Mitigating the impact of selective phenotyping in training populations on the prediction ability by multi‐trait pedigree and genomic selection models

Michel, Sebastian; Löschenberger, Franziska; Sparry, Ellen; Ametz, Christian; Buerstmayr, Hermann

doi:10.1111/pbr.12862

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…Here, the amount of information for each OTS, regardless of the kernel, is small, representing between 1.6 and 19.6% of the total available information. Moreover, the samples have a good distribution of genotypes, including those genotypes that perform well, and those that are not so good, bringing positive impacts on PA (Michel et al, 2020). As expected and similar to what Pinho Morais et al (2020) found, with a small effective population size, PA is diminished, since the sample contains small genetic variability.…”

Section: Discussionsupporting

confidence: 75%

Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

Gevartosky

Costa‐Neto

et al. 2021

Preprint

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Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

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Section: Discussionsupporting

confidence: 75%

Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

Gevartosky

Costa‐Neto

et al. 2021

Preprint

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“…This random sampling was however restricted to the same range of phenotypic performance in all four populations to prevent a putative tail effect. Such a tail effect would lead to lower prediction abilities when merely the lower or upper tail of the entire distribution is represented in the training population (e.g., by selective phenotyping of superior performing lines) (Michel et al, 2020b;Zhao et al, 2012). Since the in-house developed lines showed generally a higher performance than the externally developed lines for the investi-gated quality traits, the mentioned restriction was introduced to exclude the influence of such a tail effect when making inferences about the effect of the genetic distance on genomic predictions.…”

Section: Single-trait and Multitrait Genomic Prediction Across Breeding Programsmentioning

confidence: 99%

Genomic selection of parents and crosses beyond the native gene pool of a breeding program

Michel

Löschenberger²,

Ametz³

et al. 2021

The Plant Genome

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Genomic selection has become a valuable tool for selecting cultivar candidates in many plant breeding programs. Genomic selection of elite parents and crossing combinations with germplasm developed outside a breeding program has, however, hardly been explored until now. The aim of this study was to assess the potential of this method for commonly ranking and selecting elite germplasm developed within and beyond a given breeding program. A winter wheat (Triticum aestivum L.) population consisting of 611 in-house and 87 externally developed lines was used to compare training population compositions and statistical models for genomically predicting baking quality in this framework. Augmenting training populations with lines from other breeding programs had a larger influence on the prediction ability than adding in-house generated lines when aiming to commonly rank both germplasm sets. Exploiting preexisting information of secondary correlated traits resulted likewise in more accurate predictions both in empirical analyses and simulations. Genotyping germplasm developed beyond a given breeding program is moreover a convenient way to clarify its relationships with a breeder's own germplasm because pedigree information is oftentimes not available for this purpose. Genomic predictions can thus support a more informed diversity management, especially when integrating simply to phenotype correlated traits to partly circumvent resource reallocations for a costly phenotyping of germplasm from other programs.

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“…Here, the amount of information for each OTS, regardless of the kernel, is small, representing between 1.6 and 19.6% of the total available information. Moreover, the samples have an adequate distribution of genotypes, including those that perform well and those that are subpar, bringing positive impacts on PA [ 37 ]. As expected and similar to the findings of Pinho Morais et al [ 43 ], PA is diminished with a small effective population size since the sample contains low genetic variability.…”

Section: Discussionmentioning

confidence: 99%

Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize

et al. 2023

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Background Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. Results The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. Conclusions Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.

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Mitigating the impact of selective phenotyping in training populations on the prediction ability by multi‐trait pedigree and genomic selection models

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 6 publications

References 42 publications

Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

Genomic selection of parents and crosses beyond the native gene pool of a breeding program

Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize

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