Project Details
Projekt
A comparative functional genomic approach to unravel the genetic and genomic architecture of tail length in pigs and sheep based on a unified selection experiment design
Subject Area
Animal Breeding, Animal Nutrition, Animal Husbandry
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 450678943
The main objective is to analyze the genetic and genomic architecture of tail length in the livestock species pig and sheep. Results are imperative to assess the breeding potential on short tails, as an alternative for routine tail docking practices, and to avoid tail biting, tail lesions and hygiene related tail diseases. Preliminarily results from our own resource model populations in pig and sheep indicate a general quantitative polygenic structure of the trait, despite additional monogenic mechanisms as reported in other animal species. Consequently, we will establish a unified selection and mating experiment in both species, combined with an advanced phenotyping protocol to unravel the postulated general quantitative basis of the trait, as well as to identify potential monogenic variants across species using a functional comparative genomics approach. Hence, we aim to identify conserved and species-specific causal variants and QTL involved in the formation of the trait using whole genome resequencing. Ultimately, we intend to infer genomic mechanisms contributing to tail length, tail abnormalities and tail lesions across species. The current study offers great potential for ongoing research, i.e., for the development of a direct gene tests (for tail abnormalities including completely tail losses) and/or genomic breeding value estimations for tail length, also in other species. The expected findings could contribute to a sustainable animal welfare improvement for both livestock species through breeding. However, potential pleiotropic effects negatively affecting other breeding goal traits need to be addressed. Therefore, phenotypic and genetic correlations with economically important performance as well as functional traits will be inferred in an enhanced quantitative genetic framework (i.e., structural equation modelling).
DFG Programme
Research Grants
