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:

• developed the computational tool sTRAP for the identification of causative cis regulatory variants affecting transcription factor binding (Manke*, Heinig* Hum Mutat 2010) and successfully applied this tool in a disease gene study (Monti Nat Genet 2008) for heart failure,
• developed a statistical approach for the identification of a transcription factor driven regulatory network, including its master regulator and the interpretation of disease association (type 1 diabetes) using this regulatory network (Heinig Nature 2010),
• performed an integrated analysis of the consequences of genetic variation for multiple levels of epigenetic and transcriptional regulation (Rintisch*, Heinig* Genome Res 2014),
• developed the computational tool histoneHMM for the identification of differentially modified regions for histone modifications with broad genomic footprints (Heinig BMC Bioinformatics 2015).
• developed predictive models to identify functional genomic elements predictive of regulatory variants (Budach, Heinig* and Marsico* Genetics 2016)
• performed one of the largest eQTL studies to date in the human heart (Heinig Genome Biology 2017)

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.