Open AccessA complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress
Author(s) -
Aoun Nathalie,
Desaint Henri,
Boyrie Léa,
Bonhomme Maxime,
Deslandes Laurent,
Berthomé Richard,
Roux Fabrice
Publication year - 2020
Publication title -
molecular plant pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.945
H-Index - 103
eISSN - 1364-3703
pISSN - 1464-6722
DOI - 10.1111/mpp.12964
Subject(s) - ralstonia solanacearum , biology , genetics , epistasis , genetic architecture , quantitative trait locus , population , context (archaeology) , genetic variation , arabidopsis thaliana , candidate gene , gene , pathogen , paleontology , demography , sociology , mutant
Plant immunity is often negatively impacted by heat stress. However, the underlying molecular mechanisms remain poorly characterized. Based on a genome‐wide association mapping approach, this study aims to identify in Arabidopsis thaliana the genetic bases of robust resistance mechanisms to the devastating pathogen Ralstonia solanacearum under heat stress. A local mapping population was phenotyped against the R. solanacearum GMI1000 strain at 27 and 30 °C. To obtain a precise description of the genetic architecture underlying natural variation of quantitative disease resistance (QDR), we applied a genome‐wide local score analysis. Alongside an extensive genetic variation found in this local population at both temperatures, we observed a playful dynamics of quantitative trait loci along the infection stages. In addition, a complex genetic network of interacting loci could be detected at 30 °C. As a first step to investigate the underlying molecular mechanisms, the atypical meiotic cyclin SOLO DANCERS gene was validated by a reverse genetic approach as involved in QDR to R. solanacearum at 30 °C. In the context of climate change, the complex genetic architecture underlying QDR under heat stress in a local mapping population revealed candidate genes with diverse molecular functions.