Optimization of training sets for genomic prediction of early-stage single crosses in maize

Kadam, Dnyaneshwar C.; Rodrı́guez, Oscar; Lorenz, Aaron J.

doi:10.1007/s00122-020-03722-w

Cited by 19 publications

(24 citation statements)

References 38 publications

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“…Kadam et al (2016) reported that including SCA effects in GS models led to higher quality of predictions when predicting hybrids with untested parents (T0) compared to hybrids with one or two tested parents (T1 or T2) but this result was not confirmed in our study. We computed the SCA kinship matrix coefficient as the product, term to term, of the GCA kinship coefficients between the dent and the flint parental lines, similarly to what has been done in previous studies (Bernardo 1994;Technow et al 2014;Seye et al 2020;Kadam et al 2021). González-Diéguez et al, (2021) showed that this computation of the SCA kinship matrix does not capture the whole SCA but only part of it, i.e.…”

Section: Importance Of Sca and Its Predictionmentioning

confidence: 99%

“…Therefore, even if this approach is appealing (Kadam et al 2016;Giraud et al 2017a, b;Fristche-Neto et al 2018;Kadam et al 2021) and its efficiency compared to the tester approach was assessed by simulations (Seye et al 2020), further investigations and experimental validation are needed. The first experimental study (Fristche-Neto et al 2018) that investigated the influence of the mating design to build the TRS in maize breeding showed a clear advantage of a factorial or a diallel over a tester design.…”

Section: Introductionmentioning

confidence: 99%

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Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage

et al. 2022

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In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (i) allocating each of them to a different tester for the tester design, or (ii) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.

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Section: Importance Of Sca and Its Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage

et al. 2022

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“…It was proposed in numerous studies to define the CS by stratified sampling. These algorithms ensure that each group is well represented in the CS, possibly taking the size of each group into account [84,87,[90][91][92][93]. The efficiency of these approaches to increase predictive ability was disappointing, as they were often not better (sometimes worse) than random sampling, and most of the time not as good as relatedness-based criteria not taking structure into account.…”

Section: Taking Population Structure Into Accountmentioning

confidence: 99%

“…As a result, it is highly recommended to optimize the PEVmean or CDmean of the predicted individuals [83,87,99,100] rather than those of the individuals composing the CS [91,101]. These criteria have been tested and validated in different species such as maize [83,86,87,93], palm tree [68], wheat [102][103][104], barley [90], oat [15], cassava [105,106], miscanthus [27], Arabidopsis [99], apple tree [88], and peas [107] in populations of various levels of relatedness. CDmean led to prediction accuracies at least as good as those obtained with model-free criteria [83,86,87,91,93] with some exceptions [88][89][90]108].…”

Section: Optimizationmentioning

confidence: 99%

Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches

Rio

Charcosset

Mary‐Huard

et al. 2022

Methods in Molecular Biology

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The efficiency of genomic selection strongly depends on the prediction accuracy of the genetic merit of candidates. Numerous papers have shown that the composition of the calibration set is a key contributor to prediction accuracy. A poorly defined calibration set can result in low accuracies, whereas an optimized one can considerably increase accuracy compared to random sampling, for a same size. Alternatively, optimizing the calibration set can be a way of decreasing the costs of phenotyping by enabling similar levels of accuracy compared to random sampling but with fewer phenotypic units. We present here the different factors that have to be considered when designing a calibration set, and review the different criteria proposed in the literature. We classified these criteria into two groups: model-free criteria based on relatedness, and criteria derived from the linear mixed model. We introduce criteria targeting specific prediction objectives including the prediction of highly diverse panels, biparental families, or hybrids. We also review different ways of updating the calibration set, and different procedures for optimizing phenotyping experimental designs.

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“…While the usefulness of optimal training set (TRS) in GS is clearly supported by the literature (Rincent et al, 2012;Akdemir et al, 2015;Isidro et al, 2015;Lorenz and Smith, 2015;He et al, 2016;Cericola et al, 2017;Neyhart et al, 2017;Norman et al, 2018;Guo et al, 2019;Mangin et al, 2019;de Bem Oliveira et al, 2020;Olatoye et al, 2020;Yu et al, 2020;Kadam et al, 2021), the flexible and efficient software tools for implementing them have been limited. Indeed, only a few software tools such as STPGA (Akdemir, 2017) and TSDFGS (Ou and Liao, 2019) are available for public use.…”

Section: Introductionmentioning

confidence: 99%

TrainSel: An R Package for Selection of Training Populations

2021

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A major barrier to the wider use of supervised learning in emerging applications, such as genomic selection, is the lack of sufficient and representative labeled data to train prediction models. The amount and quality of labeled training data in many applications is usually limited and therefore careful selection of the training examples to be labeled can be useful for improving the accuracies in predictive learning tasks. In this paper, we present an R package, TrainSel, which provides flexible, efficient, and easy-to-use tools that can be used for the selection of training populations (STP). We illustrate its use, performance, and potentials in four different supervised learning applications within and outside of the plant breeding area.

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Optimization of training sets for genomic prediction of early-stage single crosses in maize

Cited by 19 publications

References 38 publications

Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage

Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage

Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches

TrainSel: An R Package for Selection of Training Populations

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