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

Main publications 2018

  • Winkler T W et al.
    A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications.
    Nat Commun, doi: 10.1038/s41467-018-04124-9
  • Rüeger S et al.
    Evaluation and application of summary statistic imputation to discover new height-associated loci.
    PLoS Gen, doi: 10.1371/journal.pgen.1007371
  • Tin A et al.
    Large-scale whole-exome sequencing association studies identify rare functional variants influencing serum urate levels.
    Nat Commun, doi: 10.1038/s41467-018-06620-4


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:

  • Medicine and health