Trinity College Dublin KARI-TRC Shirakawa Institute of Animal Genetics

Trinity College Dublin KARI-TRC Shirakawa Institute of Animal Genetics Functional genomics to identify genes and networks influencing survival following Trypanosome challenge.

Cattle Tsetse Cattle and tsetse Origins of N’Dama and Boran cattle N’Dama Boran

Studying the tolerant/susceptible phenotype has problems: Separating cause from effect Separating relevant from irrelevant. Dominance of the ‘what is happening to this weeks trendy gene/protein/cytokine?’ approach.

A gene mapping approach, by definition, points to the true genetic cause of the difference between resistant and susceptible

A) Anaemia QTL B) List of genes in the human on HSA2 ARHGA15 on BTA2 remains a candidate mRNA profiles indicate that RAC1 the target modulated by ARHGAP15 is differently expressed in Boran and N’Dama cattle.

Gene frequency H  P mutation at AA282 Alignment of N’Dama ARHGAP15 with homologues Cow NDama KFITRRPSLKTLQEKGLIKDQIFGSPLHTLCEREKSTVPRFVKQCIEAVEK Cow Boran KFITRRPSLKTLQEKGLIKDQIFGSHLHTLCEREKSTVPRFVKQCIEAVEK Human KFISRRPSLKTLQEKGLIKDQIFGSHLHTVCEREHSTVPWFVKQCIEAVEK Pig KFITRRPSLKTLQEKGLIKDQIFGSHLHTVCERENSTVPRFVKQCIEAVEK Chicken KFISRRPSLKTLQEKGLIKDQIFGSHLHLVCEHENSTVPQFVRQCIKAVER Salmon KFISRRPSMKTLQEKGIIKDRVFGCHLLALCEREGTTVPKFVRQCVEAVEK

Genotype + Phenotype -> Genetic region -> polymorphism ->understanding ->exploitation

We examined the entire genome for regions involved in one phenotype But can we move to the next level and screen multiple phenotypes simultaneously ?

Livestock are by definition adapted to the landscape they inhabit. Landscape Temperature, altitude, rainfall etc Disease challenge Nutritional challenge Human selection Farming system In Europe these are extremely homogeneous In Africa they are extremely stratified and extremely unstable

Principle components analysis of data from genome-wide expression analysis comparing gene expression in liver of Ndama (red) vs Boran (blue) in response to infection with T. congolense. Light colour day 29 post infection, dark day 32 post infection. Components 1 and 2. (Components 3 and 4 separate by day post infection) Different genotypes respond differently in a given environment. Following trypanosome challenge N’dama live while Boran die. And we can see their genomes responding differently

Principle components analysis of data from genome-wide expression analysis comparing gene expression in spleen of Ndama (red) vs Boran (blue) in response to infection with T. congolense. Light colour day 29 post infection, dark day 32 post infection. Components 1 and 2. (Components 3 and 4 separate by day post infection) And the same data for spleen. The biggest effect we see (after tissue) is breed.

Mouse time course. Liver.

So we need to understand the fit between the livestock genotype and the landscape in which they function. Example Build a road Develop a vaccine Improve (or shut off) market access Change the climate!

Livestock Landscape Genomics Any change in the landscape changes the optimal livestock type There is information in the distribution of livestock genotypes across the environment. The tools -genetic, GIS, farm system analysis - are available now to allow us to ask what features of the genome is exposed to selection by what factors in the environment. The next level of genome scanning.

Cattle Tsetse Cattle and tsetse N’Dama Boran There is information in the distribution of livestock genotypes across the environment.

To extract information from distribution is a challenge. Input: High density SNP data Detailed metadata on individual animals GIS data and derived disease/climate information Farming systems analysis Output: Predictions about consequences of change to landscapes Tools to manage landscapes for agriculture Unique probe of genome function

To extract information from distribution is a challenge. Data collection - metadata Data management Data QC Data integration Data display tools Statistics !