Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

被引:185
|
作者
Xu, Yunbi [1 ,2 ,3 ]
Liu, Xiaogang [1 ]
Fu, Junjie [1 ]
Wang, Hongwu [1 ]
Wang, Jiankang [1 ]
Huang, Changling [1 ]
Prasanna, Boddupalli M. [5 ]
Olsen, Michael S. [5 ]
Wang, Guoying [1 ]
Zhang, Aimin [4 ]
机构
[1] Chinese Acad Agr Sci, Inst Crop Sci CIMMYT China, Beijing 100081, Peoples R China
[2] Foshan Univ, CIMMYT China Trop Maize Res Ctr, Foshan 528231, Peoples R China
[3] Shanghai Acad Agr Sci, CIMMYT China Specialty Maize Res Ctr, Shanghai 201400, Peoples R China
[4] Chinese Acad Sci, Inst Genet & Dev Biol, Beijing 100101, Peoples R China
[5] CIMMYT Int Maize & Wheat Improvement Ctr, ICRAF Campus,United Nations Ave, Nairobi, Kenya
关键词
genomic selection; genetic gain; open-source breeding; genomic prediction; molecular marker; livestock breeding; MARKER-ASSISTED SELECTION; LINEAR UNBIASED PREDICTION; DAIRY-CATTLE; GENOMEWIDE SELECTION; HYBRID PERFORMANCE; ENABLED PREDICTION; BREEDING PROGRAMS; MULTIPLE-TRAIT; QUANTITATIVE TRAITS; SEQUENCING REVEALS;
D O I
10.1016/j.xplc.2019.100005
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.
引用
收藏
页数:21
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