Evaluation of Genomic Selection Training Population Designs and Genotyping Strategies in Plant Breeding Programs Using Simulation

Hickey, John M.; Dreisigacker, Susanne; Crossa, J.; Hearne, Sarah; Babu, Raman; Prasanna, Boddupalli M.; Grondona, Martín O.; Zambelli, Andrés; Windhausen, Vanessa S.; Mathews, Ky L.; Gorjanc, Gregor

doi:10.2135/cropsci2013.03.0195

Cited by 177 publications

(213 citation statements)

References 36 publications

(43 reference statements)

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“…This is in agreement with Hickey et al. () and suggests that genomic prediction in potato like in other crops can improve with increased training population size (Windhausen et al. , Crossa et al.…”

Section: Discussionsupporting

confidence: 90%

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Genomic prediction for yields, processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

2017

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Current potato breeding approaches are hampered by several factors including costly seed tubers, tetrasomic inheritance and inbreeding depression. Genomic selection (GS) demonstrated interesting results regardless of the ploidy level, and can be harnessed to circumvent these problems. In this work, three GS models were evaluated using 50,107 informative SilicoDArT markers and 11 traits in two values for cultivation and use (VCU) potato trials. Two key breeding problems modelled included predicting the performance of (i) new and unphenotyped clones (cross‐validation) and (ii) a VCU using another as training set (TS). GS models performed comparably. Cross‐validation accuracy was high for D35, D45, DMW and BVAL, in ascending order. Prediction accuracies of the VCUs were highly correlated, but the best prediction was obtained for the smaller VCU using the bigger as TS. Cross‐validation and VCU prediction accuracies were higher when bigger TSs were used. The findings herein indicate that GS can be attractively integrated in potato breeding, particularly in early clonal generations to predict and select for traits with low heritability which would otherwise require more testing years, environments and resources.

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Section: Discussionsupporting

confidence: 90%

“…) implies that using a bigger VCU 2013 as training set enabled to sample useful marker–QTL linkages of interest (Hickey et al. ). A good prediction from one VCU to another was also expected in virtue of the genetic bottleneck reported in cultivated potato (Fehr ).…”

Section: Discussionmentioning

confidence: 99%

Genomic prediction for yields, processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

2017

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“…Unfortunately, field testing is slow and costly, forcing breeders to limit the number of genotypes that is phenotyped. Genomic prediction offers the potential to alleviate this limitation, allowing to broaden the pool of genotypes for selection, and thereby increasing selection intensity (Crossa et al 2013; Windhausen et al 2012) and efficiency of breeding programs (Heffner et al 2010; Crossa et al 2013; Windhausen et al 2012; Hickey et al 2014; Longin et al 2015). …”

mentioning

confidence: 99%

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Bustos-Korts

Malosetti

Chapman

et al. 2016

G3 Genes|Genomes|Genetics

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Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction methods led to similar predictive ability, a reflection of the very strong population structure in this panel.

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“…Utilization of molecular technologies that have revolutionized commercial crop breeding can be used as a proof of concept for adoption of such genomics-based prediction methodologies [122,123] to improve trait performance in other less-studied crops [115,116]. These approaches are being adopted in crops of importance in developing countries such as in maize and wheat [121], rice [124], pulses (legumes) [11], cassava [118,120], cowpea [125], lentil [126], soybean [127,128], and pigeon pea [129].…”

Section: Molecular Breedingmentioning

confidence: 99%

Perspectives on the Application of Next-generation Sequencing to the Improvement of Africa’s Staple Food Crops

Gedil¹,

Ferguson²,

Girma³

et al. 2016

Next Generation Sequencing - Advances, Applications and Challenges

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The persistent challenge of insufficient food, unbalanced nutrition, and deteriorating natural resources in the most vulnerable nations, characterized by fast population growth, calls for utilization of innovative technologies to curb constraints of crop production. Enhancing genetic gain by using a multipronged approach that combines conventional and genomic technologies for the development of stress-tolerant varieties with high yield and nutritional quality is necessary. The advent of nextgeneration sequencing (NGS) technologies holds the potential to dramatically impact the crop improvement process. NGS enables whole-genome sequencing (WGS) and re-sequencing, transcriptome sequencing, metagenomics, as well as high-throughput genotyping, which can be applied for genome selection (GS). It can also be applied to diversity analysis, genetic and epigenetic characterization of germplasm and pathogen detection, identification, and elimination. High-throughput phenotyping, integrated data management, and decision support tools form the necessary supporting environment for effective utilization of genome sequence information. It is important that these opportunities for mainstreaming innovative breeding strategies, enabled by cutting-edge "Omics" technologies, are seized in Africa; however, several constraints must be addressed before the benefit of NGS can be fully realized. African breeding programs must have access to high-throughput genotyping facilities, capacity in the application of genome selection and marker-assisted breeding must be built and supported by capacity in genomic analysis and bioinformatics. This chapter demonstrates how interventions with NGS-enabled innovative strategies can be applied to increase genetic gain with insights from the Consortium of International

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Evaluation of Genomic Selection Training Population Designs and Genotyping Strategies in Plant Breeding Programs Using Simulation

Cited by 177 publications

References 36 publications

Genomic prediction for yields, processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

Genomic prediction for yields, processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

Perspectives on the Application of Next-generation Sequencing to the Improvement of Africa’s Staple Food Crops

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