The focus of the group is on genetic epidemiology, population genetics and bioinformatics. It includes theoretical and methodological work as well as data analysis in collaborative projects. In particular, we cover the following topics:

Statistical methods development and application

Genetic and -omics data, such as generated by next-generation sequencing (NGS) and other platforms, cause a number of challenges to their analysis. This is due to their specifics, their quality, their frequently large size and their biological and medical interpretation. We develop novel methods for the single-layer as well as the integrative, multiple-layers statistical analysis of -omics data, such as genetic, epigenetic, transcriptional and metabolic data.

We apply these methods, alongside established ones, to data sets from ongoing biological and medical collaborations, with a focus on human data, including unrelated individuals as well as families.

Last publication(s)

• Oluyomi M Adesoji, Herbert Schulz, Patrick May, Roland Krause, Holger Lerche, Michael Nothnagel, ILAE Consortium on Complex Epilepsies. Benchmarking of univariate pleiotropy detection methods applied to epilepsy. 
  Hum Mutat (2022) https://pubmed.ncbi.nlm.nih.gov/35620985 PMID: 35620985

Human population genetics and ancient DNA (aDNA) analysis

Identification of deleterious variation and harmful interactions requires an understanding of what is normal. Also,ancestry and relatedness of individuals form the background atop of which pathological changes occur. We are therefore interested in the analysis of human genetic data to empower in the interpretation of disease-related analysis results, but also to elucidate spatial and phylogeographic patterns of human genetic variation.

Last publication(s)

• Villaescusa et al. A Y-chromosomal survey of Ecuador’s multi-ethnic population reveals new insights into the tri-partite population structure and supports an early Holocene age of the rare Native American founder lineage C3-MPB373. Forensic Sci Int Genet, Epub ahead of print. https://doi.org/10.1016/j.fsigen.2020.102427

Forensic genetic statistics

Automation of marker genotyping and novel types of genetic markers in forensic statistics potentially offer new options for kinship testing, identification of individuals and other purposes. As a minor focus of the group, we aim at addressing statistical issues in forensic applications.

Last publication(s)

• Katsara et a. (2021) Evaluation of supervised machine-learning methods for predicting appearance traits from DNA. Forensic Sci Int Genet, https://doi.org/10.1016/j.fsigen.2021.102507