| Literature DB >> 28965742 |
José Crossa1, Paulino Pérez-Rodríguez2, Jaime Cuevas3, Osval Montesinos-López4, Diego Jarquín5, Gustavo de Los Campos6, Juan Burgueño7, Juan M González-Camacho2, Sergio Pérez-Elizalde2, Yoseph Beyene7, Susanne Dreisigacker7, Ravi Singh7, Xuecai Zhang7, Manje Gowda7, Manish Roorkiwal8, Jessica Rutkoski9, Rajeev K Varshney10.
Abstract
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.Entities:
Keywords: genomic selection; genomic selection and genetic gains in crop breeding populations; genomic-enabled prediction accuracy; model complexity; models for genomic genotype×environment interaction
Mesh：
Year: 2017 PMID： 28965742 DOI： 10.1016/j.tplants.2017.08.011
Source DB: PubMed Journal: Trends Plant Sci ISSN： 1360-1385 Impact factor: 18.313