全基因组关联研究
生物
连锁不平衡
特质
基因组
分子育种
选择(遗传算法)
生物技术
数量性状位点
遗传关联
关联映射
遗传学
计算生物学
基因组学
单核苷酸多态性
基因型
计算机科学
基因
人工智能
程序设计语言
作者
Kumari Shikha,J. P. Shahi,M.T. Vinayan,P.H. Zaidi,Anil K. Singh,B. Sinha
出处
期刊:3 biotech
[Springer Science+Business Media]
日期:2021-04-29
卷期号:11 (5)
被引量:36
标识
DOI:10.1007/s13205-021-02799-4
摘要
Genome-wide association study (GWAS) provides a robust and potent tool to retrieve complex phenotypic traits back to their underlying genetics. Maize is an excellent crop for performing GWAS due to diverse genetic variability, rapid decay of linkage disequilibrium, availability of distinct sub-populations and abundant SNP information. The application of GWAS in maize has resulted in successful identification of thousands of genomic regions associated with many abiotic and biotic stresses. Many agronomic and quality traits of maize are severely affected by such stresses and, significantly affecting its growth and productivity. To improve productivity of maize crop in countries like India which contribute only 2% to the world’s total production in 2019–2020, it is essential to understand genetic complexity of underlying traits. Various DNA markers and trait associations have been revealed using conventional linkage mapping methods. However, it has achieved limited success in improving polygenic complex traits due to lower resolution of trait mapping. The present review explores the prospects of GWAS in improving yield, quality and stress tolerance in maize besides, strengths and challenges of using GWAS for molecular breeding and genomic selection. The information gathered will facilitate elucidation of genetic mechanisms of complex traits and improve efficiency of marker-assisted selection in maize breeding.
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