characterization of the genetic value of an accession can result in a higher selection accuracy and a higher gain per breeding cycle (Cobb, DeClerck, Greenberg, Clark, & McCouch, ).…”

Section: Introductionmentioning

confidence: 99%

Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

Borra‐Serrano

Swaef

Muylle

et al. 2019

Grass and Forage Science

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In perennial ryegrass breeding programmes, dry‐matter yield (DMY) of individual plots is monitored destructively at the different cuts or derived from non‐destructive canopy height measurements using devices like rising plate meters (RPM). These approaches both have constraints. Destructive sampling implies low temporal resolution, restraining the study of dry‐matter accumulation rates, while RPM measurements are influenced by the canopy structure and limit intra‐field variability identification. We present a phenotyping methodology, based on the use of an affordable RGB camera mounted on an unmanned aerial vehicle (UAV), to monitor the spatial and temporal evolution of canopy height and to estimate DMY. Weekly flights were carried out from April to October above a field comprising a diverse set of accessions. To test the capacity of UAV imagery to estimate canopy height, 8 ground control points and 28 artificial height references were placed at different locations. Accurate flights with an RMSE as low as 0.94 cm were achieved. In addition, canopy height was recorded using an RPM and destructive biomass samples were collected. Different models (linear, multiple linear, principal components, partial least squares regression and random forest) were used to predict DMY, and their performance was evaluated. The best estimations were obtained by combining variables including canopy height, vegetation indices and environmental data in a multiple linear regression (R2 = .81). All models built using UAV data obtained a lower RMSE than the one using RPM data. The approach presented is a possibility for breeders to incorporate new information in their selection process.

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“…Meanwhile, in GS-assisted breeding, all molecular markers are used to predict the BV of the candidates for selection in a population that has been genotyped but not phenotyped (Meuwissen et al , 2001). The main advantages of GS over phenotypic and pedigree-based selection methods are that GS reduces the cost per cycle and increases time efficiency for variety development by shortening the breeding cycle (Crossa et al , 2017). …”

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confidence: 99%

A Bayesian Decision Theory Approach for Genomic Selection

Villar-Hernández

Pérez-Elizalde

Crossa

et al. 2018

G3 Genes|Genomes|Genetics

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Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle the selection problem based on genomic selection (GS) in single- and multi-trait settings. We proposed and tested three univariate loss functions (Kullback-Leibler, KL; Continuous Ranked Probability Score, CRPS; Linear-Linear loss, LinLin) and their corresponding multivariate generalizations (Kullback-Leibler, KL; Energy Score, EnergyS; and the Multivariate Asymmetric Loss Function, MALF). We derived and expressed all the loss functions in terms of heritability and tested them on a real wheat dataset for one cycle of selection and in a simulated selection program. The performance of each univariate loss function was compared with the standard method of selection (Std) that does not use loss functions. We compared the performance in terms of the selection response and the decrease in the population’s genetic variance during recurrent breeding cycles. Results suggest that it is possible to obtain better performance in a long-term breeding program using the single-trait scheme by selecting 30% of the best individuals in each cycle but not by selecting 10% of the best individuals. For the multi-trait approach, results show that the population mean for all traits under consideration had positive gains, even though two of the traits were negatively correlated. The corresponding population variances were not statistically different from the different loss function during the 10th selection cycle. Using the loss function should be a useful criterion when selecting the candidates for selection for the next breeding cycle.

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Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

Cited by 1,046 publications

References 79 publications

Zinc biofortification of maize (Zea mays L.): Status and challenges

Zinc biofortification of maize (Zea mays L.): Status and challenges

Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

A Bayesian Decision Theory Approach for Genomic Selection

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