DOI: 10.5061/dryad.ncjsxkswd
title: Data from: Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat

description: 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.
description: 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.