Genomic Selection: A Faster Strategy for Plant Breeding

*Gizachew Haile Gidamo*

### **Abstract**

Many agronomic traits, such as grain yield, are controlled by polygenes with minor effects and epistatic interaction. Genomic selection (GS) uses genome-wide markers to predict a genomic estimate of breeding value (GEBV) that is used to select favorable individuals. GS involves three essential steps: prediction model training, prediction of breeding value, and selection of favorable individual based on the predicted GEBV. Prediction accuracies were evaluated using either correlation between GEBV (predicted) and empirically estimated (observed) value or cross-validation technique. Factors such as marker diversity and density, size and composition of training population, number of QTL, and heritability affect GS accuracies. GS has got potential applications in hybrid breeding, germplasm enhancement, and yield-related breeding programs. Therefore, GS is promising strategy for rapid improvement of genetic gain per unit time for quantitative traits with low heritability in breeding programs.

**Keywords:** genomic selection, training population, breeding population, prediction accuracies, plant breeding

### **1. Introduction**

Since the 1990s, when promising analysis results for tagging genes or mapping QTL led to the development of marker-assisted selection (MAS). MAS has become a popular strategy in plant breeding. In the identification of underlying key genes in gene pools and their transfer to desirable traits in many plant breeding programs, marker-assisted selection and molecular breeding, have been applied. The use of MAS has shown some flaws, such as extensive selection schemes and the inability to catch "minor" gene effects when looking for crucial marker-QTL relationships. As a result, improving traits with complex inheritance, such as grain yield and abiotic stress tolerance, using MAS is difficult.

Genomic selection, also known as genome-wide selection, is a strategy that employs genotypic data from throughout the entire genome to accurately predict any trait, allowing for the selection of a favorable individual [1]. The most suitable individual is chosen based on a genomic estimate of breeding values (GEBVs). Breeding values are a popular and widely used measure in the animal breeding industry. Breeding values are defined as the "sum" of the estimated genetic deviation and the weighted total of the estimated breed effect [2], which are predicted using phenotypic data from family pedigrees based on the additive infinitesimal model. The success of selection in animal breeding, particularly in cattle and pigs, was aided by the infinitesimal genetic model and quantitative genetics. In the estimation of breeding value in animal breeding, the best linear unbiased prediction (BLUP) and Bayesian framework are often utilized. Following the introduction of genome sequencing in several model animals, a novel method for selection dubbed GEBV was developed [1]. In this chapter, principles of genomic selection and their application as a faster strategy for plant breeding is presented.
