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 2021

  • Group member, Chiara Auwerx received the prestigious Lodewijk Sandkuijl Award for the Best Talk in statistical genetics at the yearly conference of the European Society of Human Genetics.
  • Zoltan became associate editor of PLoS Genetics.

Main publications 2021

  • Darrous L et al.
    Simultaneous estimation of bi-directional causal effects and heritable confounding from GWAS summary statistics
    Nature Communications, 10.1038/s41467-021-26970-w
  • Porcu E et al.
    Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome
    Nature Communications, 10.1038/s41467-021-25805-y
  • Ojavee S E et al.
    Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis
    Nature Communications, 10.1038/s41467-021-22538-w


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