Dr Enrique Sánchez Molano

Research Fellow in Livestock Genomics (Roslin based)

My research work is mainly related to population genetics both from theoretical and applied studies. Following my Biology degree (specialized in Genetics) in Spain, I started a PhD in relation to mutational models and their relevance under natural selection by considering the joint effects of purifying selection, mutation and drift. All these studies were developed using Drosohila as an experimental organism, studying the evolution of populations under different managements and comparing the obtained results with theoretical expectations obtained by simulations.

After finishing my PhD in 2010, my research work was mainly focused on the QTL analysis for growth related traits in turbot. This research was performed at the INIA (Spain) and, being the first wide QTL analysis for growth related traits in this species, it provided several QTL that could lead to the identification of candidate genes. Simultaneously, I worked on FORTRAN based simulations to study the evolution of small subdivided populations under stabilizing selection. The results showed a better performance of dynamic methods over the use of classical methods such as the one-migrant-per-generation-and-population strategy.

In 2012 I started working in the Roslin Institute as a Postdoctoral Researcher. Working on a genome-wide study of Hip Dysplasia in UK Labrador Retriever, a simulation was developed to estimate the benefits of genomic selection against the classical phenotypic selection in this breed. Furthermore, we identified genomic regions associated with the disease and compared the performance of pedigree- and genomic-based breeding against it, evaluating also the performance of different genomic selection methods.

Since 2014 I have been part of Georgios Banos group as a Postdoctoral Research Fellow in Livestock Genomics, working in several projects related to high density SNP data analysis and the simulation of biological processes to study the evolution of populations under genomic selection. In order to do so, I have been working with both real and simulated data for a wide range of traits (disease, fitness and productivity) and selection strategies such as genomic-based optimum contributions.