Genetic Analyses and Genomic Predictions of Root Rot Resistance in Common Bean Across Trials and Populations

GWAS-assisted and multitrait genomic prediction for improvement of seed yield and canning quality traits in a black bean breeding panel

In recent years, black beans (Phaseolus vulgaris L.) have gained popularity in the United States, with improved seed yield and canning quality being critical traits for new cultivars. Achieving genetic gains in these traits is often challenging due to negative trait associations and the need for specialized equipment and trained sensory panels for evaluation. This study investigates the integration of genomics and phenomics to enhance selection accuracy for these complex traits. We evaluated the prediction accuracy of single-trait (ST) and multitrait (MT) genomic prediction (GP) models, incorporating near-infrared spectroscopy (NIRS) data and markers identified through genome-wide association studies (GWAS). The models demonstrated moderate prediction accuracies for yield and canning appearance (App) and high accuracies for color retention. No significant differences were found between ST and MT models within the same breeding cycle. However, across breeding cycles, MT models outperformed ST models by up to 45 and 63% for canning App and seed yield, respectively. Interestingly, incorporating significant SNP markers identified by GWAS and NIRS data into the models tended to decrease prediction accuracy both within and between breeding cycles. As genotypes from the new breeding cycle were included, the models' prediction accuracy generally increased. Our findings underscore the potential of MT models to enhance the prediction of complex traits such as seed yield and canning quality in dry beans and highlight the importance of continually updating the training dataset for effective GP implementation in dry bean breeding.

Genome-Wide Association Study and Genomic Prediction of Fusarium Wilt Resistance in Common Bean Core Collection

The common bean (Phaseolus vulgaris L.) is a globally cultivated leguminous crop. Fusarium wilt (FW), caused by Fusarium oxysporum f. sp. phaseoli (Fop), is a significant disease leading to substantial yield loss in common beans. Disease-resistant cultivars are recommended to counteract this. The objective of this investigation was to identify single nucleotide polymorphism (SNP) markers associated with FW resistance and to pinpoint potential resistant common bean accessions within a core collection, utilizing a panel of 157 accessions through the Genome-wide association study (GWAS) approach with TASSEL 5 and GAPIT 3. Phenotypes for Fop race 1 and race 4 were matched with genotypic data from 4740 SNPs of BARCBean6K_3 Infinium Bea Chips. After ranking the 157-accession panel and revealing 21 Fusarium wilt-resistant accessions, the GWAS pinpointed 16 SNPs on chromosomes Pv04, Pv05, Pv07, Pv8, and Pv09 linked to Fop race 1 resistance, 23 SNPs on chromosomes Pv03, Pv04, Pv05, Pv07, Pv09, Pv10, and Pv11 associated with Fop race 4 resistance, and 7 SNPs on chromosomes Pv04 and Pv09 correlated with both Fop race 1 and race 4 resistances. Furthermore, within a 30 kb flanking region of these associated SNPs, a total of 17 candidate genes were identified. Some of these genes were annotated as classical disease resistance protein/enzymes, including NB-ARC domain proteins, Leucine-rich repeat protein kinase family proteins, zinc finger family proteins, P-loopcontaining nucleoside triphosphate hydrolase superfamily, etc. Genomic prediction (GP) accuracy for Fop race resistances ranged from 0.26 to 0.55. This study advanced common bean genetic enhancement through marker-assisted selection (MAS) and genomic selection (GS) strategies, paving the way for improved Fop resistance.

Genetic analysis of resistance to bean leaf crumple virus identifies a candidate LRR-RLK gene

Bean leaf crumple virus (BLCrV) is a novel begomovirus (family Geminiviridae, genus Begomovirus) infecting common bean (Phaseolus vulgaris L.), threatening bean production in Latin America. Genetic resistance is required to ensure yield stability and reduce the use of insecticides, yet the available resistance sources are limited. In this study, three common bean populations containing a total of 558 genotypes were evaluated in different yield and BLCrV resistance trials under natural infection in the field. A genome-wide association study identified the locus BLC7.1 on chromosome Pv07 at 3.31 Mbp, explaining 8 to 16% of the phenotypic variation for BLCrV resistance. In comparison, whole-genome regression models explained 51 to 78% of the variation and identified the same region on Pv07 to confer resistance. The most significantly associated markers were located within the gene model Phvul.007G040400, which encodes a leucine-rich repeat receptor-like kinase subfamily III member and is likely to be involved in the innate immune response against the virus. The allelic diversity within this gene revealed five different haplotype groups, one of which was significantly associated with BLCrV resistance. As the same genome region was previously reported to be associated with resistance against other geminiviruses affecting common bean, our study highlights the role of previous breeding efforts for virus resistance in the accumulation of positive alleles against newly emerging viruses. In addition, we provide novel diagnostic single-nucleotide polymorphism markers for marker-assisted selection to exploit BLC7.1 for breeding against geminivirus diseases in one of the most important food crops worldwide.

Metabolome Profiling: A Breeding Prediction Tool for Legume Performance under Biotic Stress Conditions

Legume crops such as common bean, pea, alfalfa, cowpea, peanut, soybean and others contribute significantly to the diet of both humans and animals. They are also important in the improvement of cropping systems that employ rotation and fix atmospheric nitrogen. Biotic stresses hinder the production of leguminous crops, significantly limiting their yield potential. There is a need to understand the molecular and biochemical mechanisms involved in the response of these crops to biotic stressors. Simultaneous expressions of a number of genes responsible for specific traits of interest in legumes under biotic stress conditions have been reported, often with the functions of the identified genes unknown. Metabolomics can, therefore, be a complementary tool to understand the pathways involved in biotic stress response in legumes. Reports on legume metabolomic studies in response to biotic stress have paved the way in understanding stress-signalling pathways. This review provides a progress update on metabolomic studies of legumes in response to different biotic stresses. Metabolome annotation and data analysis platforms are discussed together with future prospects. The integration of metabolomics with other “omics” tools in breeding programmes can aid greatly in ensuring food security through the production of stress tolerant cultivars.

Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59-0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.

Associated SNPs, Heritabilities, Trait Correlations, and Genomic Breeding Values for Resistance in Snap Beans (Phaseolus vulgaris L.) to Root Rot Caused by Fusarium solani (Mart.) f. sp. phaseoli (Burkholder)

Root rot is a major constraint to snap bean (Phaseolus vulgaris) production in the United States and around the world. Genetic resistance is needed to effectively control root rot disease because cultural control methods are ineffective, and the pathogen will be present at the end of one season of production on previously clean land. A diversity panel of 149 snap bean pure lines was evaluated for resistance to Fusarium root rot in Oregon. Morphological traits potentially associated with root rot resistance, such as aboveground biomass, adventitious roots, taproot diameter, basal root diameter, deepest root angle, shallowest root angle, root angle average, root angle difference, and root angle geometric mean were evaluated and correlated to disease severity. A genome wide association study (GWAS) using the Fixed and random model Circulating Probability Unification (FarmCPU) statistical method, identified five associated single nucleotide polymorphisms (SNPs) for disease severity and two SNPs for biomass. The SNPs were found on Pv03, Pv07, Pv08, Pv10, and Pv11. One candidate gene for disease reaction near a SNP on Pv03 codes for a peroxidase, and two candidates associated with biomass SNPs were a 2-alkenal reductase gene cluster on Pv10 and a Pentatricopeptide repeat domain on Pv11. Bean lines utilized in the study were ranked by genomic estimated breeding values (GEBV) for disease severity, biomass, and the root architecture traits, and the observed and predicted values had high to moderate correlations. Cross validation of genomic predictions showed slightly lower correlational accuracy. Bean lines with the highest GEBV were among the most resistant, but did not necessarily rank at the very top numerically. This study provides information on the relationship of root architecture traits to root rot disease reaction. Snap bean lines with genetic merit for genomic selection were identified and may be utilized in future breeding efforts.