Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat

Abstract

Abstract Barley yellow dwarf (BYD) is one of the major viral diseases of cereals. Phenotyping BYD 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, i) evaluate the use of high-throughput phenotyping (HTP) from unmanned aerial systems to improve BYD assessment and selection, ii) identify genomic regions associated with BYD resistance, and iii) evaluate genomic prediction models ability to predict BYD resistance. Up to 107 wheat lines were phenotyped during each of five field seasons under both insecticide treated and untreated plots. Across all seasons, BYD severity was lower with the insecticide treatment and plant height (PTHTM) and grain yield (GY) showed increased values relative to untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying resistance gene Bdv2 on chromosome 7DL. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for resistance on chromosome 5AS. Given the variable heritability of the trait (0.211 – 0.806), we obtained relatively good predictive ability for BYD severity ranging between 0.06 – 0.26. Including Bdv2 on the predictive model had a large effect for predicting BYD but almost no effect for PTHTM and GY. This study was the first attempt to characterize BYD using field-HTP and apply GS to predict the disease severity. These methods have the potential to improve BYD characterization and identifying new sources of resistance will be crucial for delivering BYD resistant germplasm.

Plant phenotyping relevance

UASによるフィールド高スループットフェノタイピングを用いたBYD病徴評価が研究目的の中心であり、植物の病害状態を測定・予測する方法を実質的に適用している。

Code and data availability

The supplied blocks describe BYD phenotyping, UAS image collection, and GWAS/GS analyses, but contain no public deposit of the paper's phenotype datasets, images, or author analysis code. The only URL mentioned (uavmissionplanner.netlify.app) is a generic third-party flight-planning tool, not a paper-specific asset. No

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