Figure 1: An example region on chromosome 10 displaying the HiC score (blue), local histone QTLs (on the diagonal) and distal hQTLs (off diagonal). Most distal-QTLs lie within the same chromatin domain as their target peak.

Figure 1: An example region on chromosome 10 displaying the HiC score (blue), local histone QTLs (on the diagonal) and distal hQTLs (off diagonal). Most distal-QTLs lie within the same chromatin domain (indicated in black squares) as their target peak.

The Zaugg group investigates the variation of molecular phenotypes among individuals along with their genetic variation with the aim of better understanding the molecular basis of complex genetic diseases and inter-individual differences in drug response.

Previous and current research

One of the continuing challenges in biomedical research, in particular in translating personalised molecular medicine to the clinic, is to understand the contribution of genetic variation to hereditary traits and diseases. Genome-wide association studies have revealed thousands of associations between genetic variants and complex diseases. However, since most of these variants lie in non-coding parts of the genome, our understanding of the molecular mechanisms underlying these associations is lagging far behind the number of known associations.

To gain a better mechanistic insight into potential causes of known genotype-disease associations, our lab is investigating the variability of molecular phenotypes among individuals and trying to link them to genetic variation. In addition, since many of the disease-associated SNPs are located in regulatory elements, we have a general interest in understanding gene regulatory mechanisms.

Our recent findings indicate that about 15 percent of all regulatory elements, measured through chromatin marks by ChiP-Seq, have a genetic basis, thus challenging the traditional view of chromatin being an epigenetic mark. Many of these so-called histone quantitative trait loci (hQTLs) also have an effect on distal genes or regulatory elements (A) through a mechanism that is likely mediated by transcription factors (B). Importantly, these hQTLs are highly enriched for SNPs that have previously been found to associate with complex traits or diseases, highlighting the functional significance of studying inter-individual variation of molecular phenotypes. We are currently investigating potential mechanisms, such as enhancer compensation models as well as transcript isoform variation, to understand the complex relationship between gene expression and regulatory elements.

Future projects and goals

In the future we will expand our efforts to contributing to the understanding of complex traits and diseases along three lines of research:

  • We will apply our models to current genome-wide association studies to increase our power of understanding known associations between genetic variants and complex diseases.
  • We will expand our models to include more downstream molecular phenotypes, such as protein levels and complex composition, to estimate the impact of genetic variation on biological pathway activity.
  • We will use drug response as a model to investigate the role chromatin in mediating genotype-environment interactions across individuals.
Figure 2: H3K27ac ChIP-seq signal surrounding H3K27ac QTLs was extracted and grouped into six clusters.

Figure 2: H3K27ac ChIP-seq signal surrounding H3K27ac QTLs was extracted and grouped into six clusters. The aggregate signals for the six clusters are shown for the high-, heterozygous- and low-genotypes (blue, purple, red) for H3K27ac, H3K4me3, H3K4me1, and DNase hypersensitivity sites (DHS) (left to right). The QTL SNPs lie in the nucleosome free regions, indicating that TFs might be driving hQTLs.