Exploring pleiotropy at variant and gene-level by using adapted meta-analysis approaches.

Résumé : One major finding from genome-wide association studies (GWAS) era is that pleiotropy – that occurs when one gene influence two or more unrelated traits - is a widespread phenomenon in human complex traits. The study of common genetic risk factors between multiple diseases could help to better understand these diseases by the identification of novel genes and biological pathways involved. Moreover, an increasing number of GWAS summary statistics is made available to the scientific community, and exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In this presentation, I will present our ongoing project on development of novel big data analytics methods for leveraging pleiotropy using specific data structures (gene or pathway-level) and especially my ongoing work on development of meta-analysis approaches for pleiotropy analysis by using summary statistics from GWAS results.