Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

Wartha, Cleiton Antônio; Lorenz, Aaron J.

doi:10.1590/1984-70332021v21sa28

Cited by 18 publications

(17 citation statements)

References 118 publications

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“…As the fields of genomics and data analytics substantially evolved over the past decade, the concept of genomic selection applied to phenotypic prediction revolutionized commercial and public breeding programs by allowing plant breeders to predict the phenotype of interest in untested genotypes ( Crossa et al, 2017 ; Vieira and Chen, 2021 ; Wartha and Lorenz, 2021 ). Genomic selection has covered multiple fronts of the breeder’s equation maximizing the genetic gain in a given breeding cycle.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, a large component of a breeding cycle is allocated to progeny selection and preliminary yield trials of which the main objective is to characterize the genetic diversity present in a population of interest by evaluating a large number of genotypes for yield and overall agronomic traits. Genomic selection rises as a statistically powerful solution generating predicted values for unobserved genotypes, allowing plant breeders to shorten the breeding cycle and significantly minimize the costs associated with extensive field trials ( Vieira and Chen, 2021 ; Wartha and Lorenz, 2021 ). Up to this date, however, the wide and large-scale implementation of genomic selection across plant breeding programs still faces challenges and drawbacks.…”

Section: Discussionmentioning

confidence: 99%

“…The concept of GS revolves around using the information of all molecular markers—large and small effects—to develop prediction models for the phenotype of interest. The major advantage of GS relies on the ability to predict the yield of genotypes to allow the identification and selection of the most promising individuals earlier in the breeding pipeline, which not only reduce costs, time, and space but enhance the genetic gain by reducing the length of the breeding cycle, increasing selection intensity, as well as allowing the breeders to have a clear knowledge of the genetics of the materials early in the pipeline ( Jarquin et al, 2014b ; Crossa et al, 2017 ; Vieira and Chen, 2021 ; Wartha and Lorenz, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

et al. 2022

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The availability of high-dimensional molecular markers has allowed plant breeding programs to maximize their efficiency through the genomic prediction of a phenotype of interest. Yield is a complex quantitative trait whose expression is sensitive to environmental stimuli. In this research, we investigated the potential of incorporating soil texture information and its interaction with molecular markers via covariance structures for enhancing predictive ability across breeding scenarios. A total of 797 soybean lines derived from 367 unique bi-parental populations were genotyped using the Illumina BARCSoySNP6K and tested for yield during 5 years in Tiptonville silt loam, Sharkey clay, and Malden fine sand environments. Four statistical models were considered, including the GBLUP model (M1), the reaction norm model (M2) including the interaction between molecular markers and the environment (G×E), an extended version of M2 that also includes soil type (S), and the interaction between soil type and molecular markers (G×S) (M3), and a parsimonious version of M3 which discards the G×E term (M4). Four cross-validation scenarios simulating progeny testing and line selection of tested–untested genotypes (TG, UG) in observed–unobserved environments [OE, UE] were implemented (CV2 [TG, OE], CV1 [UG, OE], CV0 [TG, UE], and CV00 [UG, UE]). Across environments, the addition of G×S interaction in M3 decreased the amount of variability captured by the environment (−30.4%) and residual (−39.2%) terms as compared to M1. Within environments, the G×S term in M3 reduced the variability captured by the residual term by 60 and 30% when compared to M1 and M2, respectively. M3 outperformed all the other models in CV2 (0.577), CV1 (0.480), and CV0 (0.488). In addition to the Pearson correlation, other measures were considered to assess predictive ability and these showed that the addition of soil texture seems to structure/dissect the environmental term revealing its components that could enhance or hinder the predictability of a model, especially in the most complex prediction scenario (CV00). Hence, the availability of soil texture information before the growing season could be used to optimize the efficiency of a breeding program by allowing the reconsideration of field experimental design, allocation of resources, reduction of preliminary trials, and shortening of the breeding cycle.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

et al. 2022

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“…Current status, advantages, challenges, and trends in these breeding schemes are highlighted and discussed. We refer to the reader to other focused reviews on the general topic of genomic selection (Voss-Fels et al 2018, Xu et al 2020, Crossa et al 2021, Wartha et al 2021, training population optimization (Hickey et al 2014, Isidro et al 2015, Berro et al 2019, and model selection (Heslot et al 2012.…”

Section: The Breeder's Equation -Shortening the Breeding Cycle To Acc...mentioning

confidence: 99%

Genomic selection with rapid cyclingcycling: Current insights and future prospects

Volpato

Bernardeli

Gomez

2021

Crop Breed. Appl. Biotechnol.

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Enhancing the rate of genetic gain in plant breeding program is critical to address global food security in the face of climate change and a growing population. Rapid cycling genomic selection offers a powerful breeding strategy to reduce the breeding cycle and obtain rapid genetic gains in plant breeding programs. In this paper, we discuss theoretical and empirical approaches to deploy and optimize rapid cycling genomic selection in crop improvement programs. We highlight major advantages and challenges associated with rapid cycling genomic selection. Finally, we discuss the trends and general conclusion on this breeding strategy and provide recommendations for future discussions and continued adoption in plant breeding programs.

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“…Based on GEBVs, superior breeding lines are selected at preliminary stages prior to phenotyping (Voss-Fels et al, 2019). Earlier selection allows breeders increase breeding efficiency and save costs (Crossa et al, 2017) by reducing the number of promising breeding lines that need to be evaluated in advanced multi-environment and replicated field trials (Wartha and Lorenz 2021).…”

mentioning

confidence: 99%

Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat

Ballén‐Taborda

Lyerly²,

Smith³

et al. 2022

Front. Genet.

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With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.

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Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

Cited by 18 publications

References 118 publications

Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

Incorporation of Soil-Derived Covariates in Progeny Testing and Line Selection to Enhance Genomic Prediction Accuracy in Soybean Breeding

Genomic selection with rapid cyclingcycling: Current insights and future prospects

Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat

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