About this Research Topic

Background

In plant breeding and genetics, traits are frequently classified into qualitative and quantitative. A qualitative trait is generally controlled by one or a few genes, whereas a quantitative trait is controlled by several genes. The effect of each of the alleles responsible for a quantitative trait is typically small when compared to the effect of the environment, making the inference of an individual genotype difficult to establish. Quantitative traits are characterized and detected by quantitative trait loci (QTL) and genome-wide association (GWAS) analyses. In the world of disease resistance, quantitative trait loci (QTL) and quantitative resistance loci (QRL) have been identified in several pathosystems.
When breeding for disease resistance, the gene-for-gene theory is a classic case of a qualitative trait controlled by one gene at a time in the host and the pathogen. Gene-for-gene resistance requires that one resistance gene in the host and one avirulence gene in the pathogen interact so that an incompatible disease reaction results in the host, and pathogen infection is prevented. The majority of race-specific (R ) genes follow a classical gene-to-gene hypothesis and exhibit a ‘complete’ resistance.
On the other side of the spectrum, quantitative disease resistance (QDR) is when host plants exhibit a reduced disease reaction but not complete resistance. It is widely recognized that QDR provides long term host defense towards the disease, probably due to multiple genes requiring mutation for resistance breakdown as opposed to single genes as in the case of gene-for-gene resistance. However, QDR has been a longstanding challenge in the development of cultivars with durable resistance and new techniques such as association mapping could complement QTL mapping results. Emerging genetic, metabolomics and genomics tools could speed-up the development of quantitative disease resistance varieties in plants.
It is timely that a collection of reports is assembled to represent achievements in understanding and improving QDR. New technologies provide avenues for measuring QDR in plant breeding populations and new insights on plant-pathogen interactions provide new alternatives for studying QDR.
The objective of this Research Topic is to collate articles updating the status of breeding for quantitative disease resistance (QDR). The interest is to provide an updated view of the science of breeding for QDR as well as the tools that have become available in the development of QDR. Welcomed topics include:

Modern quantitative resistance loci (QRL) mapping populations for QDR in complex diseases (i.e. Association mapping using GWAS, Nested Association Mapping (NAM), Multi-parent Advanced Generation Intercross (MAGIC), Marker-assisted selection)
Virus-inducing gene silencing in plant reverse genomics and gene editing
Pathosystem coexpression network studies
Novel genetic markers for complex diseases validated in breeding populations
Identification of QTL associated with multiple disease resistance
Modeling QDR in breeding populations
Historical breeding and improvement for QDR
New phenotyping tools aimed at increasing resolution in the detection of QRL
From the lab to the field to the farmers: case studies for the implementation of Marker Assisted Selection breeding for QDR
QDR breeding progress in annual crops versus perennial
Machine learning or artificial intelligence based phenotyping
New genomics tools for dissecting/breeding of QDR
New strategies to develop populations for QDR
Genomic selection for QDR.

Please note: all submissions containing quantitative phenotypic data must be obtained from three or more independent test environments, more information is available here in the

Keywords: Quantitative disease resistance, plant breeding, mapping studies

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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