QTCC: From Quantitative Trait Correlation to Causation in dairy cattle

• Applicants: Professor Dr. Jörn Bennewitz; Professor Dr. Jens Tetens; Professor Dr. Georg Thaller
• Subject Area: Animal Breeding, Animal Nutrition, Animal Husbandry
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 448536632

The breeding goal in dairy cattle breeding includes performance traits (e.g. milk yield) and fertility and health traits, and longevity. Feed efficiency traits will be part of the breeding goal in the near future. Modern genomic tools applied to large data sets and augmented with external information about genome sequence variants allows for the comprehensive dissection of the genetic architecture of quantitative traits and, beyond that, of the shared architecture of multiple traits. The aim of the project is to analyse the interrelationships of the quantitative traits in dairy cattle beyond global correlations towards local correlation and causations. The traits considered belong to the complexes milk performance traits, fertility traits, health traits, and efficiency traits. In particular (but not exclusive), milk performance and feed efficiency will be contrasted against fertility and health traits. The following hypotheses will be tested. (i) The dissection of global genetic correlations between quantitative traits down to functional and evolutionary relevant genomic regions identifies trait-shared and trait-specific genetic architectures. (ii) The identification of pleiotropic and trait-specific lead variants allows for the separation of common and trait-specific metabolic pathways, and this in turn facilitates the identification of causative relationships between traits. (iii) Milk performance and feed efficiency are significant exposure traits which causally compromise the cow’s fertility and health. These hypotheses will be tested using modern genomic methods, partly augmented with external information, applied to a unique and disruptive observational data sets involving 150K cows with SNP-chip information, imputed whole genome sequence variants, and records for a large panel of quantitative traits. The results are important to understand and dissect the interrelationship and shared genetic architecture of the trait complexes down to single variants levels.

• DFG Programme: Research Grants