Abstract
Key message
Two environmentally stable QTLs linked to black spot disease resistance in the Rosa wichurana genetic background were detected, in different connected populations, on linkage groups 3 and 5. Co-localization between R-genes and defense response genes was revealed via meta-analysis.
Abstract
The widespread rose black spot disease (BSD) caused by the hemibiotrophic fungus Diplocarpon rosae Wolf. is efficiently controlled with fungicides. However, in the actual context of reducing agrochemical use, the demand for rose bushes with higher levels of resistance has increased. Qualitative resistance conferred by major genes (Rdr genes) has been widely studied but quantitative resistance to BSD requires further investigation. In this study, segregating populations connected through the BSD resistant Rosa wichurana male parent were phenotyped for disease resistance over several years and locations. A pseudo-testcross approach was used, resulting in six parental maps across three populations. A total of 45 individual QTLs with significant effect on BSD resistance were mapped on the male maps (on linkage groups (LG) B3, B4, B5 and B6), and 12 on the female maps (on LG A1, A2, A3, A4 and A5). Two major regions linked to BSD resistance were identified on LG B3 and B5 of the male maps and were integrated into a consensus map built from all three of the male maps. A meta-analysis was used to narrow down the confidence intervals of individual QTLs from three populations by generating meta-QTLs. Two ‘hot spots’ or meta-QTLs were found per LG, enabling reduction of the confidence interval to 10.42 cM for B3 and 11.47 cM for B5. An expert annotation of NBS-LRR encoding genes of the genome assembly of Hibrand et al. was performed and used to explore potential co-localization with R-genes. Co-localization with defense response genes was also investigated.
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Availability of data and material
Genetic maps and QTL tables are available as supplementary data.
Abbreviations
- BSD:
-
Black spot disease
- QTLs:
-
Quantitative trait loci
- MAS:
-
Marker-assisted breeding
- LG:
-
Linkage groups
- RW:
-
Rosa wichurana
- OB:
-
Rosa chinensis ‘Old blush’
- TF:
-
Rosa hybrid ‘The Fairy’
- OB population:
-
Rosa chinensis ‘Old blush’ x Rosa wichurana population
- FW population:
-
Rosa hybrid ‘The Fairy’ x Rosa wichurana population
- HW population:
-
H190 x Rosa wichurana population
- ANOVA:
-
Analysis of variance
- REML:
-
Restricted maximum likelihood
- N.N fit:
-
‘Nearest neighbor fit’
- SSR:
-
Short sequence repeat
- SNP:
-
Single nucleotide polymorphism
- LOD:
-
Logarithm of odds
- WLS:
-
Weighted least square
- AIC:
-
‘Akaike’ information criterion
- CI:
-
Confidence intervals
- cM:
-
Centimorgans
- GO:
-
Gene ontology
- R-gene:
-
Resistance gene
- NP:
-
Number of petals
- SIM:
-
Simple interval mapping
- CIM:
-
Composite interval mapping
- 2p model:
-
Two-part model
- TNL:
-
TIR-NB-LRR
- Nucleotide Binding:
-
NB-encoding
- QDR:
-
Quantitative disease resistance
- QRL:
-
Quantitative resistance loci
- CYP:
-
Cytochromes P450 monooxygenases
- PR:
-
Pathogenesis-related
- TLP:
-
Thaumatin-like protein
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Acknowledgements
This work was financially supported by RFI Objectif Végétal, the BAP department of the ‘Institut National de la Recherche Agronomique et Environnement’ (INRAE) and Région Pays de la Loire (support by the CASDAR project ROGER n° C-2014-06 from the French Ministry of Agriculture, Agrifood and Forestry). The invaluable collaboration and work of the ANAN platform (SFR Quasav) for SSR analysis as well as INRA Horticulture Experimental Facility (Beaucouzé, France), for plant management in experimental fields are gratefully acknowledged. We thank Martin Leduc for his work on OW maps, Kévin Debray for his work on SNP positioning in the genome and Briana Gastaldello for the final proofreading of the article.
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DCLA participated in the phenotyping, performed the genetic and statistical analyses and wrote the paper. VSF, FF and LHSO conceived the AC provided a technical support in the SSR genotyping and participated in the phenotyping. TT coordinated the field trials and participated in the phenotyping. LVE and JB performed the manual annotation and provided revision of the manuscript. YDO provided support in software Biomercator and the meta-analysis approach. FF provided extensive revisions of the manuscript. SP, LHSO and VSF participated in the phenotyping and provided extensive revisions of the manuscript. All authors read the final version of the manuscript and approved its publication.
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Supp. Fig. 1
Evaluation zone and rating scale of black spot disease in rose. From 0 (no symptoms) to 5 (total defoliation of the plant) with a score of 1 for less than 25% of infected leaflets, a score of 2 for infection between 25 and 50%, a score of 3 for infection between 50 and 75% of infected leaflets and 4 for infection of 75% to 100% and partial defoliation (DOCX 5875 kb)
Supp. Fig. 2
Overall mean of black spot disease (BSD) scores for three rose populations over several locations and years under natural infections in field. In the box plots, the boundaries of the box indicate the 25th percentile (on the lower part) and the 75th percentile (on the upper part). A thick line within the box marks the median, and a dot within the box marks the mean. Lines above and below the box indicate the 10th and 90th percentiles. Points above and below the whiskers indicate outliers outside the 10th and 90th percentiles. A: Data description for different years for OW population in Angers, B: Data description for different years for FW population in Angers, C: Data description for different years for HW population in Angers, Bellegarde and Diémoz. A Kruskal-Wallis test was used to compare the scoring years for HW and OW populations, and a Wilcoxon test was used to compare the two scoring years 2014-2018 for FW. For HW population, locations were considered separately. P-values of the tests are displayed on the graphs for all populations (PDF 7 kb)
Supp. Fig. 3
Genetic linkage map of the male parent hybrid, Rosa wichurana, for OW population. Linkage groups (LG) names for the male map (B1 to B7) are placed above the corresponding linkage groups according to Spiller et al. 2011. Locus names are indicated on the right side of each LG. When several markers were mapped at a same position, one or two markers were reported corresponding to the unique loci or markers with different phases. SSR markers are indicated in red. Genetic distances (cM) are indicated on the left side of each LG (PDF 31 kb)
Supp. Fig. 4
Genetic linkage map of the female parent, Rosa chinensis ‘Old Blush’, for OW population. Linkage groups (LG) names for the female map (A1 to A7) are placed above the corresponding linkage groups. Locus names are indicated on the right side of each LG. When several markers were mapped at the same position, one or two markers were reported corresponding to the unique loci or markers with different phases. SSR markers are indicated in red. Genetic distances (cM) are indicated on the left side of each LG (PDF 36 kb)
Supp. Fig. 5
QTL mapping associated with black spot disease (BSD) resistance using a normal model with an CIM analysis for OW population for the male map. Linkage groups are named as follows: “B” for the male map and the number of the linkage group. LOD score for each year and the mean of all years are calculated using a Composite Interval Mapping method (CIM) and are displayed with different colors. The same set of colors is used to represent the α=0.05 LOD threshold for declaring significant QTL based on 1000 permutations (PDF 32 kb)
Supp. Fig. 6
QTL mapping associated with black spot disease resistance using a two-part model approach for non-normally distributed data of FW population for the male map. A and B: LOD curves for the two-part model for 2014 (A) and 2018 (B); LOD.π (penetrance, equivalent to binary model) displayed in blue, LOD.µ (severity, equivalent to normal model for non-spike phenotypes) displayed in green and LOD.π.µ (sum, representing the complete model) displayed in black; LOD thresholds are displayed in red with α=0.05 for declaring significant QTL based on 1000 permutations (PDF 23 kb)
Supp. Fig. 7
QTL mapping associated with black spot disease (BSD) resistance for normally distributed data of HW population using a normal model with CIM analysis for the male map. Linkage groups are named as follow: “B” for the male map and the number of the linkage group. For HW population, BSD was scored in three locations: (A) Angers, (B) Bellegarde and (C) Diémoz. LOD score for each year and the mean of all years were calculated using a Composite Interval Mapping (CIM) method. The same set of colors is used to represent the different scoring years in the different locations. α=0.05 LOD threshold was used for declaring significant QTL based on 1000 permutations (PDF 30 kb)
Supp. Fig. 8
QTL mapping with black spot disease (BSD) resistance using a two-part model approach for non-normally distributed data of HW population for the male map. Linkage groups are named as follows: “B” for the male map and the number of the linkage group. A to C: LOD curves of the two-part model analysis for spike-like distribution of HW BSD scoring years (Angers-2018 (A), Bellegarde-2014 (B) and Diémoz-2013 (C)); LOD.π (penetrance, equivalent to binary model) displayed in blue, LOD.µ (severity, equivalent to normal model for non-spike phenotypes) displayed in green and LOD.π.µ (sum, representing the complete model) displayed in black; LOD thresholds are displayed in red with α=0.05 for declaring significant QTL based on 1000 permutations (PDF 28 kb)
Supp. Fig. 9
QTL mapping with black spot disease resistance using a two-part model approach for non-normally distributed data of FW and HW populations for the female maps. A: LOD curves for the two-part model for FW population using 2018 scoring year in Angers; B: LOD curves for the two-part model for HW population using scoring year 2014 in Bellegarde; LOD.π (penetrance, equivalent to binary model) displayed in blue, LOD.µ (severity, equivalent to normal model for non-spike phenotypes) displayed in green and LOD.π.µ (sum, representing the complete model) displayed in black; LOD thresholds are displayed in red with α=0.05 for declaring significant QTL based on 1000 permutations (PDF 18 kb)
Supp. Fig. 10
Co-localization of the Rdr1 genes cluster with the QTLs on A1 from OW and FW female maps. Linkage groups (LG) names from female genetic maps are placed above the LG and named A1 - “population name” and the corresponding chromosome is placed in the middle. Names of markers are on the right and the genetic distances (in cM) on the left. For the chromosome from the physical map, physical distances are expressed in mega-base (Mb). TNLs genes and clusters (OB2-A, OB-B, Cluster1 and Cluster2, Menz et al. 2020) are shown in black. The markers peaks of each QTL are displayed in grey. The active form of Rdr1 resistance gene (muRdr1A) is located on the cluster2 (Menz et al. 2020). QTLs widest confidence intervals are represented in plain colors on the linkage groups: blue for the OW female map, yellow for the FW female map and green for the equivalent common widest region on the physical map (PDF 13 kb)
Supp. Table 1
Common marker list for all populations for the female and male maps. a Only SSR markers are listed. The markers shared by all three populations are displayed in green; b A1-A7 for the female maps and B1-B7 for the male maps (PDF 254 kb)
Supp. Table 2
NB-encoding genes information and position in Rose genome. Summary of NB-encoding genes identified in manual annotations. Listed are gene ID names, chromosomal affiliation, end and start positions, classification (CC=Coiled Coil; LRR=Leucine Rich Repeat; NBS=Nucleotide Binding Site; TIR=Toll Interleukin Like Receptor); non-canonical domains (i.e., conserved protein domains that are atypical of NBS-LRR proteins); configuration of identified conserved NB-ARC subdomains; and detailed domain configurations, complete with reported e-values from a Pfam analysis (XLSX 64 kb)
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Lopez Arias, D.C., Chastellier, A., Thouroude, T. et al. Characterization of black spot resistance in diploid roses with QTL detection, meta-analysis and candidate-gene identification. Theor Appl Genet 133, 3299–3321 (2020). https://doi.org/10.1007/s00122-020-03670-5
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DOI: https://doi.org/10.1007/s00122-020-03670-5