Multi-environment GWAS uncovers markers associated to biotic stress response and genotype-by-environment interactions in stone fruit trees

Abstract

While breeding for improved immunity is essential to achieve sustainable fruit production, it also requires to account for genotype-by-environment interactions (G × E), which still represent a major challenge. To tackle this issue, we conducted a comprehensive study to identify genetic markers with main and environment-specific effects on pest and disease response in peach (Prunus persica) and apricot (Prunus armeniaca). Leveraging multienvironment trials (MET), we assessed the genetic architecture of resistance and tolerance to seven major pests and diseases through visual scoring of symptoms in naturally infected core collections, repeated within and between years and sites. We applied a series of genome-wide association models (GWAS) to both maximum of symptom severity and kinetic disease progression. These analyses lead to the identification of environment-shared quantitative trait loci (QTLs), environment-specific QTLs, and interactive QTLs with antagonist or differential effects across environments. We mapped 60 high-confidence QTLs encompassing a total of 87 candidate genes involved in both basal and host-specific responses, mostly consisting of the Leucine-Rich Repeat Containing Receptors (LRR-CRs) gene family. The most promising disease resistance candidate genes were found for peach leaf curl on LG4 and for apricot and peach rust on LG2 and LG4. These findings underscore the critical role of G × E in shaping the phenotypic response to biotic pressure, especially for blossom blight. Last, models including dominance effects revealed 123 specific QTLs, emphasizing the significance of non-additive genetic effects, therefore warranting further investigation. These insights will support the development of marker-assisted selection to improve the immunity of Prunus varieties in diverse environmental conditions.

Acknowledgements

This work was supported by funding through LabEx AGRO 2011-LABX-002 (under I-Site Muse framework) coordinated by Agropolis Foundation (project ID: 2002-030), the INRAE Department for Plant Genetics and Breeding, the France AgriMer CASDAR Project ‘RésiDiv’ (project ID: 6846752), and the EU Horizon Innovation Actions InnOBreed n°101061028. We are thankful to the two experimental research stations SEFRA and SICA-Centrex, to the INRAE experimental unit A2M, and to the INRAE research experimental unit UERI of Gotheron for the maintenance of the two core collections. We thank Thierry Pascal for initiating the peach collection. We also thank Freddy Combe (INRAE, UERI Gotheron), Amandine Fleury (INRAE, UERI Gotheron), Luana Gillet (INRAE, UR GAFL), and Béatrice Monnet (INRAE, UE A2M) as well as several students for their valuable contribution in field experiment and plant phenotyping. We are also grateful to Guillaume Roch (CEP Innovation) and Guy Clauzel (CEP Innovation) for grafting, pruning, and DNA sampling of the apricot core collection, to Jacques Lagnel (INRAE, UR GAFL) and Frédérique Bitton (INRAE, UR GAFL) for coordinating the bioinformatic processing of the raw data from the resequencing of the apricot core collection, and to Océane Perez (INRAE, UR GAFL) for the preliminary analysis on apricot. We acknowledge Emilie Millet (INRAE, UR GAFL) for sharing her expertise in statistical analysis and providing valuable scientific advice.

Author contributions

M.R., B.Q., and J.M.A. conceived the project and designed the experiment. J.M.A., B.Q., A.B., and L.B. created the core collections. N.D. collected the DNA samples and coordinated the genotyping of the peach core collection. J.M.A. collected the DNA samples and coordinated the resequencing of the apricot core collection. L.H. contributed to the alignment and filtration of the raw data from resequencing. M.S., A.B., L.B., F.G., M.L.P., M.R., V.Si., and S.V. realized the phenotypic observations. M.S. analyzed the data under the supervision of M.R. V.Se. supervised GWAS analyses. L.H. contributed to the identification of candidate genes. M.S. wrote the manuscript. M.R., B.Q., J.M.A., and V.Se. reviewed and edited the manuscript. All authors approved the manuscript.

Data availability

The raw phenotypic and genotypic datasets and the R scripts generated for this study are accessible on the data.gouv public repository at: https://doi.org/10.57745/2HNRN0

Conflict of interest statement

The authors declare no conflict of interest.

Supplementary Data

Supplementary data is available at Horticulture Research online.

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