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Identification of co-located QTLs and genomic regions affecting grapevine cluster architecture

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Abstract

Loose cluster architecture is an important aim in grapevine breeding since it has high impact on the phytosanitary status of grapes. This investigation analyzed the contributions of individual cluster sub-traits to the overall trait of cluster architecture. Six sub-traits showed large impact on cluster architecture as major determinants. They explained 57% of the OIV204 descriptor for cluster compactness rating in a highly diverse cross-population of 149 genotypes. Genetic analysis revealed several genomic regions involved in the expression of this trait. Based on the linkage of phenotypic features to molecular markers, QTL calculations shed new light on the genetic determinants of cluster architecture. Eight QTL clusters harbor overlapping confidence intervals of up to four co-located QTLs. A physical projection of the QTL clusters by confidence interval-flanking markers onto the PN40024 reference genome sequence revealed genes enriched in these regions.

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Acknowledgements

We wish to thank Sarina Elser for expert technical assistance. This work was funded by the “Federal Program for Ecologic Landuse and other forms of Sustainable Agriculture” (Bundesprogramm Ökologischer Landbau und andere Formen nachhaltiger Landwirtschaft, BÖLN) of BLE (Bundesanstalt für Landwirtschaft und Ernährung) Federal Office for Agriculture and Food under the title “MATA- Molekulare Analyse der Traubenarchitektur” (Molecular analysis of cluster architecture) FKZ 2811NA056 (www.ble.de).

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  1. Institute for Grapevine Breeding Geilweilerhof, Julius Kuehn Institute, Federal Research Centre of Cultivated Plants, Geilweilerhof, 76833, Siebeldingen, Germany

    Robert Richter, Florian Rist, Reinhard Töpfer & Eva Zyprian

  2. Institute for Crop and Soil Science, Julius Kuehn Institute, Federal Research Centre of Cultivated Plants, Bundesallee 58, 38116, Brunswick, Germany

    Doreen Gabriel

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Online Resource 1

Pictures of the parental types of the cross GF.GA-47-42 × ‘Villard Blanc’. Both varieties showed reduced cluster compactness. A) maternal parent GF.GA-47-42 B) paternal parent ‘Villard Blanc’. (PDF 358 kb)

Online Resource 2

Comparison of the correlation among cluster architecture sub-traits in a correlation matrix. Cluster architecture sub-traits measured in the growing season 2015 (A), 2016 (B) and combined 2015 + 2016 (C) as Kendall’s tau-b correlation coefficient. Nonsignificant correlations are depicted as 0. (see Table 1 for full sub-trait names) (PDF 270 kb)

Online Resource 3

Overview of the importance of cluster architecture variables for the prediction of the OIV204 compactness descriptor using the “cforest” function for random forest calculation with the R-package “party.” The quality of the importance prediction was assessed with error estimates, i.e., error rate, ranked probability scores (RPS), mean absolute error (MAE), mean standard error (MSE). The combined 2015 and 2016 dataset was used with season as predictor variable (A) and without season as predictor variable (B). (PDF 246 kb)

Online Resource 4

x-axis: Manifestation of cluster architecture sub-trait. y-axis: predicted probability of OIV class membership. The 2015 and 2016 data of 149 F1 genotypes of the cross GF.GA-47-42 × ‘Villard Blanc’ was grouped according to growing season (15, 16) and flower sex (hermaphrodite (H) and female (F) in the left column and analyzed without regard to growing season (right column). (PDF 742 kb)

Online Resource 5

Error rate (ER) assessment for the prediction accuracy in cumulative link models (CLMs) over OIV204 classes and flower sex. For CLMs with the lowest AIC values (see Table 2) the ER was used to assess the prediction accuracy. OIV204 classes and flower sex members exhibited different error rates. All model variants were assessed with a mixed dataset neglecting the season information (-season) or using season as additional factor variable for modeling (+season) (PDF 180 kb)

Online Resource 6

QTLs related to cluster architecture in 149 F1 individuals of the segregating population of the cross GF.GA-47-42 × ‘Villard Blanc’ calculated with interval mapping (IM) and interval mapping with flower sex as co-factor (FS) during four growing seasons. Cluster architecture traits in bold do statistically contribute to a high extent to cluster architecture (PDF 304 kb)

Online Resource 7

QTL comparison. Attributes of QTLs reproducibly calculated in the cross-population with interval mapping (IM) and with interval mapping applying flower sex as co-variable for interval mapping (IM + FS). (PDF 177 kb)

Online Resource 8

Table of Gene ontology (GO) enrichment analysis with genes positioned in confidence intervals of cluster architecture-related QTLs, compared to all the genes in the reference genome PN40024 12x V2 (PDF 283 kb)

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Richter, R., Gabriel, D., Rist, F. et al. Identification of co-located QTLs and genomic regions affecting grapevine cluster architecture. Theor Appl Genet 132, 1159–1177 (2019). https://doi.org/10.1007/s00122-018-3269-1

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