Data from: Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat
Cite this dataset
Silva, Paula et al. (2022). Data from: Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat [Dataset]. Dryad. https://doi.org/10.5061/dryad.ncjsxkswd
Abstract
Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance, and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resis- tance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a poten- tially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211–0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other ob- served traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply geno- mic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm.
Methods
A total of 381 different wheat genotypes were characterized for BYD resistance, including 30 wheat cultivars and 351 advanced breeding lines in field nurseries over 5 years. Nurseries for BYD field screening were conducted during 5 consecutive wheat seasons (2015–2016 to 2019–2020) and established for natural BYD infections by planting in mid-September, about 3 weeks earlier than the normal planting window in mid-September. The experimental unit was 1.5 m × 2.4 m with a 6-row plot on 20 cm row spacing.
A split-plot field design with 2 or 3 replications was used where the main plot was insecticide treatment, and the split-plot was the wheat genotype. For the treated replications the seed was treated at planting with Gaucho XT followed with foliar insecticide applications starting approximately 2–3 weeks after planting through heading. For the control insecticide treatment (untreated), the seed was treated with Raxil MD (fungicide) and no foliar insecticide applications were applied. Individual plots were assessed for: BYD severity characterized as the typical visual symptoms of yellowing or purpling on leaves using a 0–100% visual scale, manual plant height, grain yield, digital plant height, and NDVI.
HTP data were collected using a ground-based proximal sensing platform or a UAS. Seasons 2015–2016 and 2016–2017 were characterized by the ground platform as described in Barker et al. (2016) and Wang et al. (2018). For the other 3 seasons, we used a quadcopter DJI Matrice 100 (DJI, Shenzhen, China) carrying a MicaSenseRedEdge-M multispectral camera (MicaSense Inc., USA). The HTP data were collected on multiple dates throughout the growth cycle from stem elongation to ripening (GS 30–90; Zadoks et al. 1974). Flight plans were created using CSIRO mission planner application and missions were executed using the Litchi Mobile App (VC Technology Ltd., UK; https://uavmissionplanner.netlify.app). All UAS flights were set at 20 m above ground level at 2 m/s and conducted within 2 h of solar noon. To improve the geospatial accuracy of orthomosaic images, white square tiles with a dimension of 0.30 m × 0.30 m were used as ground control points and were uniformly distributed in the field experiment before image acquisition and surveyed to centimeter-level resolution using the Emlid REACH RS+ Real-Time Kinematic Global Navigation Satellite System unit (Emlid Ltd, Hong Kong, China). Raw collected data was used to calculate best linear unbiased estimators (BLUEs) and predictors (BLUPs). BLUP values in combination with genotypic data collected through GBS for 346 lines were used to perform GWAS and GS analyses.
Funding
Kansas Wheat Commission, Award: B65336