Welcome to the website of the Statistical Genetics Group! The group is part of the Division of Biostatistics at the Institute of Social and Preventive Medicine of the Lausanne University Hospital and affiliated with the Swiss Institute of Bioinformatics and with the University of Exeter. We are interested in the development of statistical methodologies in order to decipher the genetic architecture of complex human traits. Although our main focus is Genome-Wide Association Studies, we devise integrative analysis tools for different omics data to enhance our understanding of the genetic network of the human genome.

Projects & Collaborators

  1. Genetic fine-mapping:
    1. Detecting imperfect tagging / summary statistics imputation (S. Rüeger)
    2. Copy number variant associations (A. Mace, E. Porcu, A. Reymond, T. Frayling)
    3. Exome-chip analysis (G. Lettre, P. Deloukas, J. Hirschhorn, [GIANT consortium])
  2. Obesity genetics
    1. Gene-environment interactions (N. Mounier, R. Loos, T. Winkler, T. Frayling, I. Heid)
    2. Dissecting genetic subtypes of obesity (J. Sulc, A. Sonrel, T.Winkler, I. Heid)
    3. Index Event Bias (T. Frayling)
    4. Revealing genetic effects that depend on the parental origin of the alleles (R. Freathy, C. Hoggart)
  3. Genetic underpinnings of aging (N. Mounier, MR Rechavi, B Deplancke, J Auwerx, P. Joshi, J. Wilson)
  4. Integration of molecular and brain imaging features to gene-disease associations (E. Porcu, J. Sulc, R. Rueedi, S. Bergmann, B. Draganski, A. Reymond)
  5. Genetics of infectious- and autoimmune diseases (P-Y. Bochud, J. Fellay, M. Tafti)
  6. Mendelian randomisation (V. Rousson, J. Bowden, G. Davey-Smith)

Furthermore, we are heavily involved in the activities of the GIANT consortium (http://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium) and in various genetic analyses of

  • the Cohorte Lausannoise (CoLaus) study
  • the Swiss Hepatitis C Cohort Study (SCCS)
  • the Swiss Transplant Cohort Study (STCS)
  • the European Narcolepsy Network (ENN)
  • the Hypergenes study