TOwards Precision BREEDing using genomic prediction
This proposal describes the development of statistical genetic methodology to increase the accuracy of genomic prediction for complex phenotypic traits by optimal use of sequence information.
This new prediction methodology will approximate as closely as possible total genetic variation, i.e., between genotype variation, by the variance in genomic estimated breeding values (GEBVs), where the latter GEBVs will be based on sequence information.
These new prediction methods will depend on the identification of the causal SNPs/genomic regions in contrast to existing prediction methods that depended on long ranging within family linkage disequilibrium (LD) between SNPs and QTLs. With statistical and bioinformatics approaches, we will first identify the potential contributions of different types of genetic variation to total genetic variation, and then develop optimal differential weighting schemes for the various genetic effects in genomic prediction models. We will also investigate the design question about which factors determine the accuracy and its persistence of genomic prediction methods in relation to the construction/choice of training populations.
Furthermore, we will pay attention to models allowing training on multiple traits/environments to predict a broad breeding target in the validation or commercial setting. Ultimately, our prediction models should approach or exceed 85% accuracy with sufficient persistence for the accuracy of a target trait in pure line, multiple line and cross bred prediction.“
Breed4Food STW Partnership
The Breed4Food STW Partnership “Predicting Phenotypes” research projects are:
- From sequence to phenotype: detecting deleterious variation by prediction of functionality
- GenoMIX: utilizing crossbred information to accelarete genetic progress
- SmartBreed: Smart animal breeding with advanced machine learning
- Topbreed; towards precision breeding using genomic prediction
Mutual goal is a more sustainable and animal-friendly breeding.