Lude Franke

 

D42

Professor Lude Franke

Title of Presentation

Integration and visualisation of multiple biobank-based omics datasets

 

Date and Place

Session: C4 – 1

 

Speaker Biography

Lude Franke is professor at the Department of Genetics, University Medical Centre Groningen. He works on the development of methods for integrating diverse omics-datasets to better understand the downstream molecular consequences of genetic risk factors. Over the last ten years he worked on the development of computational methods to understand through which intermediate molecular levels genetic variants exert their effect on phenotypes, by correlating genotypes with gene expression levels and methylation levels. This work has shown that most human genetic risk factors for disease are regulatory and thus alter gene expression levels (Dubos et al, Nature Genetics 2010), indicating that through a better understanding of gene expression regulation novel biological insight can be obtained. Through a large-scale blood expression quantitative trait locus (eQTL) meta-analysis consortium (eQTLGen) that he initiated in 2010 he subsequently identified downstream (trans-eQTL) effects for over 200 different genetic risk factors (Westra et al, Nature Genetics 2013). This led to the identification of downstream pathways that are affected by individual genetic risk factors, and provides leads for follow-up research and pharmaceutical intervention. He recently identified downstream effects for 2,000 genetic risk factors on methylation (Bonder et al, BioRXiv preprint) and the discovery that gene x environment interactions are very common in human gene expression (Zhernakova et al, BioRXiv preprint). Another focus is the development of novel methods to reuse existing publicly available data. He recently integrated gene expression data from 80,000 microarrays to accurately predict gene functions and to gain better insight in cancer (Fehrmann et al, Nature Genetics 2015), developed DEPICT (Pers et al, Nature Communications 2015) to use these predicted gene functions to better interpret GWAS findings, and recently reanalyzed 10,000 RNA-seq samples (Deelen et al, Genome Medicine 2015) to investigate how genetic variation and bacterial and viral infections interact to alter gene expression levels.

Abstract

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