生物
种质资源
基因组选择
选择(遗传算法)
植物育种
基因型
计算生物学
进化生物学
生物技术
遗传学
基因
农学
机器学习
计算机科学
单核苷酸多态性
作者
José Crossa,Paulino Pérez‐Rodríguez,Jaime Cuevas,Osval A. Montesinos‐López,Diego Jarquín,Gustavo de los Campos,Juan Burgueño,Juan Manuel González-Camacho,Sergio Pérez‐Elizalde,Yoseph Beyene,Susanne Dreisigacker,Ravi P. Singh,Xuecai Zhang,Manje Gowda,Manish Roorkiwal,Jessica Rutkoski,Rajeev K. Varshney
标识
DOI:10.1016/j.tplants.2017.08.011
摘要
Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
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