The group focuses on research aimed at understanding the interaction between genotype, environment and management by using all kinds of different techniques and databases. Therefore database development, maintenance and use is an important area of attention. Furthermore statistical aspects of using large datasets especially in the field of transcriptomics and metabolomics is a major focus area.
Crops: potato, brassica, tomato and other crops of importance.
- Construction of linkage maps and QTL analysis
- Linkage Disequillibrium mapping and association mapping
- Epistatis mapping
- Genetical genomics
- Genomic prediction and selection
- Estimation of Genotype x Environment interaction
- Relating henotype data to microarray, metabolomics or proteomics data
- Methods and software for genetic analysis in polyploid species
Plant breeding has a strong link with quantitative genetics, statistics and bioinformatics, for example in identifying regions on crop genomes that are associated with observed variation in phenotypic traits, and in identifying candidate genes for these genomic regions.
Research within Plant breeding is multi-disciplinary and is dealing with a lot of different types of data, obtained from field-trials but also from high-throughput analysis of molecular markers, RNA transcripts (microarrays), proteins and secondary metabolites.
An efficient database will help in elucidating the genetics of economically important traits, in identifying molecular markers associated with agronomic traits, in allele mining and choosing interesting accessions fur further breeding with improved traits important for consumers, processors and producers.
One goal within plant breeding is to find the causal gene(s) explaining a given phenotype. Semantic web technology brings opportunities to integration data and information across spread data sources. For example, Annotex and Marker2sequence are two applications developed by Plant Breeding, which rely on this semantic web technology to integration genes, proteins, metabolites, pathways, and literature.
Contact: Richard Finkers
Current activities include combined data analysis of molecular markers, gene expression and metabolomics data and phenotype (e.g. disease resistance or product quality) scored on segregating populations of crosses. Methods being used are procedures such as random forest for classification or multiple regression in cases where the number of predictor variables (e.g. molecular markers, genes, metabolites) is much larger than the number of samples in which they have been measured (plants, tissues). Other areas of interest are modelling genotype x environment interaction, mapping and QTL analysis in single segregating populations, multiple populations or collections of germplasm. A specific focus area is also the development of a genetic analysis pipeline for polyploid crops.
Contact: Chris Maliepaard
Fine mapping of a thrips resistance QTL in Capsicum and the role of diterpene glycosides in the underlying mechanismTheoretical and Applied Genetics (2021). - ISSN 0040-5752
A novel non-trichome based whitefly resistance QTL in Solanum galapagenseEuphytica 217 (2021)3. - ISSN 0014-2336 - p. 1 - 11.
The effect of a thrips resistance QTL in different Capsicum backgroundsEuphytica 216 (2020)12. - ISSN 0014-2336
Multiparental QTL analysis: can we do it in polyploids?Acta Horticulturae 1283 (2020). - ISSN 0567-7572 - p. 55 - 64.
Using molecular markers in breeding: ornamentals catch upActa Horticulturae 1283 (2020). - ISSN 0567-7572 - p. 49 - 53.
The ability to manipulate ROS metabolism in pepper may affect aphid virulenceHorticulture Research 7 (2020)1. - ISSN 2052-7276
Aphid resistance in Capsicum maps to a locus containing LRR-RLK gene analoguesTheoretical and Applied Genetics 133 (2020)1. - ISSN 0040-5752 - p. 227 - 237.
Aphid populations showing differential levels of virulence on Capsicum accessionsInsect Science 27 (2020)2. - ISSN 1672-9609 - p. 336 - 348.
The ability to manipulate ROS metabolism in pepper may affect aphid virulence: Wageningen University & Research
Software from "FitTetra 2.0 – improved genotype calling for tetraploids with multiple population and parental data support": Wageningen University and Research
- Dennis van Muijen
- Gurnoor Singh
- Ran Wang
- Eliana Papoutsoglou
- Micaela Colley
- Yanlin Liao