Ip, Eddie (2020) Using Computational Methods to Identify the Genetic Causes of Congenital Heart Disease. PhD thesis, Victor Chang Cardiac Research Institute & St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney.
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Abstract
Congenital heart disease (CHD) is a complex disease affecting the structural development of the heart and major vessels. It is the leading cause of infant morbidity in the Western world and affects up to 1% of all live births. Genetic studies using existing sequencing techniques have tried to understand the causes of CHD, but because of its complex nature, findings are limited. Recent progress in DNA sequencing, called whole-genome sequencing (WGS), has allowed rapid, cost-effective sequencing of the complete human genome. This study investigates a CHD-affected cohort recruited at the Victor Chang Cardiac Research Institute. It comprises 97 Australian families that had WGS performed on their genomes. I applied multiple computational methods to analyse this cohort to identify the genetic causes of CHD development. In using WGS data, the most noticeable difference to whole-exome sequencing (WES) is the vast number of variants that can be called. To make sense of this volume of data, I developed a software tool called VPOT—the Variant Prioritisation Ordering Tool. By prioritisation, a multitude of high-candidate variants was reduced to a more manageable number for family-based variant analysis. This was followed by an association study that used statistical strategies to provide a bias-free genome-wide approach to the identification of genes or pathways that might be associated with the development of CHD. VPOT was also used to create different pathogenicity and minor allele frequency datasets. WGS data incorporates the sequencing of the mitochondrial DNA. This allows for the investigation of CHD-related variants in mitochondrial DNA. As a preliminary stage, I evaluated four mitochondrial variant caller software applications to determine the best-in-class. Reanalysis using new and improved methodologies can often improve diagnostic rate. To this end, I investigated a new, deep neural network-based variant caller for single samples and repurposed it for family trios, so it can be used to re-analyse the WGS in the future. The work in this thesis not only helps identify the genetic causes of CHD but also expands the bioinformatics toolkit available to genetic researchers. I hope that the work in this thesis leads to discoveries that solve more CHD cases.
| Item Type: | Thesis (PhD ) |
|---|---|
| Subjects: | R Medicine > R Medicine (General) |
| Depositing User: | Repository Administrator |
| Date Deposited: | 30 Sep 2021 05:03 |
| Last Modified: | 30 Sep 2021 05:14 |
| URI: | http://eprints.victorchang.edu.au/id/eprint/1133 |
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