QTL Mapping for Disease Resistance in Cucumber

The Brassica genus contains abundant economically important vegetable and oilseed crops, which are under threat of diseases caused by fungal, bacterial and viral pathogens. Resistance gene analogues (RGAs) are associated with quantitative and qualitative disease resistance and the identification of candidate RGAs associated with disease resistance is crucial for understanding the mechanism and management of diseases through breeding. The availability of Brassica genome assemblies has greatly facilitated reference-based quantitative trait loci (QTL) mapping for disease resistance. In addition, pangenomes, which characterise both core and variable genes, have been constructed for B. rapa, B. oleracea and B. napus. Genome-wide characterisation of RGAs using conserved domains and motifs in reference genomes and pangenomes reveals their clustered arrangements and presence of structural variations. Here, we comprehensively review RGA identification in important Brassica genome and pangenome assemblies. Comparison of the RGAs in QTL between resistant and susceptible individuals allows for efficient identification of candidate disease resistance genes. However, the reference-based QTL mapping and RGA candidate identification approach is restricted by the under-represented RGA diversity characterised in the limited number of Brassica assemblies. The species-wide repertoire of RGAs make up the pan-resistance gene analogue genome (pan-RGAome). Building a pan-RGAome, through either whole genome resequencing or resistance gene enrichment sequencing, would effectively capture RGA diversity, greatly expanding breeding resources that can be utilised for crop improvement.

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In the Eye of the Beholder: The Effect of Rater Variability and Different Rating Scales on QTL Mapping

Phytopathology ◽

10.1094/phyto-03-10-0087 ◽

2011 ◽

Vol 101 (2) ◽

pp. 290-298 ◽

Cited By ~ 42

Author(s):

Jesse A. Poland ◽

Rebecca J. Nelson

Keyword(s):

Disease Resistance ◽

Qtl Mapping ◽

Disease Severity ◽

Rating Scales ◽

Ordinal Scale ◽

Disease Assessment ◽

True Value ◽

Accuracy And Precision ◽

Rater Variability ◽

The Impact

The agronomic importance of developing durably resistant cultivars has led to substantial research in the field of quantitative disease resistance (QDR) and, in particular, mapping quantitative trait loci (QTL) for disease resistance. The assessment of QDR is typically conducted by visual estimation of disease severity, which raises concern over the accuracy and precision of visual estimates. Although previous studies have examined the factors affecting the accuracy and precision of visual disease assessment in relation to the true value of disease severity, the impact of this variability on the identification of disease resistance QTL has not been assessed. In this study, the effects of rater variability and rating scales on mapping QTL for northern leaf blight resistance in maize were evaluated in a recombinant inbred line population grown under field conditions. The population of 191 lines was evaluated by 22 different raters using a direct percentage estimate, a 0-to-9 ordinal rating scale, or both. It was found that more experienced raters had higher precision and that using a direct percentage estimation of diseased leaf area produced higher precision than using an ordinal scale. QTL mapping was then conducted using the disease estimates from each rater using stepwise general linear model selection (GLM) and inclusive composite interval mapping (ICIM). For GLM, the same QTL were largely found across raters, though some QTL were only identified by a subset of raters. The magnitudes of estimated allele effects at identified QTL varied drastically, sometimes by as much as threefold. ICIM produced highly consistent results across raters and for the different rating scales in identifying the location of QTL. We conclude that, despite variability between raters, the identification of QTL was largely consistent among raters, particularly when using ICIM. However, care should be taken in estimating QTL allele effects, because this was highly variable and rater dependent.

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