Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

ABSTRACT: Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. W

SUBMITTER: Aono AH 

PROVIDER: S-EPMC7676261 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature