Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

Abstract

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Reproductive-Stage Heat Stress in Cereals: Impact, Plant Responses and Strategies for Tolerance Improvement

“…Additionally, it excludes the need to unearth QTLs related to target traits and the need for the collection of phenotypes of the breeding population, thereby reducing costs [152]. GS utilizes genomewide estimated breeding values (GEBVs) and exploits genome-wide genotype-phenotype association, thereby capturing both major and minor gene effects [154]. Already, GS has greatly revolutionized genotypic performance prediction and selection of complex traits such as disease resistance, yield and quality in wheat [155], drought tolerance in maize [156], and several biotic and abiotic stresses in diverse crops [154].…”

“…GS utilizes genomewide estimated breeding values (GEBVs) and exploits genome-wide genotype-phenotype association, thereby capturing both major and minor gene effects [154]. Already, GS has greatly revolutionized genotypic performance prediction and selection of complex traits such as disease resistance, yield and quality in wheat [155], drought tolerance in maize [156], and several biotic and abiotic stresses in diverse crops [154]. With the increased use of GS in crop improvement programs and intensification of RSHS tolerance breeding in cereals, the development of HS tolerance improved cultivars will be expedited.…”

Genomics Enabled Breeding Strategies for Major Biotic Stresses in Pea (Pisum sativum L.)

“…Most of the successful events of the utilization of GS in biotic stress resistance were in cereal crops. In wheat, GS was used for three types of rust, Fusarium head blight, septoria tritici blotch, PMD, tan spot, and Stagonospora nodorum blotch ( Budhlakoti et al, 2022 ). The genomic prediction accuracies for these diseases ranged from 0.14 to 0.85 ( Daetwyler et al, 2010 ; Rutkoski et al, 2012 ; Mirdita et al, 2015 ; Juliana et al, 2019 ; Sarinelli et al, 2019 ).…”

Indian Wheat Genomics Initiative for Harnessing the Potential of Wheat Germplasm Resources for Breeding Disease-Resistant, Nutrient-Dense, and Climate-Resilient Cultivars

“…Genomic selection has a potential breeding strategy to map numerous genetic loci for diverse traits of interest. Various research groups started working on Indian wheat and associated germplasm genotype for crop improvement against biotic resistance ( Juliana et al, 2021 ; Budhlakoti et al, 2022 ). But still, more accurate prediction from a large genotype reservoir of Indian wheat germplasm is necessary for germplasm-assisted crop improvements for abiotic and quality-related traits.…”