It is the aim of my scientific work to contribute to the development of theory and methods used for genetic improvement in general, and in livestock species in particular. More specifically it is the aim to
I work on research towards development and use of quantitative genetic methods in commercial breeding programs in animals and plants. Research focus is on development and use of quantitative genetic methods and the development of optimum breeding programs. Such programs must be economically, environmentally and ethically sustainable. Moreover, work on a continuing deepening of our understanding of the genetic regulation of interesting traits in animals and plants.
I am working for increased international collaboration in future research programs. Such cooperation may include the Western world, the BRIC countries (Brazil, Russia, India and China) and the third world countries which are selected by the Danish program for foreign aid.
I work with designed experiments for estimation of quantitative genetic variation in physiological, immunological and behaviour traits, and the co-variation with production and health traits in cattle and pigs.
Application of likelihood and Bayesian methods for the analysis of genetic data; inferences about genetic parameters using selected data; Markov chain Monte Carlo methods in genetics.
Molecular prediction of disease and production traits in livestock
The overall research goal is to develop a statistical procedure that identifies which set of biological molecules (e.g., gene transcripts, proteins, and metabolites) best predicts phenotypes for disease and production traits in livestock. In developing this procedure the focus is on statistical methods that
1. account for relationships among biological molecules (e.g. Gaussian graphical modeling)
2. use prior information about the relationship among biological molecules.
We are working on a supervised stochastic search variable selection procedure for identifying promising subsets of molecular predictors of phenotypes in individuals. The procedure uses prior biological information and combines observations from large-scale ‘omics’ data. We have implemented this procedure into a fortran program which is currently being tested.
We are also looking into Gaussian graphical modeling, which is a multivariate statistical technique that can be used to infer relationships among molecular variables such as gene transcripts, proteins, and metabolites.
