Thesis title: Dissecting Genotype-Phenotype Associations in DS Using WGS and WES Data
Down syndrome (DS), a condition caused by trisomy 21, serves as a valuable model for investigating the mechanisms by which chromosomal dosage imbalance leads to phenotypic variability and an increased risk of disease. The expression of genes on chromosome 21 has been demonstrated to perturb transcriptional and epigenetic regulation, thus contributing to a broad spectrum of comorbidities, including congenital heart defects, leukemia, immune dysregulation, and early-onset Alzheimer's disease. Advancements in genomic technologies, most notably Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS), have now rendered possible the identification of both coding and non-coding variants that underpin these conditions.
The present PhD project has been devised to elucidate the genetic mechanisms contributing to both chromosome 21–linked and non–chromosome 21–linked comorbidities in Down syndrome (DS). The integration of WES and WGS data facilitates the identification and interpretation of novel variants associated with the heterogeneous clinical presentation of DS. Utilising exploratory analyses, large-cohort association testing, and integrative interpretation, this work establishes a robust methodological framework for genotype–phenotype association studies in DS.
The OPBG cohort served as a pilot to develop and validate this analytical workflow, focusing on common conditions such as hypothyroidism, myopia, and hypermetropia. Building on this foundation, the analysis of the larger BIG cohort (n>130) expanded the investigation to congenital heart defects, recurrent respiratory infections, and other major comorbidities, uncovering candidate genes with potential roles in DS pathogenesis.
Overall, this thesis represents one of the first genome-wide efforts to investigate the genetic architecture of DS comorbidities beyond chromosome 21. By demonstrating the polygenic and multisystemic nature of these conditions, it provides both biological insights and a conceptual framework for future large-scale and multi-omic studies on genotype–phenotype relationships in DS.