Understanding the genomic and transcriptomic landscape of cardiomyopathy
File(s)
Author(s)
Zheng, Sean
Type
Thesis
Abstract
Cardiomyopathies are intrinsic heart muscle disorders characterised by abnormal cardiac structure and/or function. The two most common primary cardiomyopathies, hypertrophic (HCM) and dilated cardiomyopathy (DCM) have classically been considered to be Mendelian disorders resulting from inheritance of pathogenic variants. Despite this, genetic causes are identified in less than half of individuals with HCM and DCM. Recent genome-wide association studies (GWAS) have highlighted that in contrast to the established monogenic dogma, common variants play an important role in disease risk. Polygenic risk describes the cumulative effects of common variants, potentially explaining the cause of HCM and DCM in current “gene-negative” cases, and the incomplete penetrance and variable expressivity that is seen in carriers of pathogenic variants. Furthermore, the power of genomics can elucidate underlying biological mechanisms that may be important in disease pathogenesis and progression. The identification of causal genes at genomic loci provides insights into biological processes and mechanisms, while exploration of rare variants in candidate genes may unearth novel rare variant causes of disease.
While the use of genomics can yield insights into biological and cellular processes in disease, they remain indirect approaches. The use of single nuclei transcriptomics of diseased tissue allows us to define changes in cellular composition and transcriptional landscapes, with computational approaches highlighting other changes in biology, such as intercellular communication networks. We leveraged these methods to perform single nuclei transcriptomics in HCM and DCM samples. Finally, by characterizing the cellular gene expression profile of HCM and DCM, we integrate genomics to identify the cellular processes that drive genetic disease risk.
In summary, in this Thesis I have used genomics and transcriptomics of HCM and DCM to better characterise genetic architecture and identify novel disease associated genes, generated clinically applicable and effective polygenic risk scores, and identified changes in cellular composition and gene expression profiles.
While the use of genomics can yield insights into biological and cellular processes in disease, they remain indirect approaches. The use of single nuclei transcriptomics of diseased tissue allows us to define changes in cellular composition and transcriptional landscapes, with computational approaches highlighting other changes in biology, such as intercellular communication networks. We leveraged these methods to perform single nuclei transcriptomics in HCM and DCM samples. Finally, by characterizing the cellular gene expression profile of HCM and DCM, we integrate genomics to identify the cellular processes that drive genetic disease risk.
In summary, in this Thesis I have used genomics and transcriptomics of HCM and DCM to better characterise genetic architecture and identify novel disease associated genes, generated clinically applicable and effective polygenic risk scores, and identified changes in cellular composition and gene expression profiles.
Version
Open Access
Date Issued
2024-08-08
Date Awarded
01/10/2024
License URL
Advisor
Ware, James
Noseda, Michela
Cook, Stuart
Sponsor
British Heart Foundation
Medical Research Foundation
Publisher Department
National Heart and Lung Institute
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)