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ARS Home » Pacific West Area » Pullman, Washington » Grain Legume Genetics Physiology Research » Research » Research Project #423588

Research Project: Enhanced Disease and Abiotic Stress Resistance in Edible Legumes

Location: Grain Legume Genetics Physiology Research

Project Number: 2090-21220-001-000-D
Project Type: In-House Appropriated

Start Date: Apr 25, 2013
End Date: Apr 24, 2018

Objective 1: Apply new knowledge of genetic inheritance and genomic analyses to accelerate breeding and for improved understanding of major genes conditioning resistance/tolerance to: white mold, Fusarium and Aphanomyces root rots, bacterial blights, Bean leaf roll virus, the slow darkening trait, and agronomic performance, including biological nitrogen fixation and drought. Objective 2: Breed, evaluate, and release dry and fresh green pea, lentil, and dry bean (kidney, pink, and pinto) germplasm with combined disease resistance, desirable seed quality attributes (slow darkening trait), and improved agronomic performance.

Edible legumes (common bean, chickpea, lentil, and pea) are high value crops that provide growers with crop options for the short season growing regions in the US. Production losses resulting from diseases caused by pathogens and abiotic stresses (drought, low soil fertility) cost growers millions in lost revenue annually. Incorporation of genetic resistance and stress tolerance traits are needed to minimize yield and quality loss caused by disease and abiotic stresses, and to reduce dependency on pesticides and fertilizers. The breeding of new edible legume germplasm and cultivars with improved resistance to diseases or abiotic stresses will be accomplished by the systematic evaluation of germplasm from different countries, interspecific lines, and core and base collections in repositories to identify new resistance sources. Once new sources of resistance are identified, inheritance studies including QTL analysis and genomic characterization will be conducted to understand the resistance source and develop breeding strategies to utilize the resistance. Genomic advances such as: the release of the annotated common bean whole genome sequence; availability of other reference genomes for synteny; abundance of genetic markers; and improved sequencing techniques will be used to characterize resistance traits. Phenotypic data will be obtained to characterize major genes conditioning resistance traits from appropriate genetic populations. Traditional and marker-assisted breeding strategies will be used to develop new cultivars with enhanced resistance to diseases, drought, and low soil fertility to ensure sustainable productivity. Genomic markers in the form of SNP arrays will be used to identify genes/QTL which condition resistance to targeted diseases and abiotic stresses segregating in different populations. Available and developed bi-parental recombinant inbred line populations and germplasm diversity panels will be used for composite interval mapping and association mapping, respectively. Genome-wide association studies will provide a new approach for identifying gene/QTL in common bean. Two fully gene-annotated, whole genome reference sequences will be used for physical mapping and validating genetic map positions. The physical map positions of the resistance-linked markers will be used to find SSR, indel, SSCP, and other sequence based-markers from associated whole genome sequence for the purpose of marker-assisted selection. Genetic maps will be developed using MapMaker, MapDisto, or JoinMap, and QTL analysis will be performed with QTL Cartographer, QGene, or other related software. Selective mapping and whole mapping approaches will be used to identify major-effect QTL associated with expression of disease and abiotic stress resistance. Principal components analysis will be used to determine population structure. Identity-by-descent and identity-by-state estimates will be calculated to measure genotype relatedness and the cofactors will be fit in regression models and evaluated using GAPIT. The best mixed models accounting for structure and genotype relatedness will be determined using Bayesian Information Criterion using GAPIT.