Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.693886
Title: Genomic dissection of arrhythmia and cardiac electromechanics
Author: Ware, James
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2012
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Abstract:
Cardiac arrhythmia is a leading cause of death in the developed world and a final common pathway for many forms of cardiac disease. Rare inherited arrhythmia syndromes contribute to this disease burden, particularly through sudden death in the young. The study of rare syndromes, such as inherited arrhythmia, can also identify genes and pathways important in common diseases. Here, genomic approaches were applied to dissect genetic determinants of cardiac arrhythmia, through gene discovery, variant discovery, and variant annotation. First, whole-exome sequencing was used to identify the genetic basis of an unexplained inherited arrhythmia syndrome. Linkage analysis and conventional sequencing excluded known causative genes in a family with Brugada Syndrome, and whole exome sequencing identifie d a shortlist of five new candidate genes that may lead to a genetic diagnosis in this family and new insights into the pathogenesis of the condition. Following the identification of genes responsible for inherited arrhythmia syndromes, the recognition of specific disease-causing variants in those genes allows for clinical application, including molecular diagnosis, cascade screening and stratified therapy. Here, two high-throughput next-generation sequencing approaches for the detection of variants in these genes were compared, technically evaluated, and optimis ed. This represents the de novo establishment of next-generation sequencing technologies and analysis pathways in our laboratory, and provides a platform for molecular diagnosis and future genotype-phenotype correlation studies. Finally, a novel approach for the functional annotation of non-synonymous variants was developed. This approach, termed 'Paralogous Annotation', identifies functionally important, disease-associated residues across protein families using multiple sequence alignment. Paralogous Annotation was validated here by demonstrating the accurate identification of disease-causing variation in genes that cause long QT syndrome - an important cause of sudden death. This methodology is widely applicable to annotate Mendelian human disease genes.
Supervisor: Peters, Nicholas ; Aitman, Tim ; Cook, Stuart Sponsor: Wellcome Trust ; Medical Research Council ; British Heart Foundation
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.693886  DOI: Not available
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