What we do

In the Statistical Genetics Group, we are interested in the development of statistical methodologies in order to decipher the genetic architecture of complex human traits related to obesity. To do this, we efficiently combine large-scale genome-wide association studies (GWAS) with various -omics data. Our methods improve genetic fine-mapping, reveal gene-environment interactions, dissect genetic subtypes of obesity, enhance causal effect estimation and detect various statistical artefacts. Furthermore, we are involved in large consortia researching the genetic basis of anthropometric traits (GIANT) and longevity (LifeGen).

Find out more about the Group’s activities

Highlights 2020

Main publications 2020

  • Sulc J et al.
    Quantification of the overall contribution of gene-environment interaction for obesity-related traits
    Nature Communications, 10.1038/s41467-020-15107-0
  • Mounier N and Kutalik Z
    bGWAS: an R package to perform Bayesian genome wide association studies
    Bioinformatics, 10.1093/bioinformatics/btaa549
  • Porcu E et al.
    Causal Inference Methods to Integrate Omics and Complex Traits
    Cold Spring Harb Perspect Med, 10.1101/cshperspect.a040493


epfl lausanne

Zoltán Kutalik
Statistical Genetics Group
CHUV / University of Lausanne
Group Webpage

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Domain(s) of activity:

  • Genes and genomes
  • Systems biology
  • Biostatistics
  • Epigenetics
  • GWAS
  • Human genetics
  • Mathematical modelling
  • Metabolomics
  • Personalised medicine
  • Population genetics
  • Transcriptomics

Domain(s) of application:

  • Basic research
  • Medicine and health