Genome-wide association studies (GWAS) have resulted in the identification of several dozens of genes and single nucleotide polymorphisms (SNPs) contributing to type 2 diabetes.
More generally, GWAS have identified thousands of SNPs contributing to complex diseases in humans.
However, the functional characterisation and biological mechanisms involving these SNPs and genes remain largely to be explored.
Indeed, the consequences of these polymorphisms are complex and little known.
One direct consequence of the SNPs is the alteration of the protein encoded by a gene, or even a complete transcriptional gene silencing (e.g., codon stop in the sequence).
Furthermore, these polymorphisms may have a regulatory role in gene expression, for example, by interfering with the binding of transcription factors and enzymes involved in DNA methylation.
Despite the strong associations of identified SNPs, they cannot explain the full heritability of type 2 diabetes, suggesting interactions mechanisms between the different layers of -omics, such as genomics, transcriptomics and Epigenomics.
The shift of paradigm in statistical genetics and the availability of transcriptomic and epigenomic data are responsible for the evolution of the discipline, moving from association studies to multi-omics, and providing insights on the functional aspect of the SNPs or genes involved, and in some cases allowing to evaluate the causal link of these variants on the pathology. The methodological developments and their applications proposed in this thesis are various, ranging from a similar approach to GWAS, leveraging the longitudinal data available in some cohorts (e.g., D.E.S.I.R.), using an joint model approach; the functional characterisation of candidate genes in insulin secretion by a multi tissue transcriptomic study and transcriptomic study in a cell model; the identification of a new candidate gene (PDGFA) involved in the deregulation of the insulin’s pathway in type 2 diabetes through epigenetic and transcriptomic mechanisms; and finally, the characterisation of the effect on the transcriptome of two substitutes of bisphenol A in a primary adipocyte model.
The increase of knowledge in biological processes involving SNPs and genes identified by GWAS could enable the development of more effective diagnostic strategies, and the identification of therapeutic targets for the treatment of type 2 diabetes and associated complications (e.g., insulin resistance, NAFLD, cancer, etc.). More generally, these multi-omics studies pave the way for the emerging approach of precision medicine, allowing the treatment and prevention of pathologies while taking into account what makes the specificity of an individual, namely his genome and his environment, both interacting on his transcriptome and his epigenome.