Project Details
Projekt
Automated Evaluation and Comparison of Grapevine Genotypes by means of Grape Cluster Architecture
Subject Area
Plant Breeding and Plant Pathology
Term
from 2016 to 2019
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 289322290
Viticulture is endangered from several fungal diseases and pests. One for which no resistance loci have been reported so far is gray mold (Botrytis cinerea) causing bunch rot. It grows on more than 200 species causing severe losses in warm and humid seasons. Botrytis is very adaptive and is considered as a high risk fungus due to very rapid formation of resistances against fungicides. In viticulture it is well known that physical barriers such as loose cluster architecture, tight berry skin and hydrophobic berry surface are important factors to prevent bunch rot (Botrytis infection). To strengthen grapevine breeding efficiency the proposed project intends to identify and narrow down genetic loci for cluster architecture traits (e.g. length of rachis, length of pedicel, berry size). As an exact data acquisition in high throughput is not manageable, University of Bonn and JKI Siebeldingen cooperate for modeling of grapevine clusters from 3D point clouds and using of the modeling data for QTL analyses. A pilot study at JKI on 150 F1-plants (pop 150) resulted in a first QTL (on chromosome 1) for pedicel length. Due to the high labor intensity of measuring more than 30 cluster parameter alternative techniques are demanding for evaluation and are a prerequisite for the identification of further QTL and for fine mapping using a larger population (pop1000).Based on previous work done at University of Bonn on the modeling of the grapevine cluster it can be expected that a solution shall be developed based on 3D-scanning and modeling approaches. Therefore we propose 1. to determine cluster traits by length measurements (reference evaluation) and 3D-scanning (point clouds) on 150 F1 plants (pop150), 2. modeling clusters and extracting known measurable traits as well as calculating new traits (e.g. angles of branches and ratios), 3. to verify the quality of the data sets in comparative QTL analyses for measured cluster traits in pop150, 4. to identify new QTL for traits calculated from modeling data (hidden traits), 5. to determine cluster traits of further 1000 F1 plants (pop1000) by using sensor data and data from modeling based on point clouds for QTL analyses and fine mapping, 6. to identify candidate genes in the QTL regions, 7. to differentiate genetic determinants of cluster architecture from coulure (environmentally induced failure of fruit set, 8. to generate scanning data on selected plants from the grapevine repository contrasting for compact and loose clusters and, 9. to amplify and sequence candidate genes within the QTL for pedicel length and eventually other QTL regions. The sequences are used for association studies and SNPs/InDels for marker development and characterization of loci.
DFG Programme
Research Grants
