The enormous amount of genetic and genomic data generated in the last decade shows great promise in our ability to understand better human diseases and improving public health. Yet, the genetic architecture of complex human phenotypes remains elusive, and important questions are still unanswered. Our research addresses methodological issues related to the analysis of large multidimensional data in genetics and genomics. It focuses on particular on the development and application of innovative methods that aim at i) improving association mapping in large genomics datasets where multiple correlated variables are measured across multiple biological levels; ii) allowing for the robust evaluation of causal models that include both genetic, genomic, clinical and environmental data; and iii) identifying and targeting discoveries that have the highest potential clinical utility.
We have developed several packages for the analysis of genetic data in human. Most of them are available here: https://gitlab.pasteur.fr/statistical-genetics