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Genetic and Epigenetic Gene Regulation

Heinig Lab












The Heinig lab is located at the Insitiute of Computational Biology, which is part of the Computational Health Center of Helmholtz Munich.

Together with my research group, I develop AI solutions for personalized network based precision medicine.

Technological advances allow for an unprecedented in-depth characterization of the molecular basis of complex diseases. In particular SNP genotyping, DNA methylation assays and gene expression profiling in large cohorts have been used to identify numerous disease associated loci and genes. However, a deeper mechanistic or systems level understanding of disease processes still remains elusive in most cases.

The aim of our research is the development and application of computational and statistical tools for the identification of molecular regulatory networks underlying common diseases and the genetic and epigenetic mechanisms controlling these networks from population level DNA and multi-omics data sets. In a second step we aim to personalize the networks based on single cell data. This will enable us to implement new concepts for precision medicine. A special focus is the molecular characterization of metabolic and cardiovascular diseases, in particular diabetes and arrhythmias like atrial or ventricular fibrillation.

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:

Using single cell trancriptomics to personalize gene regulatory networks

Single cell RNA-seq not only enables us to explore the individual celltypes and their transcriptional programs. It also enables to study the effects of common and rare gene variation with celltype resolution. Importantly, it enables us to personalize gene regulatory networks.

Current approaches infer a single network, which can be thought of as a static reference for the whole population. In reality however, inter-individual differences in the genome and the environment are expected to cause differences in the network topology. Therefore, not just a single reference core gene network but a personalizable core gene network is required for precision medicine applications. Single cell RNA-seq measures the full transcriptomes of multiple cells of the same person. This allows to infer person and celltype-specific gene regulatory networks.

To fully leverage the potential of single cell data we have:

Identifying disease core genes and their networks

Complex traits are associated with houndreds if not thousands of non-coding variants throughout the whole genome. Theoretical models such as the omnigenic core gene model have been proposed to reconcile this observed genetic architecture with the potential molecular mechanisms: when small effects of multiple risk loci converge on the same downstream core genes in regulatory networks, a large proportion of the heritability can be explained. The key challenges are that downstream targets are difficult to identify using QTL data and that the core genes for specific diseases are unknown.

To adress these challenges, we have:

  • developed Speos - a graph neural network approach to predict core genes of complex disease from network, GWAS and gene expression data (Ratajzcak bioRxiv 2023) - code - docs
  • developed a data integration approach that makes use of polygenic risk scores and pathway annotations to identify trans-acting QTL from protein and transcript expression data. We applied it to the atrial fibrillation cohort of the symAtrial consortium to identify candidate core genes of atrial fibrillation (Assum Nature Communications 2022) - code


2022 Nature Genetics DOI: 10.1038/s41588-021-00969-x

Hawe JS*, Wilson R*, Schmid KT*, Zhou L, Lakshmanan LN, Lehne BC, Kühnel B, Scott WR, Wielscher M, Yew YW, Baumbach C, Lee DP, Marouli E, Bernard M, Pfeiffer L, Matías-García PR, Autio MI, Bourgeois S, Herder C, Karhunen V, Meitinger T, Prokisch H, Rathmann W, Roden M, Sebert S, Shin J, Strauch K, Zhang W, Tan WLW, Hauck SM, Merl-Pham J, Grallert H, Barbosa EGV; MuTHER Consortium, Illig T, Peters A, Paus T, Pausova Z, Deloukas P, Foo RSY, Jarvelin MR, Kooner JS, Loh M†, Heinig M†, Gieger C†, Waldenberger M†, Chambers JC†.

Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function


Dr. Matthias Heinig

Junior Group Leader

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