Genetic mapping reveals complex architecture and candidate genes involved in common bean response to Meloidogyne incognita infection

Root-knot nematodes (RKNs), particularly Meloidogyne incognita, are among the most damaging and prevalent agricultural pathogens due to their ability to infect roots of almost all crops. The best strategy for their control is through the use of resistant cultivars. However, laborious phenotyping procedures make it difficult to assess nematode resistance in breeding programs. For common bean, this task is especially challenging because little has been done to discover resistance genes or markers to assist selection. We performed genome-wide association studies and quantitative trait loci mapping to explore the genetic architecture and genomic regions underlying the resistance to M. incognita and to identify candidate resistance genes. Phenotypic data were collected by a high-throughput assay, and the number of egg masses and the root-galling index were evaluated. Complex genetic architecture and independent genomic regions were associated with each trait. Single nucleotide polymorphisms on chromosomes Pv06, Pv07, Pv08, and Pv11 were associated with the number of egg masses, and SNPs on Pv01, Pv02, Pv05, and Pv10 were associated with root-galling. A total of 216 candidate genes were identified, including 14 resistance gene analogs and five differentially expressed in a previous RNA sequencing analysis. Histochemical analysis indicated that reactive oxygen species might play a role in the resistance response. Our findings open new perspectives to improve selection efficiency for RKN resistance, and the candidate genes are valuable targets for functional investigation and gene editing approaches.

Automated summary

Root-knot nematodes, especially Meloidogyne incognita, are highly damaging agricultural pathogens infecting the roots of almost all crops. Identifying resistance genes for common bean is challenging, but genome-wide association studies and quantitative trait loci mapping can help discover candidate genes for resistance to RKN. This study revealed complex genetic architecture and identified potential target genes for functional investigation and gene editing approaches.