Sustainable deployment of QTLs conferring quantitative resistance to crops: first lessons from a stochastic model

TL;DR

This paper presents a stochastic model analyzing how different combinations of quantitative trait loci (QTLs) affect the durability of plant disease resistance, providing insights for sustainable crop protection strategies.

Contribution

It introduces a novel stochastic model to study pathogen adaptation to quantitative resistance, highlighting how QTL combinations influence durability.

Findings

Combining QTLs affecting different pathogen traits enhances durability.

Resistance targeting only pathogen reproduction is more durable.

QTLs that slow pathogen spread can further prolong resistance.

Abstract

Quantitative plant disease resistance is believed to be more durable than qualitative resistance, since it exerts less selective pressure on the pathogens. However, the process of progressive pathogen adaptation to quantitative resistance is poorly understood, which makes it difficult to predict its durability or to derive principles for its sustainable deployment. Here, we study the dynamics of pathogen adaptation in response to quantitative plant resistance affecting pathogen reproduction rate and its carrying capacity. We developed a stochastic model for the continuous evolution of a pathogen population within a quantitatively resistant host. We assumed that pathogen can adapt to a host by the progressive restoration of reproduction rate or of carrying capacity, or of both. Our model suggests that a combination of QTLs affecting distinct pathogen traits was more durable if the evolution of repressed traits was antagonistic. Otherwise, quantitative resistance that depressed only pathogen reproduction was more durable. In order to decelerate the progressive pathogen adaptation, QTLs that decrease the pathogen's ability to extend must be combined with QTLs that decrease the spore production per lesion or the infection efficiency or that increase the latent period. Our theoretical framework can help breeders to develop principles for sustainable deployment of quantitative trait loci..