Work Package 1 – Numerical Genomics

Rationale: The overall objective is to develop statistical tools and software approaches to underpin livestock genomics and to enable delivery of the results through the breeder to the consumer. The availability of DNA sequence, high-density SNP genotyping, expression profiling and proteomics provides enormous opportunities to understand and exploit the genetic control of complex traits to the benefit of livestock, the consumer and the environment. However, analysis and interpretation of high throughput genomic data, followed by prediction and selection based upon the results, requires the development of sophisticated statistical, modelling and informatic tools. The continuous development of new technologies requires parallel evolution of the statistical and modelling tools.

The output of analyses of different types of studies (QTL, gene expression, proteomics) therefore needs to be integrated with output from comparative and bioinformatic studies (developed in Work Package 3 – Genomics and Bioinformatics) to optimise inference on underlying genetic causality and to facilitate gene identification. Once genes or linked markers associated with a sustainability trait are identified, this information must be combined with information from phenotypic and other data to allow the breeder to obtain desired genetic improvements without unforeseen deleterious side effects such as inbreeding.

Finally, tools developed for either data interpretation or for exploitation must be put into a user-friendly format and demonstrated in practical applications to facilitate usage by the research community and to maximise uptake by the breeding industry.

Objectives

·       Develop and trial robust approaches for combined linkage and disequilibrium mapping of QTL

·       Develop and trial approaches for marker-assisted, gene-assisted and genomic selection

·       Develop improved methods for the combination of positional and expression QTL analysis

·       Disseminate developed approaches and use in analysis of data from other WPs

·       Incorporate developed analytical methods into user-friendly software available to the European research community.

WP Leader: Professor Chris Haley (Roslin Institute)

Partners involved:
The Roslin Institute and R(D)SVS, University of Edinburgh
University of Aarhus Faculty of Agricultural Sciences

Institute National de la Recherche Agronomique

Wageningen University
ID-Lelystad

Agricultural University of Norway

Institute for Pig Genetics

Agricultural Research Organization, The Volcani Center

Universidade Federal De Viscosa