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Abstract

Epigenetic marks are essential for maintaining cellular identity and functions, through regulation of chromatin structure and transcriptional activation. This process is especially relevant in early development during cell differentiation, where chromatin organization and DNA methylation (DNAme) patterns serve as “epigenetic barriers” to preserve cellular identity. Disruptions in epigenetic regulation are therefore commonly associated with atypical development. Yet, the direct mechanisms underlying the role of epigenetic dysregulation in pathology remain an area of active research. One challenge in the field of research originates from the plasticity of DNAme; specifically, as DNAme can be modified by factors including cellular identity, biological sex, age, and environmental factors, it can be difficult to tease apart the signals driving its variability. The overarching objective of my thesis is to develop bioinformatic tools to better dissect the sources of DNAme variability, with a focus on rare diseases of epigenetic origin. Chapter 2 describes a patient with compound heterozygous variants in ASXL1, combined immunodeficiency, and EBV-associated B-cell lymphoma. I reported an altered DNAme landscape and associated immune cell dysfunction associated with the patient’s clinical phenotypes. In Chapter 3, I curated population-specific reference DNAme datasets to enable detection of disease-associated DNAme signatures, comparing multiple statistical methods. Chapter 4 details the optimization of a cell type prediction pipeline for pediatric samples using machine learning, alongside the development of a user-friendly R package that empowers future research in pediatric and longitudinal immune dynamics. Finally, in Chapter 5, I synthesized current strategies for addressing cellular heterogeneity in DNAme analyses and proposed a set of best-practice guidelines. Together, this body of work supports more accurate identification of DNAme patterns associated with rare diseases and immune landscape alterations.