Bioinformatic and Biostatistic Analysis of Epigenetic Data from Humans and Mice in the Context of Obesity and its Complications

Worldwide obesity has more than doubled since 1980 and at least 2.8 million people die each year as a result of being overweight or obese. An elevated body weight is the result of the interplay between susceptibility gene variants and an obesogenic environment, and recent evidence shows that epigenetic processes are likely involved. The growing availability of high-throughput technologies has made it possible to assess quickly the entire epigenome of large samples at a relatively low cost. As a result, vast amounts of data have been generated and researchers are now confronted to both bioinformatic and biostatistic challenges to make sense of such data in the context of obesity and its complications. In this doctoral thesis, we explored associations between the human blood methylome and obesity-associated gene variants as well as dietary fat quality and quantity. We used well described preprocessing techniques and statistical methods, along with publicly available data from consortiums and other research groups, as well as tools for pathway enrichment and chromatin state inference. We found associations between obesityassociated SNPs and methylation levels at proximal promoters and enhancers, and some of these associations were replicated in multiple tissues. We also found that contrary to dietary fat quantity, dietary fat quality associates with methylation levels in the promoter of genes involved in metabolic pathways. Then, using a gene-targeted approach, we looked at the impact of an acute environmental stress (sleep loss) on the methylation and transcription levels of circadian clock genes in skeletal muscle and adipose tissue of healthy men. We found that a single night of wakefulness can alter the epigenetic and transcriptional profile of core circadian clock genes in a tissue-specific manner. Finally, we looked at the effects of chronic maternal obesity and subsequent weight loss on the transcription of epigenetic machinery genes in the fetus and placenta of mice. We found that the transcription of epigenetic machinery genes is highly sensitive to maternal weight trajectories, and particularly those of the histone acetylation pathway. Overall, this thesis demonstrated that genetics, obesogenic environment stimuli and maternal programming impact epigenetic marks at genomic locations relevant in the pathogenesis of obesity