Statistical genetics is a quickly developing field involving massive amounts of data (molecular markers, gene expression, proteomics, metabolomics, DNA and RNA sequences, high-throughput phenotyping, etc) that calls for advanced analytical methods. This is one of the main research topics in our group, aiming to develop methodology for sound inference for genetic, genomic and phenotypic data.
We combine a wide expertise in statistics (linear / non-linear models, mixed models and Bayesian statistics) with a diverse educational background (mathematic, statistics, biology, agronomy).
We research methods applied to a wide range of biological data, including simple and complex populations (NAM, MAGIC, etc), and often under a multivariate setting (multiple environments and/or traits). Our research is done within several national and international projects, collaborating with research teams in the academia and industry..
We also participate in training and education via regular courses at the University (BSc, MSc, and PhD), but also via specific courses targeting the academia and industry (genetic linkage mapping, quantitative trait locus (QTL) mapping and association mapping.