The objective of this industrial PhD project is to evaluate and implement tools for Genome Wide Selection (GWS) in ryegrass breeding. Genomic information is now in use in several animal species for the prediction of genetic merit. GWS has revolutionized animal breeding, and is also expected to have great potential in grass breeding. Implementation of the technology into ryegrass breeding involves a number of new technical and statistical challenges, to be addressed in this project. In contrast to animals, ryegrass, like other outcrossing species, is bred in families and not as individuals. New computational/statistical tools relying on Bayesian principles for high dimensional model selection need to be developed and implemented. The methods must take account of the specifics of ryegrass breeding and utilize genome sequence based on family samples. The PhD project will specifically address: (1) development of genomic prediction models based on phenotype data collected on families and allele frequency data obtained from sequencing of family pools; (2) development of parameters and algorithms for corrections of putative population structures in the association model; (3) validate developed models on data from a ryegrass breeding program and make genetic merit predictions for new breeding material.
Applicants to the PhD position must have a relevant Master’s degree (or graduate in the very near future – documentation for final thesis and date of examination must be enclosed in the application) or equivalent. We are seeking a candidate with a strong interest in statistical analysis, for instance an MSc in quantitative genetics or in biostatistics with an interest for applications in agriculture.