Recipient Organization
NORTH DAKOTA STATE UNIV
1310 BOLLEY DR
FARGO,ND 58105-5750
Performing Department
Plant Sciences
Non Technical Summary
As interest in dry bean (Phaseolus vulgaris L.) as a functional and nutritious food increases, breeders must select germplasm based on specific metabolites profiles important to the consumer while maintaining agronomic performance traits important to the grower. As lines with improved functional food properties are released, food stocks with relevant metabolite properties will be valuable to the food industry. This project will utilize genetic populations, genomic tools, and metabolite profiles of dry bean to discover genetic factors associated with an important subclass of metabolites, flavonoids, found in the harvested bean seed. While pulses are in general labeled as healthy because of their flavonoid content, bean seeds have the highest concentrations and variability of flavonoids of any U.S pulse crop. Yet that feature has not been researched in depth despite the fact that flavonoids provide major health benefits as a whole food or as a diet supplement. With that in mind, another project goal is to combine food chemistry, genetics/genomics, and breeding research to discover genetic factors associated with increased flavonoid content in dry bean. Long term application of the result may support the development of flavonoid-enhanced commodity grade dry beans for the dinner table or for use in the supplement industry as an enriched source of flavonoids. While enhancing the nutritional features of dry bean is important, it must not ignore agronomic traits such as white mold. This project will continue its past focus on white mold tolerance by again using recently developed genetic populations and genomic tools, improved disease screening techniques to further discover genetic factors for white mold tolerance. Given that bean is a member of the billion dollars crops club in the US, and North Dakota is the leading bean producer in the US, this researcher will benefit North Dakota citizens by providing research tools that breeders can implement to develop varieties tolerant of the a major production constraint with the improved metabolite profiles.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Goals / Objectives
1: Identify molecular factors controlling metabolite content in common bean2: Identify molecular factors controlling plant disease resistance traits in common bean
Project Methods
Objective 1: Identify molecular factors controlling metabolite content in common bean. To provide a reference for future research, flavonoid content will be measured in five cultivars each from the pinto, medium red, pink, black, cranberry, light red, and red kidney market classes. In addition, varieties from the emerging yellow bean Mayocoba market class, favored in Latin communities in the U.S., and two other yellow bean classes will also be included. Historical and recent releases from each class will be chosen. All cultivars/lines will be members of diversity panels (Middle American Diversity Panel, Durango Diversity Panel, Andean Diversity Panel used by McClean, Miklas, and Osorno for genetic research, and for which fresh seed is available.To determine the variability in flavonoid content as it relates to various seed coat color or patterning genes, 41 introgression lines that contain one to three recessive alleles of color/pattern genes will also be surveyed. The genotype 5-593 is the recurrent parent for all lines, and the introgression lines contain the recessive allele backcrossed to the BC3 level for the following genes: G B V Gy Rk R P T J. If the expression profile (presence or absence; expression level) of a flavonoid differs between 5-593 and an introgression line, it can be concluded that the gene(s) in the recessive state controls the expression of that flavonoid. This will inform our candidate gene selection for cloning. Polyphenol analysis will be performed using a Liquid chromatography-mass spectrometry (LC-MS) system on seed coats from lines described above.Molecular mapping of those genes that control color and pattern in dry bean will utilize a modified version of the introgression mapping approach developed for white mold resistance QTL (41). The mapping of the G gene will be described here as an example of the experimental approach that will applied to other color/patterning genes as well. A reference genome sequence of 5-593 is under development (McClean and Schmutz, unpublished) which will be available for this activity. Introgression lines 5-593 gv (PI 608684) and 5-593 gbv (PI 608685) will be sequenced to a depth of 10X, and the donor of the recessive g allele to those introgression lines (PI 310511) will be sequenced to a depth of 40X. The region of Pv04, where G maps (27), that is polymorphic between 5-593 and the two introgression lines and the donor line, will be considered the location of G. Marker development for G will follow. A total of 41 introgression lines are available (USDA/NPGS repository) with various combination of recessive alleles at the many color/patterning genes. Once the technique described for the G gene is proven effective, the same approach, using other introgression lines with appropriate allelic combinations at a locus, will be applied to discover the genetic location of other color/patterning genes.Objective 2: Identify molecular factors controlling plant disease resistance traits in common bean. Here, the discovery of the physical location of the genetic factors underlying a white mold the WM7.5 QTL will be described in detail as an example of how other WM QTL will be mapped during the project. Three different populations will be used for this analysis: M25 (Montrose/ I9365-25; n=130); R31 (Raven/I9365-31; n=105); and AN (Aztec/ND88-106-04; n=85). New indel markers will be designed to the predicted physical position of WM7.5. Those markers are then used to map the QTL using standard bi-parental mapping techniques. This mapping is predicted to narrow the interval down to ~100 candidate genes. The interval will be further fine-mapped using QTL sequencing (QTL-seq; 43), a modification of the procedure we published recently (41). The modification uses a WM tolerant and susceptible bulks based on phenotypic response and haplotype state versus the traditional QTL-seq approach that only uses phenotypic data.Four different libraries will be created: 1) resistant bulk, 2) susceptible bulk, 3) resistant parent, and 4) susceptible parent. The libraries will be pooled and sequenced to a minimum of 8X per line on a single lane of the Illumina X10 sequencer. Reads will be mapped to the common bean reference genomes (G19833 and UI 111) to develop resistant and susceptible reference assemblies, tolerant and susceptible bulk reads will then be mapped to the two reference assemblies, and finally SNPs will be called. Genomic regions enriched for fixed SNPs are considered the QTL-seq peak. The boundaries of these peaks delineate the region where the candidate gene(s) is located within the genome.Another approach to QTL mapping will involve deep sequencing (~20X) of 15 genotypes (A195, R31-83, Xana, USPT-WM-12, I9365-31, USPT-WM-1, VA19, I9365-25, 11A-29, G122, WMG904-20-3, 29C-40, NY6020-4, SR905, VCP-13) representing the sources of resistance for our cumulative mapping efforts. In addition, the eight parents of the WM-MAGIC population will be resequenced to the same depth. The sequencing reads for each of these lines will be mapped to the G19833 and UI 111 reference genome assemblies. Once QTL intervals are defined by QTL-seq introgression mapping, we can compare the sequence of the gene models within those intervals among the 22 lines to determine which genes are polymorphic and represent the better candidate genes. This gene polymorphism data will add a level of precision to our candidate gene calling.