(BCMV) is a major disease in common bean (
L.). Host plant resistance is the most effective strategy to minimize crop damage against BCMV and the related
(BCMNV). To facilitate breeding for ...resistance, we sought to identify candidate genes and develop markers for the
gene and the unknown gene with which it interacts. Genome-wide association study (GWAS) of the Durango Diversity Panel (DDP) identified a peak region for
on chromosome Pv11. Haplotype mapping narrowed the
genomic interval and identified Phvul.011G092700, a vacuolar protein-sorting 4 (Vps4) AAA+ ATPase endosomal sorting complexes required for transport (ESCRT) protein, as the
candidate gene. The race Durango Phvul.011G092700 gene model,
, contains a 10-kb deletion, while the race Mesoamerican
consists of a single nucleotide polymorphism (SNP) deletion. Each mutation introduces a premature stop codon, and they exhibit the same interaction with the pathogroups (PGs) tested. Phvul.005G125100, another Vps4 AAA+ ATPase ESCRT protein, was identified as the candidate gene for the new recessive
gene, and the recessive allele is likely an amino acid substitution in the microtubule interacting and transport (MIT) domain. The two Vps4 AAA+ ATPase ESCRT proteins exhibit high similarity to the
Cucsa.385040 candidate gene associated with recessive resistance to
in cucumber.
alone has no resistance effect but, when combined with
, provides resistance to BCMV (except PG-V) but not BCMNV, and, when combined with
, provides resistance to BCMV (except BCMV PG-VII) and BCMNV. So instead of different resistance alleles (i.e.,
and
), there is only
with a differential reaction based on whether it is combined with
or
, which are tightly linked in repulsion. The new tools and enhanced understanding of this host-virus pathogen interaction will facilitate breeding common beans for resistance to BCMV and BCMNV.
•Remote sensing techniques were evaluated for dry bean high-throughput plant phenotyping (HTPP).•Vegetation indices (VIs) were extracted from unmanned aerial system and satellite-based images.•VIs ...exhibited strong relationship with biomass and seed yield at pod development stages.•In addition, a new method for normalizing multiple thermal images was proposed.•With multispectral sub-meter spatial resolution satellite imagery, HTPP is feasible.
Dry bean breeding programs are crucial to improve the productivity and resistance to biotic and abiotic stress. Phenotyping is a key process in breeding that refers to crop trait evaluation. In recent years, high-throughput plant phenotyping methods are being developed to increase the accuracy and efficiency for crop trait evaluations. In this study, aerial imagery at different resolutions were evaluated to phenotype crop performance and phenological traits using genotypes from two breeding panels, Durango Diversity Panel (DDP) and Andean Diversity Panel (ADP). The unmanned aerial system (UAS) based multispectral and thermal data were collected for two seasons at multiple time points (about 50, 60 and 75 days after planting/DAP in 2015; about 60 and 75 DAP in 2017). Four image-based features were extracted from multispectral images. Among different features, normalized difference vegetation index (NDVI) data were found to be consistently highly correlated with performance traits (above ground biomass, seed yield), especially during imaging at about 60–75 DAP (early pod development). Overall, correlations were higher using NDVI in ADP than DDP with biomass (r = −0.67 to −0.91 in ADP; r = −0.55 to −0.72 in DDP) and seed yield (r = 0.51 to 0.73 in ADP; r = 0.42 to 0.58 in DDP) at about 60 and 75 DAP. For thermal data, a temperature data normalization (utilizing common breeding plots in multiple thermal images) was implemented and the MEAN plot temperatures generally correlated significantly with biomass (r = 0.28–0.88). Finally, lower resolution satellite images (0.05–5 m/pixel) using UAS data was simulated and image resolution beyond 50 cm was found to reduce the relationship between image features (NDVI) and performance variables (biomass, seed yield). Four different high resolution satellite images: Pleiades-1A (0.5 m), SPOT 6 (1.5 m), Planet Scope (3.0 m), and Rapid Eye (5.0 m) were acquired to validate the findings from the UAS data. The results indicated sub-meter resolution satellite multispectral imagery showed promising application in field phenotyping, especially when the genotypic responses to stress is prominent. The correlation between NDVI extracted from Pleiades-1A images with seed yield (r = 0.52) and biomass (r = −0.55) were stronger in ADP; where the strength in relationship reduced with decreasing satellite image resolution. In future, we anticipate higher spatial and temporal resolution data achieved with low-orbiting satellites will increase applications for high-throughput crop phenotyping.
White mold, caused by the fungus Sclerotinia sclerotiorum (Lib.) de Bary, is a major disease that limits common bean production and quality worldwide. The host-pathogen interaction is complex, with ...partial resistance in the host inherited as a quantitative trait with low to moderate heritability. Our objective was to identify meta-QTL conditioning partial resistance to white mold from individual QTL identified across multiple populations and environments. The physical positions for 37 individual QTL were identified across 14 recombinant inbred bi-parental populations (six new, three re-genotyped, and five from the literature). A meta-QTL analysis of the 37 QTL was conducted using the genetic linkage map of Stampede x Red Hawk population as the reference. The 37 QTL condensed into 17 named loci (12 previously named and five new) of which nine were defined as meta-QTL WM1.1, WM2.2, WM3.1, WM5.4, WM6.2, WM7.1, WM7.4, WM7.5, and WM8.3. The nine meta-QTL had confidence intervals ranging from 0.65 to 9.41 Mb. Candidate genes shown to express under S. sclerotiorum infection in other studies, including cell wall receptor kinase, COI1, ethylene responsive transcription factor, peroxidase, and MYB transcription factor, were found within the confidence interval for five of the meta-QTL. The nine meta-QTL are recommended as potential targets for MAS for partial resistance to white mold in common bean.
Bean common mosaic necrosis virus (BCMNV) is a major disease in common bean (
Phaseolus vulgaris
L.). Host plant resistance is the primary disease control. We sought to identify candidate genes to ...better understand the host-pathogen interaction and develop tools for marker-assisted selection (MAS). A genome-wide association study (GWAS) approach using 182 lines from a race Durango Diversity Panel (DDP) challenged by BCMNV isolates NL-8 Pathogroup (PG)-III and NL-3 (PG-VI), and genotyped with 1.26 million
single-nucleotide polymorphisms
(SNPs), revealed significant peak regions on chromosomes Pv03 and Pv05, which correspond to
bc-1
and
bc-u
resistance gene loci, respectively. Three candidate genes were identified for NL-3 and NL-8 resistance. Side-by-side receptor-like protein kinases (RLKs), Phvul.003G038700 and Phvul.003G038800 were candidate genes for
bc-1
. These RLKs were orthologous to linked RLKs associated with virus resistance in soybean (
Glycine max
). A basic Leucine Zipper (bZIP) transcription factor protein is the candidate gene for
bc-u
. bZIP protein gene Phvul.005G124100 carries a unique non-synonymous mutation at codon 14 in the first exon (Pv05: 36,114,516 bases), resulting in a premature termination codon that causes a nonfunctional protein. SNP markers for
bc-1
and
bc-u
and new markers for
I
and
bc-3
genes were used to genotype the resistance genes underpinning BCMNV phenotypes in the DDP, host group (HG) differentials, and segregating F
3
families. Results revealed major adjustments to the current host-pathogen interaction model: (i) there is only one resistance allele
bc-1
for the
Bc-1
locus, and differential expression of the allele is based on presence vs. absence of
bc-u
; (ii)
bc-1
exhibits dominance and incomplete dominance; (iii)
bc-1
alone confers resistance to NL-8; (iv)
bc-u
was absent from HGs 2, 4, 5, and 7 necessitating a new gene symbol
bc-u
d
to reflect this change; (v)
bc-u
d
alone delays susceptible symptoms, and when combined with
bc-1
enhanced resistance to NL-3; and (vi)
bc-u
d
is on Pv05, not Pv03 as previously thought. These candidate genes, markers, and adjustments to the host-pathogen interaction will facilitate breeding for resistance to BCMNV and related Bean common mosaic virus (BCMV) in common bean.
ABSTRACT
Common bean (Phaseolus vulgaris L.) is an important crop grown for household revenue, food, and nutrition security in many parts of the world, especially in Africa and Latin America. ...Anthracnose caused by Colletotrichum lindemuthianum is a major disease of common bean globally. The objective of this study was to determine the response of selected pinto bean genotypes to seven races of C. lindemuthianum the causative fungus for anthracnose. A total of 56 pinto bean genotypes and three checks were evaluated for resistance to C. lindemuthianum races 51, 65, 73, 247, 253, 263, and 1085. Significant differences were observed among the 56 pinto genotypes in their reaction to the seven races, which was generally skewed towards susceptibility except for races 51 and 73. There was no genotype that was resistant to all seven races. In general, the genotypes that showed resistance to most of the races were those that carried Co‐42, which highlighted the importance of this locus to anthracnose resistance in pinto beans. Three genotypes—NDZ14006‐4, NDZ14110‐4, and NDZ14043—showed superior resistance (resistant to six of the seven races).
Key message
R-BPMV
is located within a recently expanded TNL cluster in the
Phaseolus
genus with suppressed recombination and known for resistance to multiple pathogens including potyviruses ...controlled by the
I
gene.
Bean pod mottle virus
(BPMV) is a comovirus that infects common bean and legumes in general. BPMV is distributed throughout the world and is a major threat on soybean, a closely related species of common bean. In common bean, BAT93 was reported to carry the
R-BPMV
resistance gene conferring resistance to BPMV and linked with the
I
resistance gene. To fine map
R-BPMV
, 182 recombinant inbred lines (RILs) derived from the cross BAT93 × JaloEEP558 were genotyped with polymerase chain reaction (PCR)-based markers developed using genome assemblies from G19833 and BAT93, as well as BAT93 BAC clone sequences. Analysis of RILs carrying key recombination events positioned
R-BPMV
to a target region containing at least 16 TIR-NB-LRR (TNL) sequences in BAT93. Because the
I
cluster presents a suppression of recombination and a large number of repeated sequences, none of the 16 TNLs could be excluded as
R-BPMV
candidate gene. The evolutionary history of the TNLs for the
I
cluster were reconstructed using microsynteny and phylogenetic analyses within the legume family
.
A single
I
TNL was present in
Medicago truncatula
and lost in soybean, mirroring the absence of complete BPMV resistance in soybean. Amplification of TNLs in the
I
cluster predates the divergence of the
Phaseolus
species, in agreement with the emergence of
R-BPMV
before the separation of the common bean wild centers of diversity. This analysis provides PCR-based markers useful in marker-assisted selection (MAS) and laid the foundation for cloning of
R-BPMV
resistance gene in order to transfer the resistance into soybean.
Bean common mosaic virus (BCMV) and bean common mosaic necrosis virus (BCMNV) have a damaging impact on global common bean (Phaseolus vulgaris L.) cultivation, causing potential yield losses of over ...80%. The primary strategy for controlling these viruses is through host plant resistance. This research aimed to identify and validate structural variations for the bc‐ud gene as revealed by long‐read sequencing, develop an efficient DNA marker to assist selection of bc‐ud in snap and dry beans, and examine the interactions between the bc‐ud allele and other BCMV resistance genes. A gene (Phvul.005G125100) model on chromosome Pv05, encoding a vacuolar protein‐sorting 4 (Vps4) AAA+ ATPase endosomal sorting complexes required for transport (ESCRT) protein, was identified as the best candidate gene for bc‐ud. An 84‐bp repetitive insertion variant within the gene, exhibited 100% co‐segregation with the bc‐ud resistance allele across 264 common bean accessions. The 84‐bp repetitive insertion was labeled with an indel marker IND_05_36225873 which was useful for tracking the bc‐ud allele across diverse germplasm. A different single nucleotide polymorphism variant within the same candidate gene was associated with the bc‐4 gene. Segregation in F2 populations confirmed bc‐ud and bc‐4 were alleles, so bc‐4 was renamed bc‐ur to fit gene nomenclature guidelines. The interactions of bc‐ud and bc‐ur with other resistance genes, such as bc‐1 (receptor‐like kinase on Pv03) and bc‐2 (Vps4 AAA+ ATPase ESCRT protein on Pv11), validated gene combinations in the differential “host groups” effective against specific BCMV/BCMNV “pathogroups.” These findings increase our understanding of the Bc‐u locus, and enhance our ability to develop more resilient bean varieties through marker‐assisted selection, reducing the impact of BCMV and BCMNV.
Core Ideas
The Bc‐u locus contains multiple recessive resistance alleles (bc‐ud and bc‐ur).
A gene encoding vacuolar protein‐sorting 4 AAA+ ATPase endosomal sorting complexes required for transport protein on Pv05 is the candidate gene for Bc‐u.
Different causal mutations within the candidate gene underlie bc‐ud and bc‐ur alleles.
White mold (WM) is a major disease in common bean (
Phaseolus vulgaris
L.), and its complex quantitative genetic control limits the development of WM resistant cultivars. WM2.2, one of the nine ...meta-QTL with a major effect on WM tolerance, explains up to 35% of the phenotypic variation and was previously mapped to a large genomic interval on Pv02. Our objective was to narrow the interval of this QTL using combined approach of classic QTL mapping and QTL-based bulk segregant analysis (BSA), and confirming those results with Khufu
de novo
QTL-seq. The phenotypic and genotypic data from two RIL populations, ‘Raven’/I9365-31 (R31) and ‘AN–37’/PS02–029C–20 (Z0726-9), were used to select resistant and susceptible lines to generate subpopulations for bulk DNA sequencing. The QTL physical interval was determined by considering overlapping interval of the identified QTL or peak region in both populations by three independent QTL mapping analyses. Our findings revealed that meta-QTL WM2.2 consists of three regions, WM2.2a (4.27-5.76 Mb; euchromatic), WM 2.2b (12.19 to 17.61 Mb; heterochromatic), and WM2.2c (23.01-25.74 Mb; heterochromatic) found in both populations. Gene models encoding for gibberellin 2-oxidase 8, pentatricopeptide repeat, and heat-shock proteins are the likely candidate genes associated with WM2.2a resistance. A TIR-NBS-LRR class of disease resistance protein (Phvul.002G09200) and LRR domain containing family proteins are potential candidate genes associated with WM2.2b resistance. Nine gene models encoding disease resistance protein pathogenesis-related thaumatin superfamily protein and disease resistance-responsive (dirigent-like protein) family protein etc found within the WM2.2c QTL interval are putative candidate genes. WM2.2a region is most likely associated with avoidance mechanisms while WM2.2b and WM2.2c regions trigger physiological resistance based on putative candidate genes.
The genetic improvement of economically important production traits of dry bean (
L.), for geographic regions where production is threatened by drought and high temperature stress, is challenging ...because of the complex genetic nature of these traits. Large scale SNP data sets for the two major gene pools of bean, Andean and Middle American, were developed by mapping multiple pools of genotype-by-sequencing reads and identifying over 200k SNPs for each gene pool against the most recent assembly of the
genome sequence. Moderately sized
ean
biotic
tress
valuation (BASE) panels, consisting of genotypes appropriate for production in Central America and Africa, were assembled. Phylogenetic analyses demonstrated the BASE populations represented broad genetic diversity for the appropriate races within the two gene pools. Joint mixed linear model genome-wide association studies with data from multiple locations discovered genetic factors associated with four production traits in both heat and drought stress environments using the BASE panels. Pleiotropic genetic factors were discovered using a multi-trait mixed model analysis. SNPs within or near candidate genes associated with hormone signaling, epigenetic regulation, and ROS detoxification under stress conditions were identified and can be used as genetic markers in dry bean breeding programs.
•Remote sensing is useful for high-throughput field phenotyping in legume crops.•Average GNDVI data at mid-pod fill stage was significantly correlated with seed yield.•Average GNDVI data at flowering ...and mid-pod fill was significantly correlated with biomass rating.•Remote sensing data was useful in evaluating drought stress effects on different genotypes.
Phenotyping traits in large field crop trials with numerous breeding lines is an arduous task. Unmanned aerial vehicle (UAV) based remote sensing is currently being investigated for high-throughput agricultural field phenotyping applications. The system is conducive for rapid assessment of crop response to the environment, at a desired spatio-temporal resolution. Therefore, objective of this study was to evaluate such technology towards monitoring responses of dry bean lines to drought and low nitrogen stress (i.e., two trials and two seasons) under field conditions. A semi-automated image processing protocol was developed to extract features such as: (i) average green normalized difference vegetation index (GNDVI); and (ii) canopy area (total number of plant pixels) from individual plots. The data were acquired at mid-pod fill and late-pod fill growth stages in 2014 season, and at flowering, mid-pod fill, and late-pod fill growth stages in 2015 season. The relationships between remotely sensed image features with that of crop response variables such as seed yield, days to flowering, days to harvest maturity, days to seed fill, and biomass rating (for drought trial only) were assessed temporally. Overall, in drought experiment, both average GNDVI and canopy area were significantly correlated with seed yield in all trials at 5% level of significance. The average GNDVI and canopy area at flowering growth stages and average GNDVI at mid-pod fill stage were consistently highly correlated (r > 0.73) with seed yield. The average GNDVI at flowering (r of −0.54 to −0.73) and mid-pod fill (r of −0.52 to −0.73) stages was highly correlated with biomass rating. Thus, average GNDVI could possibly be used as a viable phenotype for capturing biomass differences as well. A pilot thermal imaging of the sample breeding plots in drought trials also indicated its potential in capturing the temperature differences resulting from stress. For the nitrogen stress experiment, the correlations between remotely sensed image features and response variables were lower than in the drought experiment. The nitrogen from vegetative growth did not effeciently partition into seed production, which could have resulted in low correlations.
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