Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

Abstract

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.

Translational genomics for achieving higher genetic gains in groundnut

“…Genomic selection (GS) has emerged as the promising new molecular breeding approach for improving even complex traits in less time and with more precision and accuracy (Meuwissen et al 2001;Crossa et al 2017;Xu et al 2020).…”

“…It is important to mention that GS approach not only promises to handle complex traits but also provides the additional advantage by reducing the selection cycle and avoiding extensive phenotyping through the selection of the superior lines based on prediction of the genomic-estimated breeding values (GEBV) (Crossa et al 2017). However, the prediction accuracy in GS approach is affected by several factors such as size of the training population and its constitution/structure, precise and quality phenotyping, marker density, and trait heritability (Xu et al 2020).…”

Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

“…Genomic selection (GS) is a relatively new breeding method in which individuals are selected based on their predicted breeding values that are calculated from genome-wide DNA marker profiles [72]. Decreasing costs of DNA marker screening methods such as high-density SNP arrays and genotyping by sequencing (GBS) approaches, and the development of statistical methods that can accurately predict marker effects are the main reasons why GS has increasingly been implemented in modern animal and plant breeding programs [73,74]. Two main avenues by which GS can accelerate the rate of genetic gain is by improving the accuracy at which individuals are selected and by reducing the length of the breeding cycle.…”

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

“…Moreover, integrating multityping information, the genotyping 3 environment 3 management (G3E3M) interaction could also be investigated, and predictive phenomics would be possible (Xu, 2016;Araus et al, 2018). In recent years, crop yield growth (genetic gain) has been slowing down, affected by several factors, for example, population, genotype, heritability, GS model, and breeding scheme (Xu et al, 2020). Integrated with optimized experimental design, high quality of field trials, robust crop model, envirotyping, and other strategies, high-throughput and accurate phenotyping will improve the heritability and potential for genetic gain (Araus et al, 2018).…”