To understand the variation of food product quality caused by phenotype variations as a function of genes and environment is a tremendous challenge, since the amount of information that has to be taken into account is huge. Measurement technology for coll ecting the required data is now readily available, but statistical tools for integrating all the available data about the genome, proteome, metabolome, quality parameters and environmental information in one integrative approach are not developed yet. But this is where we have to go if we aim for bridging the gap between genotype and food quality. The impact of such integrative statistical methodology for food production can hardly be overstated. Data analytical methodology will contribute to utilise gene tic information and thereby help to increase plant and animal production, improve food product quality and the well-being of consumers.
This proposal seeks support for a project where statistical approaches in genetic selection are combined with multivari ate statistical tools that are able to handle many data sets originating from different genetics and quality measurement technologies, so-called multi-matrix techniques. The aim is to develop methods that detect genetic markers in an integrative approach taking into account complex relationships between the different data sets. In order to make it easier to draw conclusions from huge amounts of data and to include biological background information in the statistical analysis, graphical representation tool s will be developed.
The project is a joint collaboration by Nofima Marin, with competencies in statistical genomics, quantitative genetics and genomic selection and Nofima Mat, with competencies in statistical methods for combining information in complex data.