Integrative methods for linking genotype to phenotype: an application to intervertebral disc degeneration

Intervertebral disc degeneration (IDD) is a complex condition influenced by genetics, environment, and lifestyle. Despite advancements in sequencing technologies, challenges remain in understanding the biology of complex diseases. The increasing use of artificial intelligence has led to innovative approaches in studying these conditions, benefiting translational medicine, drug discovery, and personalized treatment. This thesis introduces novel computational frameworks for studying the genetics of complex disorders, including a tool based on knowledge graph embeddings and Genopyc, a Python library for variant analysis. We exploited these tools in the study of biological underpinnings of IDD. Additionally, we investigated the autoimmune basis of Modic change, a comorbidity of IDD, using TwinsUK, the largest twin cohort worldwide. Our findings demonstrate the successful utility of these computational tools in advancing the understanding of IDD biology, emphasizing the importance of integrating diverse data sources to achieve a comprehensive understanding of this intricate condition.