Zaugg Group

Personalised genomics to study genetic basis of complex diseases

The Zaugg Group investigates the variation of molecular phenotypes among individuals along with their genetic and epigenetic variation with the aim of better understanding the molecular basis of complex genetic diseases and inter-individual differences in drug response. We are applying and developing computational biology tools for integrating large-scale biological ‘omics data to address questions in personalised genomics and investigate basic gene regulatory mechanisms.

Previous and current research

Our research vision is to understand the causes underlying phenotypic variation (including disease) across individuals, with the goal of pushing the boundaries of precision medicine. We believe that a crucial part of precision medicine is to quantitatively understand the interplay of genetics, epigenetics and environmental factors, including interaction within the cellular microenvironment. The vast majority of genetic variants associated with complex traits are located in non-coding regions, and environmental factors often leave an epigenetic signature, it is not surprising that a growing number of diseases are recognised as being mediated through non-coding genomic elements. Therefore, one pillar of our research is focused on understanding the role of non-coding genomic elements in gene regulatory mechanisms, while the second pillar is focused on applying these models to understand basic biological processes and disease mechanisms.

Towards this goal we are developing a systems biology framework to predict how cells of a specific type and/or individual respond to defined perturbations, given their molecular and epigenetic profile. We envision that such a framework applied to precision medicine will shed light on patient-specific disease mechanism and We are particularly interested in employing it to study the interaction of cells – including immune cells – with their microenvironments, and how extrinsic signals interact with the cell-intrinsic signalling-regulatory networks.

The next layer we will need to tackle, is to understand cell-cell interactions and molecular processes that are triggered within the cells. Towards this goal we are embarking on studies where we integrate the spatial information both, on a tissue level, as well as intracellularly.

Future projects and goals

In the future we will expand our efforts to contribute 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 of chromatin in mediating genotype-environment interactions across individuals.
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.
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.