From sequence to phenotype: detecting deleterious variation by prediction of functionality


The overall goal of the project is to develop procedures in livestock species to utilise the wealth of sequence and functional information currently accumulating. The proposed research project is based on data available at Wageningen University and at the Breed4Food partners for chicken, turkey, pig and cattle.


The data includes whole-genome sequences of hundreds of individuals for each of these four species and genotypes and phenotypes for a large number of individuals from breeding lines of the Breed4Food partners. This sequence data will be used for the identification of genetic variants (SNPs) predicted to affect the phenotype. Computational methods based on machine learning will be developed to predict the likelihood of a given variant in the data being deleterious or pathogenic, by integrating genome annotation, evolutionary conservation and functional genomics data, and transferring knowledge obtained from other species (human) as far as possible. These predictors will be tested and validated in chicken, as it allows for relatively fast, inexpensive experimentation, but will be designed to be generally applicable in livestock breeding. Within the available genotyped pedigrees (pigs, chicken) as well as in specific experimental crosses between carriers of a selection of the identified variants (chicken), we will verify whether offspring homozygous for a variant are indeed affected or non-viable.
The main objectives of the project are to:

  1. Develop an approach combining whole-genome sequencing, bioinformatics and machine learning to identify potentially deleterious variants with particular emphasis on lethal or pathogenic variants;
  2. Explore the frequency of such variants in commercial breeding lines of the four livestock species;
  3. Demonstrate the utility of specific marker-assisted breeding to remove deleterious and negative mutations from the population, resulting in improved performance of the commercial breeding lines.
  4. Predict the likelihood of any mutation to have an effect on the phenotype “

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.


For more information, please feel free to contact us.