Genomic Prediction across Structured Hybrid Populations and Environments in Maize

National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China

National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China

Authors to whom correspondence should be addressed.

Academic Editor: Jelli Venkatesh

Plants 2021, 10(6), 1174; https://doi.org/10.3390/plants10061174

Received: 24 March 2021 / Revised: 1 June 2021 / Accepted: 2 June 2021 / Published: 9 June 2021

(This article belongs to the Special Issue Plant Molecular Breeding and Biotechnology)

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Abstract

Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding. View Full-Text

Keywords: maize; genomic prediction; genotype by environment; hybrid prediction; yield per plant

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MDPI and ACS Style

Li, D.; Xu, Z.; Gu, R.; Wang, P.; Xu, J.; Du, D.; Fu, J.; Wang, J.; Zhang, H.; Wang, G. Genomic Prediction across Structured Hybrid Populations and Environments in Maize. Plants 2021, 10, 1174. https://doi.org/10.3390/plants10061174

AMA Style

Li D, Xu Z, Gu R, Wang P, Xu J, Du D, Fu J, Wang J, Zhang H, Wang G. Genomic Prediction across Structured Hybrid Populations and Environments in Maize. Plants. 2021; 10(6):1174. https://doi.org/10.3390/plants10061174

Chicago/Turabian Style

Li, Dongdong, Zhenxiang Xu, Riliang Gu, Pingxi Wang, Jialiang Xu, Dengxiang Du, Junjie Fu, Jianhua Wang, Hongwei Zhang, and Guoying Wang. 2021. "Genomic Prediction across Structured Hybrid Populations and Environments in Maize" Plants 10, no. 6: 1174. https://doi.org/10.3390/plants10061174

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Genomic Prediction across Structured Hybrid Populations and Environments in Maize

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