Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane.

Abstract

Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder's equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.

Plant phenotyping relevance

サトウキビ育種におけるゲノム選抜と統合される高スループット表現型解析を、方法論・応用の一部として包括的に論じるレビューであり、植物フェノタイピングが明示的かつ実質的な主題です。

Code and data availability

This is a review article on genomic selection in sugarcane with no original phenotyping measurements or computational analysis of its own. The Data Availability Statement reads 'Not applicable.' The URLs in Table 2 are code repositories from cited prior studies (e.g., nimbus, DeepGS, DL_Wheat), not authors' assets for,

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