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

被引:2
|
作者
Gunundu, Rodreck [1 ,2 ,4 ]
Shimelis, Hussein [1 ]
Mashilo, Jacob [1 ,3 ]
机构
[1] Univ KwaZulu Natal, Coll Agr Engn & Sci CAES, African Ctr Crop Improvement ACCI, Pietermaritzburg, South Africa
[2] Seed Co, Rattray Arnold Res Stn, Harare, Zimbabwe
[3] Towoomba Res Ctr, Limpopo Dept Agr & Rural Dev, Crop Sci Directorate, Bela Bela, South Africa
[4] Seed Co, Rattray Arnold Res Stn, POB CH142, Harare, Zimbabwe
关键词
genetic gains; genomic selection; phenotypic selection; prediction accuracy; speed breeding; 3 BREEDING ERAS; GRAIN-YIELD; POPULATION-STRUCTURE; GENETIC GAINS; DROUGHT TOLERANCE; COMPLEX TRAITS; INBRED LINES; PREDICTION; PLANT; COMPONENTS;
D O I
10.1111/pbr.13127
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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.
引用
收藏
页码:573 / 593
页数:21
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