Seed weight and size are important yield components. Thus, selecting for large seeds has been a key objective in crop domestication and breeding. In common bean, seed shape is also important since it influences industrial processing and plays a vital role in determining the choices of consumers and farmers. In this study, we performed genome-wide association studies (GWAS) on a core collection of common bean accessions to dissect the genetic architecture and identify genomic regions associated with seed morphological traits related to weight, size and shape. Phenotypic data was collected by high-throughput image-based approaches, and utilized to test associations with 10,362 SNP markers using multi-locus mixed-models. We searched within genome-associated regions for candidate genes putatively involved in seed phenotypic variation. The collection exhibited high variability for the entire set of seed traits, and the Andean gene pool was found to produce larger, heavier seeds than the Mesoamerican gene pool. Strong pairwise correlations were verified for most seed traits. GWAS identified marker-trait associations accounting for a considerable amount of phenotypic variation in length, width, projected area, perimeter and circularity in four distinct genomic regions. Promising candidate genes were identified, e.g., those encoding an AT-hook motif nuclear-localized protein 8, type 2C protein phosphatases and a protein Mei2-like 4 isoform, known to be associated with seed size and weight regulation. Moreover, the genes that were pinpointed are also good candidates for functional analysis to validate their influence on seed shape and size in common bean and other related crops.

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.

No abstract

Root-knot nematodes (RKN), particularly Meloidogyne incognita, are among the most damaging and prevalent agricultural pathogens due to their ability to infect roots of almost all crop species, including common bean. 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 since little has been done to discover resistance genes or find markers to assist selection. In this study, we performed genome-wide association studies and QTL 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 root-galling index were evaluated 30 days after inoculation. Complex genetic architecture and independent genomic regions were associated with each trait according to the Fixed and random model Circulating Probability Unification. SNPs located on chromosomes Pv06, Pv07, Pv08 and Pv11 were associated with the number of egg masses, and on Pv01, Pv02, Pv05 and Pv10 with root-galling. A total of 215 candidate genes were identified, including 14 resistance gene analogs and five differentially expressed in a previous RNA-seq analysis. The histochemical analysis indicated that the reactive oxygen species might play a role in the resistance response. Our findings open new perspectives to improve selection efficiency for RKN resistance in common bean, and the candidate genes are valuable targets for functional investigation and gene editing approaches.