Congenital heart defects (CHD) are structural anomalies affecting the heart, are found in 1% of the population and arise during early stages of embryo development. Without surgical and medical interventions, most of the severe CHD cases would not survive after the first year of life. The improved health care for CHD patients has increased CHD prevalence significantly, and it has been estimated that the population of adults with CHD is growing ~5% per year. Understanding the causes of CHD would greatly help improve our knowledge of the pathophysiology, family counseling and planning and possibly prevention and treatment in the future. The aim of my thesis was to identify novel or known CHD genes enriched for rare coding genetic variants in isolated CHD cases and learn about the relative performance of different study designs. High-throughput next generation sequencing (NGS) was used to sequence all coding genes (whole exome) coupled with various analytical pipelines and tools to identify candidate genes in different family-based study designs. Since there is no general consensus on the underlying genetic model of isolated CHD, I developed a suite of software tools to enable different family-based exome analyses of de novo and inherited variants (chapter 2) and then piloted these tools in several gene discovery projects where the mode of inheritance was already known to identify previously described and novel pathogenic genes, before applying them to an analysis of families with two or more siblings with CHD. Based on the tools developed in chapter 2, I designed a two-stage study to investigate isolated parent-offspring trios with Tetralogy of Fallot (chapter 3). In the first stage, I used whole exome sequence data from 30 trios to identify genes with de novo coding variants. This analysis identified six de novo loss-of-function and 13 de novo missense variants. Only one gene showed recurrent de novo mutations in NOTCH1, a well known CHD gene that has mostly been associated with left ventricle outflow tract malformations (LVOT). Besides NOTCH1, the de novo analysis identified several possibly pathogenic novel genes such as ZMYM2 and ARHGAP35, that harbor de novo loss-of-function variants (frameshift and stop gain, respectively). In the second stage of the study, I designed custom baits to capture 122 candidate genes for additional sequencing using NGS in a larger sample size of 250 parent-offspring trios with isolated Tetralogy of Fallot and identified six de novo variants in four genes, half of them are loss-of-function variants. Both of NOTCH1 and its ligand JAG1 harbor two additional de novo mutations (two stop gains in NOTCH1 and one missense and a splice donor in JAG1). The analysis showed a strongly significant over-representation of de novo loss-of-function variants in NOTCH1 (P=3.8 ×10-9). To assess alternative family-based study design in CHD, I combined the analysis from 13 isolated parent-offspring trios with 112 unrelated index cases of isolated atrioventricular septal defects (AVSD) in chapter 4. Initially, I started with a case/control analysis to test the burden of rare missense variants in cases compared with 5,194 ethnically matching controls and identified the gene NR2F2 (Fisher exact test P=7.7×10-07, odds ratio=54). The de novo analysis in the AVSD trios identified two de novo missense variants in the same gene. NR2F2 encodes a pleiotropic developmental transcription factor, and decreased dosage of NR2F2 in mice has been shown to result in abnormal development of atrioventricular septa. The results from luciferase assays show that all coding sequence variants observed in patients significantly alter the activity of NR2F2 target promoters. My work has identified both known and novel CHD genes enriched for rare coding variants using next-generation sequencing data. I was able to show how using single or combined family-based study designs is an effective approach to study the genetic causes of isolated CHD subtypes. Despite the extreme heterogeneity of CHD, combining NGS data with the proper study design has proved to be an effective approach to identify novel and known CHD genes. Future studies with considerably larger sample sizes are required to yield deeper insights into the genetic causes of isolated CHD.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:590265 |
| Date | January 2014 |
| Creators | Al Turki, Saeed |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/245178 |
Page generated in 0.0017 seconds