Extracting Clinically Relevant Information from WES and Array Data: A Path to Personalized Medicine

“It’s far more important to know what person the disease has than what disease the person has”.- Hippocrates (5th century B.C.). Almost 2,400 years ago, the Greek physician Hippocrates recognized the profound impact of individual variability on human health and disease. His holistic approach to medicine stressed the importance of understanding each patient's unique characteristics and advocating for a personalized approach to treatment. In the 21st century, we have taken Hippocrates' concept of precision medicine to a new level. Advances in genomic technologies have enabled us to study individual genomes on an extensive level. Moreover, the rapid and cost-effective sequencing of entire genomes and exomes has facilitated the development of vast repositories of patient genetic data. This knowledge is now being implemented to tailor treatments to individual genetic profiles, leading to more effective targeted therapies with improved patient outcomes. Driven by the promise of precision medicine, this dissertation focuses on the utility of SNP arrays and WES in extracting clinically relevant genetic information. Furthermore, it discusses the PRSs, their potential in clinical decision-making, and the integration of rare and common variants in studying monogenic diseases. These topics are covered in four chapters as follows. Chapter 1 provides a comprehensive overview of two prominent genotyping technologies: SNP arrays and WES. It briefly describes their technical aspects and general bioinformatic analysis pipelines. Moreover, the chapter delineates the characteristics of key population-based biobank studies, including the number of participants and the nature of data accessible within these repositories, to highlight the transformative influence of emerging novel technologies on modern research landscapes and their role in shaping personalized medicine. Chapter 2 focuses on the use of WES data in clinical settings. It describes traditional manual WES analysis followed by a published study that demonstrates its effectiveness in exploring the genetic basis of autism/intellectual disability. Furthermore, it introduces our novel approach of integrating WES data into a machine-learning model for identifying novel genetic determinants of COVID-19 susceptibility and severity. Chapter 3 shifts the focus to the utilization of SNP arrays in large-scale research, introducing PRS as the central argument. This part delves into a project of the INTERVENE consortium about the development and application of a novel PRS-based framework, emphasizing my contributions as a Genomics England biobank analyst. The population-based biobanks will again be highlighted, as they provide the foundation for this work. Additionally, Chapter 3 addresses the application of GWAS in uncovering SNP-phenotype relationships. The last chapter presents another project born within the INTERVENE consortium, focused on the attempt to integrate rare and common genetic variants in the study of two monogenic diseases, Rett and Alport syndrome. It first discusses the scientific basis of this novel approach, analyzing prior studies. Then, on those bases, it introduces our early efforts to investigate the clinical variability observed among patients with Rett and Alort syndrome. While the research is still in progress, this dissertation presents preliminary findings from our retrospective genotype-phenotype analysis for Rett syndrome and the utilization of generalized linear models using PRS for traits of interest for both Rett and Alport syndrome. In conclusion, this dissertation provides a thorough overview of the application of genotyping technologies in both clinical and research settings. It aims to bring to the forefront the potential of novel analytical approaches in advancing our knowledge of genetic contributions to disease, paving the way to personalized medicine.