Plant breeding has been the key powerful strength for productivity gains in main food, feed and industrial crop plants. The traditional and Marker-Assisted Breeding (MAB) are the two prime strategies utilized to implement crop improvement programmes. The traditional breeding encompasses crossing of contrast parents and selection over succeeding generations. This approach has several drawbacks, including the need for a long time to develop varieties based on phenotypic selection, higher environmental noise, and being less successful for multifaceted and poorly heritable traits. There are two types of MAB: Marker-Assisted Selection (MAS) and Genomic Selection (GS). MAS utilizes DNA markers that are known to be associated with the trait of attention to select plants with the targeted allele influencing that specific characteristic. It is effective only for the characters that are governed by a few major effects Quantitative Trait Loci (QTLs). While GS is an advanced version of MAS, it exploits all the markers within the genome to estimate the performance of desired feature. The genomic selection is a modern breeding technology which has revolutionized the breeding of crops, in this the prediction model based on the genotypic and phenotypic data of the training population (TP), is employed to determine the genomic-estimated breeding values (GEBV) for the breeding population (BP), which is acquired by genome-wide molecular markers and serve as a tool for the selection of elite plants. Genomic selection has a huge potential for crop enhancement because it enhances selection efficiency, reduces breeding cycle time and phenotyping costs, and eventually accelerates the genetic gain, particularly for the complex traits. In order to optimize the benefits of GS, next-generation sequencing (NGS) technologies can be integrated with high-throughput phenotyping to obtain precise genetic and phenotypic information.
Breeding population, Breeding value, Genomic selection, High-throughput phenotyping, Marker assisted selection, Next-generation sequencing, Training population
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