We are proud to announce the graduations and upcoming defences of Animal Breeding & Genomics PhD candidates.
- Latest update: June 2020 -
On Wednesday 2 September at 16:00, Martijn Derks will defend his thesis entitled 'From sequence to phenotype: the impact of deleterious variation in livestock’.
The DNA provides a blueprint of life containing the instruction, together with the environment, that determine the phenotype. In this thesis I attempt to further close the genotype phenotype gap in livestock, contributing to our understanding of important variation in the animals genomes. I analysed hundred thousands of genotypes, and hundreds of whole genome sequenced individuals to identify variation with impact, either deleterious (e.g. recessive lethals) or variants with positive effects on important selection traits.
The aim of this thesis was to connect functional genomics, bioinformatics, and breeding data to identify high-impact variation, describing its functional consequences at the molecular, phenotypic, and population level. The results and proposed tools are valuable to infer function from variation, which will be applied to improve livestock breeds in the future.
Esther M.M. van der Heide
On Friday 11 September at 13:30, Esther M.M. van der Heide will defend her thesis entitled 'Predicting survival in dairy cattle using machine learning'.
Although cows can live to be twenty years old, the average lifespan of a dairy cow is only five to six years. Improving the survival of dairy cows would have several benefits such as increasing farm profitability and reducing the environmental impact of milk production. However, the complexity of survival makes it difficult to improve this trait in practice. In my thesis, I proposed using phenotypic prediction of survival to select young cows, improving survival through increased lifespan of selected cows and better heifer management.
The aim of this thesis was to investigate if accurate prediction of survival phenotype was possible and to investigate if machine learning techniques could contribute to accurate prediction of this complex phenotype. The results of this thesis provide valuable insights in the challenges of predicting survival traits and the suitability of various (machine learning) methods for the prediction of survival in dairy cattle.
On Friday 11 September at 16:00, Biaty Raymond will defend his thesis entitled ‘Use of whole genome sequence data from genomic prediction across populations and species'.
One of the major developement in Animal and plant breeding over the past decade has been the implementation of routine genomic prediction. This is a method that use large scale DNA information to predict the genetic value of individuals that are considered for selection as parents for the next generation. It was expected that the use of whole genome sequence data will increase the accuracy of genomic prediction since the data is expected to contain all the causal variants for any given trait.
The aim of this thesis was to investigate the benefit of whole genome sequence data for genomic prediction, mainly across populations and species. Several genomic prediction models and scenarios were investigated, ranging from those that consider all variants in whole genome sequence data to have the same effect on a given trait, to models that differentially weight the variants based on their potential causal effect on the trait. Results from the thesis highlight the importance of pre-selecting variants in the genome based on biological knowledge and the differential weighting of such variants in prediction models.
Harmen P. Doekes
On Friday 18 September at 16:00, Harmen P. Doekes will defend his thesis entitled 'Genomic characterization and conservation of genetic diversity in cattle'.
Genetic diversity in livestock populations is important, because it forms the basis for these populations to adapt to changing environments and human demands. Traditionally, livestock genetic diversity has been characterized and conserved with pedigree-based measures of inbreeding and kinship. Thanks to the increasing availability of genomic information, in particular single nucleotide polymorphism (SNP) data, we now have additional opportunities to better manage genetic diversity.
The aim of this thesis was to use SNP data to characterize and conserve genetic diversity in Dutch cattle. The Holstein Friesian (HF) breed was the main breed of interest. Results showed, among others, that (1) the way genomic selection was implemented was accompanied by an increase in inbreeding rates, (2) inbreeding negatively affects yield, fertility and udder health, though not all inbreeding is equally harmful, and (3) gene bank collections are a valuable resource in terms of both genetic diversity and genetic merit.
On Tuesday 6 October at 11:00, Haibo Lu will defend his thesis entitled 'Changes in the genetic background of milk composition during lactation'.
Milk production is affected by negative energy balance in early lactation and pregnancy in late lactation. It is known that for milk production traits genetic variances change and genetic correlations differ from unity during lactation. In addition, effects of specific QTL e.g., DGAT1, on milk production traits change during lactation. However, most GWAS for milk production traits assumed constant genetic effects during lactation. These studies might miss QTL whose effects change during lactation.
The objectives of this thesis were to specifically identify the QTL whose effects on milk production traits change during lactation, pregnancy, and test seasons through different GWAS approaches. These results increase our understanding of the changes in the genetic background of milk composition and might contribute to the development of better management indicators based on milk composition.