Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane

Autor: Karansher Singh Sandhu, Aalok Shiv, Gurleen Kaur, Mintu Ram Meena, Arun Kumar Raja, Krishnapriya Vengavasi, Ashutosh Kumar Mall, Sanjeev Kumar, Praveen Kumar Singh, Jyotsnendra Singh, Govind Hemaprabha, Ashwini Dutt Pathak, Gopalareddy Krishnappa
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Plants, Vol 11, Iss 16, p 2139 (2022)
Druh dokumentu: article
ISSN: 11162139
2223-7747
DOI: 10.3390/plants11162139
Popis: 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.
Databáze: Directory of Open Access Journals
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