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Zoltan Kutalik

Associate professor, the University of Lausanne, Switzerland and University of Exeter, UK

Zolt├ín Kutalik, PhD is a statistical geneticist, associate professor at the University of Lausanne, heading the Statistical Genetics Group and honorary senior lecturer at the University of Exeter. His main research interest lies in developing statistical methods integrating various data modalities to better understand the genetic architecture of complex human diseases. He is a council member of the Swiss Institute of Bioinformatics (SIB), scientific programme committee member of the ESHG, EMGM, BC2, ISCB conferences and evaluation committee member of the Swiss National Science Foundation (SNSF). Zoltan is on the advisory board of the LongITools EU project, the CoLaus study, he is member of the science council of the Health 2030 Genome Center, an editorial board member of PLoS Genetics, Human Molecular Genetics, EJHG and ASHG. He won the Early Career Bioinformatician Award of the SIB, the Investigator-in-training award of the University of Lausanne and shared the Leenaards Prize. Five of his group members won the Lodewijk Sandkuijl Award of the ESHG in the past years. He published >200 peer-reviewed articles (>55,000 citations, h-index>100) in international scientific journals (incl. Nature, Nature Human Behavior, Nature Genetics and Nature Communications). His research has been financed by the SNSF, the SIB, the and the Leenaards Foundation. 

Mendelian Randomisation: connecting diseases, omics layers, drugs and even humans

In this talk I will give a high-level overview of many applications of Mendelian Randomisation (MR). I will demonstrate how this tool can reveal metabolic networks, how genetic signals propagate across omics layers, and facilitate drug target predictions. These discoveries are possible thanks to the ability of MR to integrate quantitative trait loci (QTLs) for complex traits with QTLs for a wide range of molecular (omics) phenotypes. The MR framework also enabled the elucidation of the behavioural mechanisms behind study participation and self-reporting errors. Furthermore, its flexibility allowed MR to be employed to infer human interactions (between family members). While this method is amenable to many applications, its assumptions are often violated, which can lead to biased causal effect estimates. I will discuss different extensions of MR that can improve inference in case of non-linear, heterogeneous causal effects or pleiotropic instruments. Finally, I will emphasise that flexible applications of MR must be accompanied with ever-improving extensions allowing for more realistic modelling assumptions.