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.

Principal Investigator

Oscar Lao Grueso

Lao Grueso, Oscar
CSIC Tenured Scientist
Algorithms for Population Genomics

Current members


Predoctoral Researcher
Algorithms for Population Genomics

Olga Dolgova

Dolgova, Olga
Postdoctoral Researcher
Algorithms for Population Genomics

Francesc Ganau Penella

Ganau Penella, Francesc
Predoctoral Researcher
Algorithms for Population Genomics

Ongoing projects


Roca-Ayats N, Maceda I, Bruque CD, Martínez-Gil N, Garcia-Giralt N, Cozar M, Mellibovsky L, Van Hul W, Lao O, Grinberg D, Balcells S. 2024. Evolutionary and functional analyses of LRP5 in archaic and extant modern humans. Human Genomics, 18(1):53. DOI:10.1186/s40246-024-00616-6

Huang X, Rymbekova A, Dolgova O, Lao O, Kuhlwilm M. 2023. Harnessing deep learning for population genetic inference. Nature Reviews Genetics. DOI:10.1038/s41576-023-00636-3

Moreno-Ruiz N; Genomics England Research Consortium, Lao O, Aróstegui JI, Laayouni H, Casals F. 2022. Assessing the digenic model in rare disorders using population sequencing data. European Journal of Human Genetics, 30(12):1439-1443. DOI:10.1038/s41431-022-01191-x.