Identifying the Genetic Causes of Congenital Heart Disease

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Embargoed until 2020-03-01
Copyright: Szot, Justin Oliver
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Abstract
Congenital heart diseases (CHDs), structural abnormalities of the heart that arises during embryonic development, are the most common inborn malformations, affecting up to 1% of the population. Causes for CHD have been attributed to a combination of environmental teratogens and genetic mutations, but cumulatively only 20% of cases can be explained. To date, over 60 genes in human have been implicated in CHD, but considering mutations in over 500 genes in mouse result in CHD, many more are expected to be found in humans. In this respect, this study sought to identify disease-causal gene mutations in families with sporadic or inherited CHD, and to identify potential CHD genes for future in vivo study. 30 Australian families with heterogenous non-syndromic CHDs, 16 familial and 14 sporadic cases, were analysed by Exome sequencing and their variants prioritised for disease-causal candidacy through a novel two-tiered approach: (1) CHD Gene screen, filtering for variants in genes with previous human CHD association; (2) Comprehensive Screen, an unbiased filtering of inherited rare, predicted-damaging, variants according to all possible Mendelian inheritance patterns. This approach identified pathogenic and likely pathogenic variants in 7 of 30 cases when limited to genes with known CHD association, increasing to 16 of 30 by comprehensive analysis. Two novel candidate variants, a KAT6A missense mutation predicted borderline benign by in silico tools, and a PBX1 missense mutation predicted pathogenic, were investigated functionally in vitro, vindicating their predicted disease-causal status. The two-tiered variant filtering approach was applied to an additional three families, of whom genome sequence data was available, successfully identifying pathogenic mutations in each case. In summary, 11 new genes without previous association to human CHD; PBX1, FLT4, TEK, TIE1, TEAD2, ZFP36L2, KDM5A, KMT2C, CNOT1, UPF2, and USP34, could be linked to the cardiac defects of the respective families. Hence, this study highlights that unbiased comprehensive analysis of exome and genome data can substantially increase the success rate of identifying likely disease-causal variants compared to a purely targeted approach. These findings are not only applicable for genetic counselling of affected families, but have potentially uncovered novel signalling pathways necessary for proper cardiac development.
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Author(s)
Szot, Justin Oliver
Supervisor(s)
Dunwoodie, Sally
Chapman, Gavin
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Publication Year
2017
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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