Genomic Prediction across Structured Hybrid Populations and Environments in Maize

Li, Dongdong; Xu, Zhenxiang; Gu, Rong; Wang, Pingxi; Xu, Jialiang; Du, Dengxiang; Fu, Junjie; Wang, Jianhua; Zhang, Hongwei; Wang, Guoying

doi:10.3390/plants10061174

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…This is in line with previous findings that showed that adding less related lines to a training set did not reduce the predictive ability (e.g. Brauner et al 2020 ; Li et al 2021 ; Zhu et al 2021 ) and increasing the training set size generally results in higher predictive abilities of genomic selection (e.g. Zhao et al 2012 ; Thorwarth et al 2017 ; Li et al 2021 ; Zhu et al 2021 ).…”

Section: Discussionsupporting

confidence: 92%

“…5 ). Genomic prediction has been described to strongly depend on the relatedness between training and prediction set ( Albrecht et al 2011 ; Riedelsheimer et al 2013 ; Li et al 2021 ; Zhu et al 2021 ). Our results corroborate these findings, as the prediction among groups resulted in only low predictive abilities, even for the predictions among the more closely related Flint material.…”

Section: Discussionmentioning

confidence: 99%

“… Brauner et al 2020 ; Li et al 2021 ; Zhu et al 2021 ) and increasing the training set size generally results in higher predictive abilities of genomic selection (e.g. Zhao et al 2012 ; Thorwarth et al 2017 ; Li et al 2021 ; Zhu et al 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.)

Weiß

Zhu

Leiser

et al. 2022

G3 Genes|Genomes|Genetics

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Genomic selection is a well investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits, but replaces marker data with near infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative, but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as six Flint landraces, which were phenotyped in multi-environment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction—while achieving similar or even better results than with marker data.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.)

Weiß

Zhu

Leiser

et al. 2022

G3 Genes|Genomes|Genetics

Self Cite

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“…It is possible that GS by itself will be better than conventional programs in cases with even higher levels of G×E interaction, or cases where the genomic models are more accurate. Many different approaches to improve prediction accuracy have been previously shown, such as including non‐additive effects in the model (Dias et al., 2018; Oliveira et al., 2020; Zystro et al., 2021b), the relationship between the individuals from the training population and the target population (Li et al., 2021), among others. In addition to these considerations, our work highlights the impact that cycle time can have in a GS program.…”

Section: Discussionmentioning

confidence: 99%

Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

Peixoto,

Coelho,

Leach

et al. 2023

Crop Science

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New technologies have been developed over the last few years aiming to support breeding pipeline optimization for long‐term genetic gains. However, the implementation of these new tools and their impact on any breeding program's budget are not well studied. Here, we compare multiple breeding pipeline strategies accounting for genomic selection and high‐throughput phenotyping by means of hybrid gain and cost effectiveness. We simulated a hybrid crop breeding program through coalescent theory. We compared two strategies for parental updates and four breeding pipelines: conventional breeding pipeline; conventional breeding pipeline with high‐throughput phenotyping; conventional breeding pipeline with genomic selection; conventional breeding pipeline with genomic selection and high‐throughput phenotyping. All analyses were implemented under three different levels of genotype‐by‐environment interaction (G×E) and two traits heritabilities (0.3 and 0.7). Overall, the results show that scenarios with early parental selection perform better than the others. In addition, the implementation of high‐throughput phenotyping (HTP) delivered the highest hybrid gain in the long‐term, whereas the implementation of genomic selection seems to be more cost‐effective. We suggest, considering breeding programs with complex trait inheritance and accounting for higher levels of G×E, to invest in breeding pipelines accounting for genomic selection as a strategy to create and maintain long‐term hybrid gain. Moreover, considering an unconstrained budget, the investment in both, genomic selection and HTP, represents the best strategy. Hence, these results provide strategies that may aid breeders in optimizing self‐pollination breeding programs.This article is protected by copyright. All rights reserved

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“…It is possible that GS by itself will be better than conventional programs in cases with even higher levels of G×E interaction, or cases where the genomic models are more accurate. Many different approaches to improve prediction accuracy have been previously shown, such as including non-additive effects in the model (Dias et al, 2018;, the relationship inbetween the individuals from the training population and the target population (Li et al, 2021), among others.…”

Section: Discussionmentioning

confidence: 99%

Application of quantitative genetics tools to breeding program optimization

Peixoto

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Overall, the application of quantitative genetics theory has greatly influenced plant breeding programs over the last few decades. The aim of this study was to use and develop quantitative genetics tools to improve breeding programs. In the first chapter we simulated a hybrid crop breeding program and compared breeding pipelines with two strategies for parental updates, and we compare the gain and costs to implements genomic selection and high-throughput phenotyping into the pipeline. Our results suggest that early parental selection performs better and that high-throughput phenotyping together with genomic selection delivers the highest hybrid gain in the long-term. In the second chapter we evaluated the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction. We evaluated 506 hybrids in six environments and measured 21 traits. We considered eight statistical models for prediction and three cross-validation schemes CV1, CV0, and CV00. The results indicated RKHS model outperforming GBLUP models, and models with additive plus dominance kernels presented slight improvement for some traits. Therefore, we recommend using the RKHS model as a standard model for sweet corn hybrid prediction, and to implement genomic selection in sweet corn breeding programs to optimize the testcross stage and the candidates that reach the field stage. In the third chapter we describe SMate, a flexible R package for cross prediction and optimization, which represents a tool for breeding programs to balance genetic gains and inbreeding rate levels. The package builds a valid mate plan based on two core aspects: (i) prediction of usefulness for potential cross, and (ii) optimization of the set of crosses. In conclusion, SMate package enables to optimize cross selection in breeding programs targeting long term genetic gains while balancing genetic diversity and inbreeding rate levels. In summary, quantitative genetics tools have been largely applied in breeding programs and has evolved with it. Our study demonstrated potential to contribute to the quantitative genetics field and direct impact in breeding programs. Keywords: Mate allocation. Inbreeding. Genomic hybrid prediction. Stochastic simulation.

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Genomic Prediction across Structured Hybrid Populations and Environments in Maize

Cited by 7 publications

References 39 publications

Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.)

Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.)

Simulation‐based decision‐making and implementation of tools in hybrid crop breeding pipelines

Application of quantitative genetics tools to breeding program optimization

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