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open-access-imgOpen AccessHigh‐throughput NGS‐based genotyping and phenotyping: Role in genomics‐assisted breeding for soybean improvement
Author(s)
Bhat Javaid Akhter,
Yu Deyue
Publication year2021
Publication title
legume science
Resource typeJournals
Abstract Soybean is an important food crop that provides edible protein and oil for human and animal nutrition. Conventional phenotypic‐based breeding approaches have made significant contribution in the last century by developing many improved soybean varieties. However, due to the longer time taken to develop a variety, low genetic gain per unit time, and adverse environmental influence of phenotypic‐based selection, conventional approaches are not sufficient to maintain pace with growing population and climate change. In this context, the recent method of genotypic selection, that is, genomics‐assisted breeding (GAB) is considered a promising approach to address the challenges in soybean breeding. However, to harness the true potential of GAB in soybean improvement, the great coverage and precision in the genotyping and phenotyping are required. Previously, a huge gap was observed between the discovery and practical use of quantitative trait loci (QTLs) in soybean improvement. It has been suggested that low marker density and manually collected phenotypes are the major reasons for this failure. Hence, high‐throughput genotyping (HTG) providing higher genome‐wide marker density, as well as accurate and precise phenotyping using high‐throughput digital phenotyping (HTP) platforms, can significantly increase the success of QTL and candidate gene identification in soybean. These approaches can greatly increase the practical utility of GAB in soybean and also offer a faster characterization of germplasm and breeding materials. This review provides the detailed information on how the recent innovations in genomics and phenomics can assists in improving the efficiency and potential of GAB in soybean improvement.
Subject(s)agronomy , artificial intelligence , biology , candidate gene , computational biology , computer science , context (archaeology) , gene , genetics , genome , genomic selection , genomics , genotype , genotyping , germplasm , marker assisted selection , microbiology and biotechnology , paleontology , phenomics , plant breeding , programming language , quantitative trait locus , selection (genetic algorithm) , single nucleotide polymorphism , trait
Language(s)English
ISSN2639-6181
DOI10.1002/leg3.81

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