Genomic characterisation and dissection of the onset of resistance to acetyl CoA carboxylase-inhibiting herbicides in a large collection of Digitaria insularis from Brazil - Sehgal, Deepmala; Oliveira, Claudia; Mathioni, Sandra; Widdison, Stephanie; Plumb, Will; Campos, Breno; Kaundun, Shiv Shankhar (Frontiers in Genetics, 2024)
An in-depth genotypic characterisation of a diverse collection of Digitaria insularis was undertaken to explore the neutral genetic variation across the natural expansion range of this weed species in Brazil. With the exception of Minas Gerais, populations from all other states showed high estimates of expected heterozygosity (HE > 0.60) and genetic diversity. There was a lack of population structure based on geographic origin and a low population differentiation between populations across the landscape as evidenced by average Fst value of 0.02. On combining haloxyfop [acetyl CoA carboxylase (ACCase)-inhibiting herbicide] efficacy data with neutral genetic variation, we found evidence of presence of two scenarios of resistance evolution in this weed species. Whilst populations originating from north-eastern region demonstrated an active role of gene flow, populations from the mid-western region displayed multiple, independent resistance evolution as the major evolutionary mechanism. A target-site mutation (Trp2027Cys) in the ACCase gene, observed in less than 1% of resistant populations, could not explain the reduced sensitivity of 15% of the populations to haloxyfop. Tthe genetic architecture of resistance to acetyl CoA carboxylase (ACCase)-inhibiting herbicides was dissected using a genome wide association study (GWAS) approach. GWAS revealed association of three SNPs with reduced sensitivity to haloxyfop and clethodim. In silico analysis of these SNPs revealed important non-target site genes belonging to families involved in herbicide detoxification, including UDPGT91C1 and GT2, and genes involved in vacuolar sequestration-based degradation pathway. Exploration of five genomic prediction models revealed that the highest prediction power (≥ 0.80) was achieved with the models Bayes A and RKHS, incorporating SNPs with additive effects and epistatic interactions, respectively.