Enhancing genomic prediction for economic traits in Holstein dairy cattle
[Contact: Prof. Hassan Baneh, h.baneh@skoltech.ru]

Dairy cattle breeding programs have experienced a revolutionary change caused by genomic selection, which enables breeders to identify genetically superior animals at a much younger age; in fact, animals that passed DNA testing can receive an accurate GEBV before they reach sexual maturity and without having their own phenotype records. In 2009, a new model called ssGBLUP was introduced in which all genotype, phenotype and pedigree information are combined in a model to evaluate the population at once, which is a single step genetic evaluation. However, it was a great progress, but still the selection accuracy is not very high some traits and the breeder are trying to improve it. However, the current SNP chip genotypes are evenly distributed over the genome and can tackle a large part of genetic variation in the genome, but still the genetic variance explained by the models based the SNP chip genotypes is lower than expected. Genotype imputation, the process of extending a small SNP array to the higher panel, can be a potential methods to improve the power of genomic studies. The imputed panel data can be used for genomic studies of complex traits in livestock. Therefore, this project aims to:

  1. To evaluate the main factors influencing genotype imputation of Bovine SNP panels.
  2. To perform Genome-wide Association study for the productive and reproductive traits in dairy cattle.
  3. To investigate the genomic prediction accuracy using Whole-Genome Imputed Sequence data
  4. To investigate the genetic architecture of economic traits in dairy cattle

Required skills: Solid knowledge of quantitative genetics, animal breeding programs (linear mixed model, animal model-based genetic evaluation, GWAS and genomic prediction). The candidate should be proficient in statistical analysis for large data set and have experience with quantitative genetics analysis techniques such as BLUP and GBLUP models. Programming and bioinformatics skills and experience are considered advantageous.