The Group focuses on developing new tools and machine learning algorithms for describing the population substructure in the genome and understanding the biological implications of such structure, identifying the fingerprint of polygenic adaptation in complex phenotypes and evaluating the impact of archaic introgression in phenotypes of interest. In particular, we address questions related to which is the genetic origin from a population point of view of a given individual, which are the demographic and selective factors that shaped the genetic variation present in a population, and how ultimately this variation influences and allows us to detect the individual risk in complex common diseases with a genetic burden. Overall, all this multidisciplinary combined knowledge allows us to better understanding how (which are the genetic markers involved in the disease) and why (which is the natural history of the disease) we get sick.

Our Group focuses on human species but the universality of the proposed methods allows us to apply them to other model organisms.

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