Genomic selection to introgress exotic maize germplasm into elite maize in China to improve kernel dehydration rate

Yong, Hongjun; Wang, Nan; Yang, Xiaojun; Fengyi, Zhang; Tang, Juan; Yang, Zhiyuan; Zhao, Xiaoxi; Li, Yang; Li, Mingshun; Zhang, Degui; Zhang, Hao; Weng, Jianfeng; Han, Jienan; Li, Huihui; Li, Xinhai

doi:10.1007/s10681-021-02899-5

Cited by 1 publication

(2 citation statements)

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“…The magnitude of prediction accuracies is classified as low (0.1 to 0.4), medium (0.4 to 0.6) and high (0.6 and above) (Beyene et al, 2021;Môro et al, 2017;Shikha et al, 2017;Yong et al, 2021). Low prediction efficiencies were reported for grain yield in Brazil (0.26), China 1).…”

Section: Genomic Selection For Agronomic Traits In Maizementioning

confidence: 99%

“…The correlation directly links the GEBV forecast accuracy to the selection response and measures selection accuracy. The magnitude of prediction accuracies is classified as low (0.1 to 0.4), medium (0.4 to 0.6) and high (0.6 and above) (Beyene et al, 2021; Môro et al, 2017; Shikha et al, 2017; Yong et al, 2021). Low prediction efficiencies were reported for grain yield in Brazil (0.26), China (0.24) and Germany (0.42) (Table 1).…”

Section: Genomic Selection For Agronomic Traits In Maizementioning

confidence: 99%

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Genomic selection and enablers for agronomic traits in maize (Zea mays): A review

Gunundu,

Shimelis,

Mashilo

2023

Plant Breeding

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Maize is a commodity crop providing millions of people with food, feed, industrial raw material and economic opportunities. However, maize yields in Africa are relatively stagnant and low, at a mean of 1.7 t ha−1 compared with the global average of 6 t ha−1. The yield gap can be narrowed with accelerated and precision breeding strategies that are required to develop and deploy high‐yielding and climate‐resilient maize varieties. Genomic and phenotypic selections are complementary methods that offer opportunities for the speedy choice of contrasting parents and derived progenies for hybrid breeding and commercialization. Genomic selection (GS) will shorten the crop breeding cycle by identifying and tracking desirable genotypes and aid the timeous commercialization of market‐preferred varieties. The technology, however, has not yet been fully embraced by most public and private breeding programmes, notably in Africa. This review aims to present the importance, current status, challenges and opportunities of GS to accelerate genetic gains for economic traits to speed up the breeding of high‐yielding maize varieties. The first section summarizes genomic selection and the contemporary phenotypic selection and phenotyping platforms as a foundation for GS and trait integration in maize. This is followed by highlights on the reported genetic gains and progress through phenotypic selection and GS for grain yield and yield components. Training population development, genetic design and statistical models used in GS in maize breeding are discussed. Lastly, the review summarizes the challenges of GS, including prediction accuracy, and integrates GS with speed breeding, doubled haploid breeding and genome editing technologies to increase breeding efficiency and accelerate cultivar release. The paper will guide breeders in selection and trait introgression using GS to develop cultivars preferred by the marketplace.

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Section: Genomic Selection For Agronomic Traits In Maizementioning

confidence: 99%