Development and validation of functional kompetitive allele-specific PCR markers for herbicide resistance in Brassica napus

Front Plant Sci. 2023 Nov 23:14:1213476. doi: 10.3389/fpls.2023.1213476. eCollection 2023.

Abstract

Effective weed control in the field is essential for maintaining favorable growing conditions and rapeseed yields. Sulfonylurea herbicides are one kind of most widely used herbicides worldwide, which control weeds by inhibiting acetolactate synthase (ALS). Molecular markers have been designed from polymorphic sites within the sequences of ALS genes, aiding marker-assisted selection in breeding herbicide-resistant rapeseed cultivars. However, most of them are not breeder friendly and have relatively limited application due to higher costs and lower throughput in the breeding projects. The aims of this study were to develop high throughput kompetitive allele-specific PCR (KASP) assays for herbicide resistance. We first cloned and sequenced BnALS1 and BnALS3 genes from susceptible cultivars and resistant 5N (als1als1/als3als3 double mutant). Sequence alignments of BnALS1 and BnALS3 genes for cultivars and 5N showed single nucleotide polymorphisms (SNPs) at positions 1676 and 1667 respectively. These two SNPs for BnALS1 and BnALS3 resulted in amino acid substitutions and were used to develop a KASP assay. These functional markers were validated in three distinct BC1F2 populations. The KASP assay developed in this study will be valuable for the high-throughput selection of elite materials with high herbicide resistance in rapeseed breeding programs.

Keywords: ALS genes; KASP assay; SNPs; herbicide resistance; marker-assisted selection.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (grant no. 31972875), the Project of Agriculture, Rural areas, Farmers and Nine Parties of Zhejiang Province (grant no. 2022SNJF010) and Key Laboratory of Digital Upland Crops of Zhejiang Province (grant no. 2022E10012).