Genomics tools

Workpackage Leader: This e-mail address is being protected from spambots. You need JavaScript enabled to view it & This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Workpackage 1 is devoted to the characterisation of the genetic components attributed to yields in order to enhance the efficiency of selection for complex traits.

A segregating population of 297 recombinant inbred lines (RIL) was generated and its genetic map established.  The RILs were constructed from a cross between a Blocky Bell pepper cultivar Yolo Wonder and a hot small fruited landrace cultivar Criollo de Morelos. Global gene expression analysis studies with the use of microarray technology will be used to reveal changes in the expression between lines of segregating progeny.  The differential expression will be mapped as a primary quantitative trait. In addition an a priori approach will be implemented where the knowledge of genes involved in the growth mechanisms of plant or fruit in other species will provide guidance in the selection of a candidate gene in pepper.

Yolo Wonder mature	Criollo de Morelos mature

Phenotyping tools

WP 2.1: Phenotyping tools- Development of an image analysis tool: WP Leader: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

WP 2.2: Phenotyping tools- Development of a fluorescence tool: WP Leader: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

WP 2.3: Phenotyping tools- Large scale experiments: WP Leader: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Workpackage 2 is dedicated to the production of fast and automated tools for large scale phenotyping in a range of environmental conditions. Two different climatic locations (Wageningen- The Netherlands and Almeria -Spain) were chosen for the phenotyping experiment. At regular intervals the plant development will be monitored. In addition, the creation of a mobile recording device called SPY-SEE enables the collection of sequences of images to assist with the automated quantification of plants characteristics. The use of a statistical modelling approach will be adopted on the occasions where segmenting individual plants proves to be impractical. Furthermore, to enhance the acquisition of parameters influencing a plant’s yield increase, a fluorescence device has been developed to assess the fluorescence kinetics.

SPY-SEE setup in greenhouse	SPY-SEE recording device
Central unit and fluoresence head
Pattern of fruit load (number of fruits per plant) of 9 genotypes from the RIL poplulation CM 334 x Yolo wonder in time

Data analysis tools

In WP 3.1 the emphasis is on developing and adjusting existing deterministic crop models to make these applicable for use in QTL analysis. An inventory of crop growth models has been made and three crop models, ranging from simple to complex, have been developed or selected. The simplest model simulates growth of vegetative and generative biomass based on light use efficiency. Partitioning to the fruits (harvest index) is assumed to be constant. The second model resembles the simplest model, but includes a boxcar train method to simulate fruit development. The most complex model is INTKAM (> 50 parameters), which contains many submodels for e.g. light interception, photosynthesis, respiration, dry matter partitioning and fruit growth.

It is an important research question in this project, to determine which model will best serve our goals. A simple model with only view parameters that can all be determined for all genotypes, or a complex model with many parameters. Such a complex model is more flexible and ‘physiologically sound’. However, it contains many parameters which can not be determined for each genotype and hence have to be assumed equal for all genotypes. Furthermore, part of the parameters will hardly influence the model output. Based on sensitivity analysis the most relevant parameters in such a complex model have to be determined and will be measured on all genotypes.

In a preliminary greenhouse experiment some crop model parameters have been determined for 9 genotypes of the mapping population. These 9 genotypes were selected from the 150 genotypes in such a way that phenotypic and genotypic variability was still covered. A large genetic variation in leaf photosynthetic rate was determined (Fig. 1). The highest value was about 60% larger than the lowest value. This result is promising, and quite unexpected. Often it is believed that genotypic variation in leaf photosynthetic rates in rather small. This large genetic variation in leaf photosynthetic rates opens possibilities for breeding for substantial increases in pepper yield.

Figure 1: Genotypic variation in photosynthetic capacity for 9 genotypes from the mapping population. A start has been made with the selection of the most relevant crop growth model parameters and a model uncertainty and sensitivity analysis concerning yield. A probabilistic sensitivity analysis will be applied (Fig. 2).

Fig. 2. Schematic presentation of so-called probabilistic sensitivity analysis.

This is a better way to determine the importance of parameters in a model than the often used percentual change approach in which a parameter value is varied by plus or minus 10% and compare this with the percentual change in output. In a probabilistic sensitivity analysis, probability distributions for the model parameters are determined, and a large number (e.g. 1000) of parameter combinations (for each parameter a random drawing from its distribution) are created. The model is running for each of these 1000 parameter combinations and a statistical analysis between input and output will reveal the importance of each parameter in the output. Preliminary results showed that some photosynthesis parameters are very important, whereas e.g. the leaf scattering coefficient or the stomatal response to humidity and temperature plays only a minor role.

One omission in this advanced sensitivity analysis, but also in the more common percentual change approach, is the fact that correlations between parameters are not yet taken into account. We will try to take this into account in our final analysis when possible.

WP 3.2 is dedicated to the development of QTL mapping methodology for the identification of crop growth parameters. Attention should be given to the modeling of phenotypic traits over time, and more specifically to the changes (increase/decrease and acceleration/deceleration) that these traits show. Furthermore, growth traits/ the growth of traits should not be studied for individual traits, but for all traits simultaneously. The mapping of QTL for longitudinal traits may be done by a two step approach comprising the fitting of a suitable growth curve (e.g., logistic, exponential, Gompertz) and subsequently treating the curve parameter estimates as trait records (e.g., Malosetti et al. (2006) TAG113:288-300). However, here we aim to integrate these two steps into one flexible method that for example takes into account the uncertainty in parameter estimates.

Figure1: Logistic growth curve with three possible perturbations, i.e., earliness, maximum yield and growth rate.

Validation

Workpackage Leader: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

Workpackage 4 will provide the validation experiment of the technology and knowledge accumulated from the previous workpackages. Different vegetative parameters, such as the number of internodes, stem length or leaf area, are being controlled as inputs for the model. Reproductive parameters are also being measured. Total dry matter production and its partitioning are being determined from weekly fruit harvests and the final destructive harvest of the plants. Fruit load, fruit growth duration and potential fruit weight are also monitored.

General view of the greenhouse in Almeiria - Spain
Measuring leaf area	Measuring stem length	Pruning
Labeling for monitor fruit develpoment	Plant labeling for monitor growth	Integrated pest management

Dissemination

Workpackage 5 aims to disseminate the achievements of the SPICY project to the European plant breeding industry. For that reason an industrial advisory board has been formed to enable the exchange of ideas between academic and industrial researches. Additionally, a website was created to allow greater accessibility to the SPICY project. Furthermore, workshops and specialized courses will be organised with the purpose of transmitting the technological knowledge gained through the SPICY project.