Brassica vegetables and some of their novel food products such as sprouts, microgreens, and baby leaf, are rich in bioactive compounds and through their antioxidant activity are able to prevent or mitigate chronic diseases, such as cancer, cardiovascular disturbances, diabetes and obesity, and are useful for the prevention of neurodegenerative diseases like Alzheimer and Parkinson. Several interesting Brassica oleracea genes have been identified by several authors for improving bio-morphometric or quality traits, or for disease resistance and their detection can also present a new effort in terms of conventional or organic plant breeding. Agronomic traits like the ones related to the inflorescence or leaf, are regulated by quantitative, multigenic factors similar to bioactive compounds accumulation, which also act in multigenic genomic regions. The high-throughput genotyping techniques determine an important enhancement in the identification and detection of novel superior key alleles having a potential application in breeding programs. Allele mining represents a helpful tool able to dissect naturally occurring allelic mutations controlling the candidate gene expression differences among the several allelic variants, which can be present in the same or different loci. The complex loci are usually controlled by multiple alleles, which function as a diversity source allowing functional genes to generate, and the new breeding techniques are based on their detection. The methods applied for the identification of QTLs or candidate genes include traditional breeding approaches in addition to the linkage map construction by several genotyping techniques, and transcriptome analysis. PCR-based methods followed by genotyping through molecular markers also represent the basic tool for detecting genes or their most highly performing allelic variants. The following work aims to highlight the several methods, which can be utilized for superior alleles identification and detection in B. oleracea crops; the genomic loci analyzed in the present chapter were listed and commented on, focusing on the main results achieved and on the methods applied by the different authors.

Allele Mining in Brassica oleracea Crops

Treccarichi, Simone;Ammar, Hajer Ben;Branca, Ferdinando
2024-01-01

Abstract

Brassica vegetables and some of their novel food products such as sprouts, microgreens, and baby leaf, are rich in bioactive compounds and through their antioxidant activity are able to prevent or mitigate chronic diseases, such as cancer, cardiovascular disturbances, diabetes and obesity, and are useful for the prevention of neurodegenerative diseases like Alzheimer and Parkinson. Several interesting Brassica oleracea genes have been identified by several authors for improving bio-morphometric or quality traits, or for disease resistance and their detection can also present a new effort in terms of conventional or organic plant breeding. Agronomic traits like the ones related to the inflorescence or leaf, are regulated by quantitative, multigenic factors similar to bioactive compounds accumulation, which also act in multigenic genomic regions. The high-throughput genotyping techniques determine an important enhancement in the identification and detection of novel superior key alleles having a potential application in breeding programs. Allele mining represents a helpful tool able to dissect naturally occurring allelic mutations controlling the candidate gene expression differences among the several allelic variants, which can be present in the same or different loci. The complex loci are usually controlled by multiple alleles, which function as a diversity source allowing functional genes to generate, and the new breeding techniques are based on their detection. The methods applied for the identification of QTLs or candidate genes include traditional breeding approaches in addition to the linkage map construction by several genotyping techniques, and transcriptome analysis. PCR-based methods followed by genotyping through molecular markers also represent the basic tool for detecting genes or their most highly performing allelic variants. The following work aims to highlight the several methods, which can be utilized for superior alleles identification and detection in B. oleracea crops; the genomic loci analyzed in the present chapter were listed and commented on, focusing on the main results achieved and on the methods applied by the different authors.
2024
9781032453187
Bioscience, Environment & Agriculture
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/598469
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