Division of Quantitative Biology

The Doeschl-Wilson group investigates how the genetics of individuals affects the spread of infectious disease, both within an animal and between animals. We are an interdisciplinary group of scientists aiming to effectively combine field and laboratory experiments with mathematical modelling and quantitative genetics theory, with the ultimate aim to improve livestock health and resilience.  

Using the genetics and physiology of avian reproduction allows us to develop strategies utilising traditional or marker assisted selection to tackle problems as diverse as osteoporosis in laying hens, growth and reproduction in meat type birds, antimicrobial activity of egg white and shell quality in laying hens.

I lead the HighlanderLab, which focuses on managing and improving populations using data science, genetics, and breeding.

We focus on populations used for food, feed, and fibre production with some spillover into other populations.

 We are particularly interested in:

(i) methods for genetics and breeding,

(ii) design and optimisation of breeding programmes, and

(iii) analysis of data to unravel biology and to find new ways of improving populations.

Leverage Big Genomic Data through novel computational and bionformatics approaches to dissect genetically complex traits to improve efficiency and robustness of farmed animal species  

My research focuses on the dissection of the complex trait variation of the type that underlies the health, production and reproduction traits in livestock and frequent and high impact human diseases such as cardiovascular and metabolic diseases, glaucoma and mental disorders. I use phenotypic, environmental and ‘omic (including genomic) information recorded in large populations to disentangle the complex trait architecture.

My work has focused on applying and developing tools that allow us to better understand the genetic and environmental architecture underlying complex traits. This is important from a basic biology, clinical and animal breeding viewpoints. Identifying which genes and pathways (or other genomic features) are responsible for trait variation, be it normal or pathological, will enable the design of tailored breeding programmes, unveil pharmacological targets and inform on interventions needed to prevent ill health (if we know an individual has a genetic high risk of suffering a disease, we can put in place measures that counteract this risk). These have important implications both from a welfare and an economic point of view. Some advances have been done towards unveiling regions responsible for variation, and my research has contributed to this. We have also come to the realisation that more powerful analytical and computational tools are needed to further assist in this task and to apply the outcomes of the research to ‘real life’ (for example risk prediction tools in clinical practice). Again, my current research plays an important role in the advancement of the field.

My main research interest is in the development of novel breeding methodologies and tools. This work involves a synergy between theoretical quantitative genetics and practical animal breeding. My main expertise is the application of genomic selection on big datasets for various species (pig, chicken, cattle, etc.) and various models, with emphasis on single-step genomic prediction.

Application of population and quantitative genetic approaches to dissect the genetic basis of phenotypic traits in domesticated animal species and to analyze the processes of domestication and breed development.