TranSYS Training School 25th November 09.00

Prof Lude Franke

 Full Professor of Functional Genomics in the Department of Genetics

University Medical Centre Groningen

Wednesday 25th November 2020  09.00 CET

Reconciling germline and somatic variation through regulatory network integration


One strategy to do this is by performing expression quantitative trait locus (eQTL) mapping that permits the identification of the local (cis) and distal (trans) effects of genetic variants on gene expression levels. To identify these effects, we initiated a large blood eQTL meta-analysis consortium (eQTLGen, Westra et al, Nature Genetics 2013) that revealed both cis- and trans-eQTL effects for many genetic risk factors. We studied different characteristics of these molecular downstream effects, and observed that genetic variants often affect gene expression levels only in specific cell types and that these effects are highly context-specific (Zhernakova et al, Nature Genetics 2017, Bonder et al, Nature Genetics 2017). Another focus has been the development of novel methods to reuse publicly available data. We integrated gene expression data from 80,000 microarrays to accurately predict gene functions and gain better insight into somatic mutations in cancer (Fehrmann et al, Nature Genetics 2015). My group also developed DEPICT (Pers et al, Nature Communications 2015), which uses these predicted gene functions to better interpret GWAS findings. We recently re-did this analysis using 31,000 publicly available RNA-seq samples to make functional inferences about non-coding genes that are usually not captured well on microarrays. We also used this method to examine the clinical symptoms that mutations within these genes might cause and showed these predicted clinical features can help to increase the diagnostic yield of clinical exome-sequencing (Deelen et al, Nature Communications 2019). His group is currently concentrating on integrating large-scale multi-omics datasets by conducting large-scale trans-QTL meta-analyses in >30,000 samples (Vosa et al, BioRXiv 2018) in conjunction with single-cell RNA- seq data (Van der Wijst, Nature Genetics 2018) with the principal aims to conduct eQTL meta-analysis and to reconstruct personalized regulatory networks that can be used to better understand disease-associated genetic variants. To do this optimally, we have now initiated the single-cell eQTL consortium where >20 international research groups work towards a large-scale federated cell-type specific eQTL analysis in >3,000 samples and reconstruction of cell-type specific gene regulatory networks (Van der Wijst, eLife 2020).



Lude Franke is Full Professor of Functional Genomics in the Department of Genetics, University Medical Centre Groningen. His group focuses on multi-omics data generation and analysis, with a particular interest in the development of computational methods to identify the downstream molecular effects of these disease-associated genetic variants.