The European Mathematical Genetics Meeting 2021 (Virtual Meeting)
The European Mathematical Genetics Meeting 2021 will take place at the Ecole normale supérieure in Paris’ Latin quarter on 22-23 April 2021. This annual meeting offers an informal environment to exchange ideas on the use of mathematical techniques in the field of genetics. The 2021 edition will put an emphasis on the use of bioinformatics approaches for human genome data.
Due to the Covid-19 pandemic, the meeting will take place online and free of charge
Registrations close: 31 March 2021
Abstract submissions close: 28 February 2021
Registration to the EMGM2021 meeting is free but mandatory. After registering, please submit an abstract using the Submissions menu (400 words maximum) by copy/pasting your text into the form. Please do not enter keywords, reference or contact details in the abstract text (you should provide keywords in a separate entry on the form).
For more information and to register, visit the event webpage.
Inserm UMR1078, Génétique, Génomique fonctionnelle et Biotechnologies
Faculté de Médecine, Brest, France
Inserm NeuroDiderot UMR1141
Université de Paris, Paris, France
Centre de recherche en Epidémiologie et Santé des Populations, Villejuif, France
Hugues Roest Crollius
Institut de Biologie de l’ENS
Ecole Normale Supérieure, Paris, France
Eleftheria Zeggini obtained a BSc in Biochemistry and a PhD in Immunogenetics of Juvenile Arthritis from the University of Manchester. In 2004, she joined the Wellcome Centre for Human Genetics in Oxford to work on the genetics of type 2 diabetes, and on design, analysis and interpretation issues in large-scale association studies. In 2008, she joined the Wellcome Sanger Institute Human Genetics Faculty and in 2018 moved to Munich as founding director of the Institute of Translational Genomics at the Helmholtz Zentrum München. In 2020, she was also appointed Chair of Translational Genomics at the TUM School of Medicine and received the TUM Liesel Beckmann Distinguished Professorship. Her research leverages big data and aims to translate insights from genomics into mechanisms of disease development and progression, shortening the path to translation and empowering precision medicine.
Ernest Turro is an Associate Professor in Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on the development and application of statistical methods for interpreting genomic and phenotypic data. Previously, at the University of Cambridge, he was the lead biostatistician of the UK’s NIHR-funded pilot project for the 100,000 Genomes Project. During that time, he made contributions towards understanding the genetic causes of rare hereditary disorders and he supervised the bioinformatic component of a diagnostic platform for patients with bleeding and platelet disorders. Prior to this, at Imperial College London and at Cambridge, Ernest developed a number of influential statistical methods for modelling gene expression data.
Catherine Bourgain is a researcher at the French National Institute for Health and Medical Research (INSERM) and the director of the “Centre de Recherche Médecine, Sciences, Santé, Santé Mentale, Société” (Cermes3 ; CNRS, EHESS, Inserm, Univ. Paris). Trained in population genetics, statistics and epidemiology, she has worked for ten years in genetic epidemiology, developing statistical methods for the genetic characterization of complex diseases. In 2013, she joined the Cermes3 and started a series of collective and multidisciplinary social science studies on high-throughput genomics in the context of care, including oncology and cardiovascular diseases.
Hugues Aschard is the head of the Statistical Genetics group in the department of Computational Biology at the Institut Pasteur. His research focuses on the development and the application of novel methods for the integration of biological, clinical and environmental data to understand the genetic architecture of multifactorial diseases in human. It combines in-depth theoretical work related to association screening, risk prediction and causal inference, and discovery-oriented studies that aim at answering specific biological questions. The methods he develops focus in particular on multivariate analysis, that is, the combined analysis of multiple outcomes and multiple predictors, and approaches allowing for interaction effects between risk factors.