Single and Multi-trait GWAS Identify Genetic Factors Associated with Production Traits in Common Bean Under Abiotic Stress Environments.

The genetic improvement of economically important production traits of dry bean (Phaseolus vulgaris 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 P. vulgaris genome sequence. Moderately sized B ean A biotic S tress E 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.

Deploying a Proximal Sensing Cart to Identify Drought-Adaptive Traits in Upland Cotton for High-Throughput Phenotyping.

Field-based high-throughput phenotyping is an emerging approach to quantify difficult, time-sensitive plant traits in relevant growing conditions. Proximal sensing carts represent an alternative platform to more costly high-clearance tractors for phenotyping dynamic traits in the field. A proximal sensing cart and specifically a deployment protocol, were developed to phenotype traits related to drought tolerance in the field. The cart-sensor package included an infrared thermometer, ultrasonic transducer, multi-spectral reflectance sensor, weather station, and RGB cameras. The cart deployment protocol was evaluated on 35 upland cotton (Gossypium hirsutum L.) entries grown in 2017 at Maricopa, AZ, United States. Experimental plots were grown under well-watered and water-limited conditions using a (0,1) alpha lattice design and evaluated in June and July. Total collection time of the 0.87 hectare field averaged 2 h and 27 min and produced 50.7 MB and 45.7 GB of data from the sensors and RGB cameras, respectively. Canopy temperature, crop water stress index (CWSI), canopy height, normalized difference vegetative index (NDVI), and leaf area index (LAI) differed among entries and showed an interaction with the water regime (p < 0.05). Broad-sense heritability (H2) estimates ranged from 0.097 to 0.574 across all phenotypes and collections. Canopy cover estimated from RGB images increased with counts of established plants (r = 0.747, p = 0.033). Based on the cart-derived phenotypes, three entries were found to have improved drought-adaptive traits compared to a local adapted cultivar. These results indicate that the deployment protocol developed for the cart and sensor package can measure multiple traits rapidly and accurately to characterize complex plant traits under drought conditions.