PhD theses (ongoing)

Solange DURUZ

In the context of both severe selection in farm animals and of current and potential effects of climate change, it is crucial to implement a suitable and sustainable management of the breeding practice in Switzerland, supported by a judicious use of geographic information technologies. To reduce further loss of genetic diversity and to protect and promote the diversity of farm animal genetic resources (FAnGR), this research will propose a novel monitoring approach of Swiss FAnGR including characteristics of the Swiss transhumance system (alping), whose effects will be investigated (milk production). Phenotype and genotype data, but also the location data of more than 8’000 alps from the Braunvieh cattle breeders’ association will be used as a case study and processed by a combination of population genetics and spatial statistic tools. The monitoring based on different categories of criteria (genetic, demographic, geographic, socio-economic, cryo-conservation, etc.) will enable the determination of a level of endangerment for Swiss livestock species and breeds according to FAO recommendations. 
Moreover, investigations dedicated to the study of the alping system will permit to i) study lactation curves varying in time to point out phenotypes and genotypes that are worth preserving (valuable traits related to production quality and quantity) and to ii) define  present and future environmental conditions showing the highest probability to constitute sustainable breeding conditions, i.e. favoring a sufficient grass quality.

To achieve these goals, the research described here comes within the scope of biogeoinformatics: biological data will help to monitor diversity and to identify key genomic regions associated with important phenotypes (e.g. fat, protein or lactose proportion in milk) and related to selection; geography will make it possible to analyse the spatial distribution of animal characteristics and to link the latters with environmental variation; and finally computer science is required to process the corresponding large volumes of data (GWAS, spatial statistics, pedigree analysis, etc.) and to integrate the different thematic criteria to facilitate decision making (e.g. breed or species prioritization).​

Estelle ROCHAT