Genomic selection: Essence, applications, and prospects

Abstract

Genomic selection (GS) emerged as a key part of the solution to ensure the food supply for the growing human population thanks to advances in genotyping and other enabling technologies and improved understanding of the genotype-phenotype relationship in quantitative genetics. GS is a breeding strategy to predict the genotypic values of individuals for selection using their genotypic data and a trained model. It includes four major steps: training population design, model building, prediction, and selection. GS revises the traditional breeding process by assigning phenotyping a new role of generating data for the building of prediction models. The increased capacity of GS to evaluate more individuals, in combination with shorter breeding cycle times, has led to wide adoption in plant breeding. Research studies have been conducted to implement GS with different emphases in crop- and trait-specific applications, prediction models, design of training populations, and identifying factors influencing prediction accuracy. GS plays different roles in plant breeding such as turbocharging of gene banks, parental selection, and candidate selection at different stages of the breeding cycle. It can be enhanced by additional data types such as phenomics, transcriptomics, metabolomics, and enviromics. In light of the rapid development of artificial intelligence, GS can be further improved by either upgrading the entire framework or individual components. Technological advances, research innovations, and emerging challenges in agriculture will continue to shape the role of GS in plant breeding.

FIGURE 1

The main driving forces behind the development and advancement of genomic selection (GS) in plant breeding. Forces are grouped into breeding challenges, conceptual framework, and technology development. Global food security and resource allocation are constant breeding challenges that led to GS and other breeding innovations. Genotype–phenotype relationship, methodological developments, and improved understanding of the genetic control of complex traits provide a conceptual framework for the inception and continued improvement of GS. Advances in genotyping, phenotyping, and envirotyping technologies enable the revision of the breeding process and enable the implementation of GS. Emerging challenges in agriculture, technological advances, and improvements in the conceptual framework will continue to shape the role of GS in plant breeding.

FIGURE 2

Genomic selection (GS) steps in plant breeding. GS follows a cyclical four‐step process, with outputs from one step serving as inputs for the following step. (1) Training population design, where breeders define the individuals used for model building, as well as the testing and genotyping approaches. Training populations can be designed from newly created populations that must undergo genotyping and phenotyping or in some cases from historical data where genotypic and phenotypic information already exist. (2) Model building, where information from the training population is used as input data to build models that are assessed using cross‐validation, and the genomic prediction (GP) models with better performance are selected. (3) Prediction, where predicted genotypic values are generated by using the trained GP model and genotypic data from untested individuals in the breeding program. (4) Selection, where decisions are made using the predicted genotypic values alone or together with other criteria. GS is a cyclical process, and as genotypic and phenotypic data of advanced individuals become available, training populations are updated, and models are retrained.

FIGURE 3

Prediction scenarios for genomic selection in the multi‐environment trial context. (A) Prediction of tested genotypes in untested environments. (B) prediction of untested genotypes in tested environments. (C) prediction of untested genotypes in untested environments.

FIGURE 4

Genomic selection (GS) to explore and harness genetic diversity. Turbocharging gene banks through GS is a framework proposed to explore available genetic resources in gene banks to identify diversity donors for pre‐breeding and bridging. GS can help speed up the recurrent improvement of diversity donors (pre‐breeding) and the improvement within elite by diversity donor crosses (bridging).

FIGURE 5

Challenges and future trends of genomic selection (GS) in plant breeding. Critical challenges include decision‐making under uncertainty, the unrealized potential of deep learning (DL) models, data complexity from different data types, partial understanding of how phenotypes emerge from genomes interacting with environments through development, and the growing gap between technology capabilities and adoption. Possible solutions to these challenges are the developments of decision support systems, explainable AI, efficient modeling of multiple data types, reconstruction and analysis of gene regulatory networks and pangenomes, and effective education and training programs for breeders and farmers. We foresee GS strategies design based on probability decisions, effective DL models for GS, efficient integration of multiple data types into GP, better understanding of the genetic control of traits under different environments, and more hands‐on training for breeders and farmers on new technologies.