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.
Highlights
Sustainable food production is the utmost requirement for food and nutritional security
Genomic selection has shown its potential in plant and animal breeding research by increasing genetic gains in the last two decades
Revolution in terms of cheaper NGS technologies has made it possible to sequence the crop and animal genomes at a relatively low cost. It resulted in a number of completely sequenced crop and animal genomes with high-density SNP genotyping chips and their availability in the public domain, which may further boost the predictive ability of a genomic selection (GS) model
Summary
Sustainable food production is the utmost requirement for food and nutritional security. Genomic selection emerged as an important tool which can utilize such information for modeling the crop yield for effective and rapid selection under different environmental conditions to meet the production challenges in a climate-changing world. Epistatic interactions play a key role in explaining genetic variation for quantitative traits Ignoring such type of information in the prediction model might result in lower genomic prediction accuracies (Cooper et al, 2002). RR RKHS RF Bayesian LASSO and multiple linear regression RRBLUP Bayes Cπ and RKHS RRBLUP
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