Genetic and Epigenetic Gene Regulation

Using natural genetic and epigenetic variation to characterize regulatory networks underlying complex diseases

Motivated by the fact that most disease associated variants identified to date are located in non-coding parts of the genome, which likely harbors regulatory elements, we are studying the effect of naturally occurring sequence variation on gene regulation. To characterize regulatory sequence variants two related challenges have to be met: 1) regulatory elements have to be recognized and 2) the corresponding target genes have to be identified. Epigenetic marks such as histone modifications have proved instrumental for the identification of regulatory elements in the genome, while the integrated analysis of genetic variation and gene expression provides a strategy (expression QTL mapping) to identify targets of regulatory variants. Ultimately the integration of genetic, genomic and epigenomic data set is expected to lead to a comprehensive understanding of regulatory sequence variation and its role in disease. Towards these goals we have:

Regulatory networks and computational systems biology of atrial fibrillation

Atrial fibrillation is the most common form of arrhythmia. It leads to a fivefold increase in the risk of stroke and thus constitutes a major health burden. Within the SymAtrial junior research alliance, we are characterizing the molecular pathways and regulatory mechanisms involved in disease aetiology and progression by using integrative data analysis and multilevel modelling. In particular we will:

  • Identify deregulated key transcription factors and their target genes using differential expression results in a case control setup.
  • Identify posttranscriptional regulatory mechanisms using integrated analysis of deregulated miRNAs and their mRNA and protein targets. 
  • Identify candidate causal variants for published and novel GWA loci using heart eQTL data, DNA methylation data and publicly available chromatin data in conjunction with computational sequence analysis.
  • Integrate the components of the AF associated regulatory networks, relate them to metabolite concentrations and translate results to potential blood-based omics markers.

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