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Promoter-enhancer interactions are essential for gene regulating, Capture Hi-C is a chromosome conformation capture method to map promoter-enhancer interactions at high resolution. We have Capture Hi-C data forGM12878 cells, immortalized primary B lymphocytes, in three replicates. Although Capture Hi-C maps enhancer elements together with the promoters they regulate, the overlap between enhancer datasets produced by other methods such as ChIP-seq and Capture Hi-C is lower than expected. In order to understand the reasons for lower overlap, we investigated the enhancer potential of replicated and non-replicated Capture Hi-C interactors, as well as enhancer overlapping and non-overlapping Capture Hi-C interactors. We performed a systematic comparison between our interactor and experimental regulatory and transcriptomic datasets to determine the extent of enhancer mapping. The results show replicated interactors have higher enhancer potential than non-replicated ones. However, there is evidence that interactors not overlapping with experimental validated regulatory datasets can also potentially be true enhancers.

Genetic associations have been discovered for many human complex traits, and yet for most associated loci the causal variants and molecular mechanisms remain unknown. Studies mapping quantitative trait loci (QTLs) for molecular phenotypes, such as gene expression, RNA splicing, and chromatin accessibility, provide rich data that can link variant effects in specific cell types with complex traits. These genetic effects can also now be modeled in vitro by differentiating human induced pluripotent stem cells (iPSCs) into specific cell types, including inaccessible cell types such as those of the brain. In this thesis, I explore a range of approaches for using QTLs to identify causal variants and to link these with molecular functions and complex traits. In Chapter 2, I describe QTL mapping in 123 sensory neuronal cell lines differentiated from human iPSCs. I observed that gene expression was highly variable across iPSC-derived neuronal cultures in specific gene categories, and that a portion of this variability was explained by commonly used iPSC culture conditions, which influenced differentiation efficiency. A number of QTLs overlapped with common disease associations; however, using simulations I showed that identifying causal regulatory variants with a recall-by- genotype approach in iPSC-derived neurons is likely to require large sample sizes, even for variants with moderately large effect sizes. In Chapter 3, I developed a computational model that uses publicly available gene expression QTL data, along with molecular annotations, to generate cell type-specific probability of regulatory function (PRF) scores for each variant. I found that predictive power was improved when the model was modified to use the quantitative value of annotations. PRF scores outperformed other genome-wide scores, including CADD and GWAVA, in identifying likely causal eQTL variants. In Chapter 4, I used PRF scores to identify relevant cell types and to fine map potential causal variants using summary association statistics in six complex traits. By examining individual loci in detail, I showed how the enrichments contributing to a high PRF score are transparent, which can help to distinguish plausible causal variant predictions from model misspecification.

Les microARN sont des petits ARN non-codant impliqués dans la régulation de multiples fonctions biologiques dont la réponse immunitaire. L'infection par un pathogène induit un changement transcriptomique fort chez l'hôte. Cependant, la variabilité de ces dérégulations reste encore mal décrite. Cette thèse avait pour principaux objectifs de mieux comprendre la spécificité et la variabilité de la réponse des microARN chez l'homme ainsi que mettre en évidence les bases génétiques de cette diversité en utilisant comme modèle l'infection des cellules dendritiques par Mycobacterium tuberculosis (MTB). Nous avons utilisé une approche ex vivo et des techniques à haut débit dans le but de décrire la réponse des microARN suite à l'infection par MTB dans la population générale, et de la comparer à celle induite par d'autres mycobactéries et bactéries intracellulaires. Nous montons que l'infection modifie profondément l'expression des microARN ainsi que la diversité de leurs isoformes, dont un certain nombre de microARN sont impliqués dans une réponse très conservée. Nos résultats soulignent aussi l'effet de l'infection sur les réseaux de régulation de l'expression des gènes impliquant les microARN et montrent que l'expression de 3% de ces transcrits peut-être corrélée à un marqueur génétique. Grâce à l'intégration de ces différentes analyses, nous proposons certains microARN candidats qui pourraient jouer un rôle dans la variabilité de la réponse immunitaire. L'ensemble de nos résultats constitue la première tentative de compréhension de l'architecture génétique de la réponse des microARN et apporte un nouvel éclairage sur le rôle de ces transcrits dans la réponse antibactérienne. / MicroRNAs (miRNAs) are important epigenetic regulators of gene expression that play a key role in many biological processes, including the immune response. Although infection is accompanied by marked changes in the transcriptional profiles of host cells, little is known about the variability of host miRNA responses to infection. In this thesis, we aimed to define the extent and specificity of pathogen-induced miRNA transcriptional responses of host cells, and to characterise the genetic basis of miRNA variability upon infection, using the model of Mycobacterium tuberculosis (MTB) infection of human dendritic cells. To this end, we have combined ex vivo approaches with a range of high-throughput genomic techniques to profile miRNA responses to MTB at the population-level and to compare this response with other mycobacterial and non-mycobacterial infections. We show that miRNAs display marked changes in expression and in isomiR distribution upon infection that are highly consistent across diverse bacteria, demonstrating the presence of a strong core miRNA response to bacterial infection. Our results highlight the impact of infection on miRNA-mediated gene regulatory networks and show that the expression of 3% of miRNAs are controlled by proximate expression quantitative trait loci (eQTLs) and identify a number of candidate miRNAs that may play a role in variability in the immune response to infection. Together, these results provide the first assessment of the impact of genotype-environment interactions on the regulation of miRNA expression, as well as offering novel insights into the specificity of these miRNAs in the response to mycobacterial infections.

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