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Quantitative trait loci for flowering time and inflorescence architecture in rose

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Abstract

The pattern of development of the inflorescence is an important characteristic in ornamental plants, where the economic value is in the flower. The genetic determinism of inflorescence architecture is poorly understood, especially in woody perennial plants with long life cycles. Our objective was to study the genetic determinism of this characteristic in rose. The genetic architectures of 10 traits associated with the developmental timing and architecture of the inflorescence, and with flower production were investigated in a F 1 diploid garden rose population, based on intensive measurements of phenological and morphological traits in a field. There were substantial genetic variations in inflorescence development traits, with broad-sense heritabilities ranging from 0.82 to 0.93. Genotypic correlations were significant for most (87%) pairs of traits, suggesting either pleiotropy or tight linkage among loci. However, non-significant and low correlations between some pairs of traits revealed two independent developmental pathways controlling inflorescence architecture: (1) the production of inflorescence nodes increased the number of branches and the production of flowers; (2) internode elongation connected with frequent branching increased the number of branches and the production of flowers. QTL mapping identified six common QTL regions (cQTL) for inflorescence developmental traits. A QTL for flowering time and many inflorescence traits were mapped to the same cQTL. Several candidate genes that are known to control inflorescence developmental traits and gibberellin signaling in Arabidopsis thaliana were mapped in rose. Rose orthologues of FLOWERING LOCUS T (RoFT), TERMINAL FLOWER 1 (RoKSN), SPINDLY (RoSPINDLY), DELLA (RoDELLA), and SLEEPY (RoSLEEPY) co-localized with cQTL for relevant traits. This is the first report on the genetic basis of complex inflorescence developmental traits in rose.

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Acknowledgments

We thank Drs. Evelyne Costes, Vincent Segura, Damien Fumey (INRA Montpellier), Patrick Favre, Philippe Morel, and Gilles Galopin (SAGAH, INRA Angers) for their helpful advice on plant measurements, Charles-Eric Durel, Sylvain Gaillard, Fabrice Dupuis, and Alix Pernet (GenHort, INRA Angers) for statistical analyses, Arnaud Remay, Sébastien Pineau, and the other members of GenHort INRA Angers and Biogenouest® for genetic experiments. Dr. Thomas Debener (University of Hannover) kindly gave us primer information on rose SSR. Dr. Shogo Matsumoto and the members of the Laboratory of Horticultural Science, Nagoya University helped KK write the MS. The authors gratefully acknowledge Daphne Goodfellow for correcting the English. This work was supported by grants from the Département de Génétique et d’Amélioration des Plantes, INRA and Région Pays de la Loire.

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Correspondence to Fabrice Foucher.

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Communicated by H. Nybom.

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122_2010_1476_MOESM2_ESM.ppt

Scatter plots between the date of first flowering (D1Flower) in different years for the 98 F 1 hybrids derived from the cross The Fairy (TF) × a hybrid of R. wichurana (RW). Average D1Flower of three replicated plants were calculated for each genotype in each year, and these are plotted. Pearson’s product-moment correlation coefficient r and its significant level (***P < 0.0001) are also shown. (PPT 403 kb)

122_2010_1476_MOESM3_ESM.ppt

Scatter plots of 9 inflorescence traits in 2 years for the 98 F 1 hybrids derived from the cross The Fairy (TF) × a hybrid of R. wichurana (RW). Average trait values were calculated for each genotype in each year, and these are plotted. Pearson’s product-moment correlation coefficient r and its significant level (***P < 0.0001) are also shown. NV1 (Number of nodes on VEG1, vegetative part of 1st order shoot), NF1 (Number of nodes on INF1, inflorescence part of 1st order shoot), NF2 (Number of nodes on INF2, the longest 2nd order shoot of inflorescence), LV1 (Average internode length of VEG1), LF1 (Average internode length of INF1), LF2 (Average internode length of INF2), NBF2 (Number of 3rd order shoots of INF2), BIF2 (Percentage of lateral meristems on INF2 that develop into 3rd order shoots), FLW (Total number of flowers produced by INF1). (PPT 80 kb)

122_2010_1476_MOESM4_ESM.ppt

Segregation patterns of inflorescence architecture among F 1 hybrids derived from the cross The Fairy (TF) × a hybrid of R. wichurana (RW). Scatter plots of least-square means of inflorescence traits and illustrations of inflorescence parts of 1st order shoots (INF1) are shown. In the illustrations, thick solid lines indicate internodes, and circles with thin lines indicate flowers with floral stalks. For simplicity, only the longest 2nd order shoots (INF2) are illustrated in (a) and (b), whereas in (c) all shoots are shown. (a) Numbers of inflorescence nodes of 1st order shoots (NF1) were tightly correlated with those of 2nd order shoots (NF2). NF1 and NF2 did not change independently. (b) Similarly, lengths of inflorescence internodes of 1st order shoots (LF1) were tightly correlated with those of 2nd order shoots (LF2). (c) In contrast, NF and LF were not significantly correlated, therefore there were two independent axes of morphological variations of inflorescence. Furthermore, increasing LF was linked with an increase in branching intensity of 2nd order shoots (BIF2) and was thus related to the increase in flower production (FLW). Increasing NF also resulted in an increase in FLW. Therefore, the genetic variation of FLW was characterized by the two-dimensional morphospace with independent developmental pathways, related to NF and LF. (PPT 347 kb)

122_2010_1476_MOESM5_ESM.ppt

Genomic positions of QTLs detected on the linkage groups of the integrated ‘The Fairy’ × ‘a hybrid of R. wichurana’ map (TF × RW) by multiple QTL mapping (MQM) for the least-square means of 10 inflorescence developmental traits. QTLs are represented by boxes whose length represents the LOD-1 confidence interval, and extended lines represent the LOD-2 confidence interval. For trait abbreviation, see Table 3 (PPT 688 kb)

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Kawamura, K., Hibrand-Saint Oyant, L., Crespel, L. et al. Quantitative trait loci for flowering time and inflorescence architecture in rose. Theor Appl Genet 122, 661–675 (2011). https://doi.org/10.1007/s00122-010-1476-5

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