Congenital heart disease (CHD), the most prevalent birth defect, has high morbidity and mortality despite laudable surgical and medical advances. Elucidating the etiology could improve prevention, diagnostics, and management. However, only one third of CHD has an identifiable cause, and new approaches are needed to discover the remaining etiology. CHD is sporadic, under strong selective pressure, and primarily genetic, pointing to rare and de novo variant etiology. Correspondingly, 8% and 1% of CHD is attributable to protein-coding de novo and rare inherited mutations. Only 1% of the genome is protein-coding, and in other diseases the vast majority of genetic risk is noncoding. Therefore, for the work described in this thesis I tested the hypothesis that noncoding de novo and rare variants contribute to CHD. This is challenging to investigate owing to the paucity of analytical strategies, so I developed and applied new methodological frameworks. These leveraged the gamut of statistics from Fisher’s exact tests to neural network machine learning as well as an array of phenotypic read-outs, ranging from predicted transcription factor binding site disruption to observed congenital heart tissue RNAseq expression. With these approaches, I developed software that builds a foundation for investigating the rare variant contribution to CHD and reliably demonstrated a noncoding de novo variant contribution to CHD at least as high as the contribution of coding mutations.
|Advisor:||Gelb, Bruce D., Schadt, Eric E.|
|Commitee:||Buxbaum, Joseph, Garg, Vidu, Houten, Sander, Ma’ayan, Avi|
|School:||Icahn School of Medicine at Mount Sinai|
|Department:||Genetics and Genomic Sciences|
|School Location:||United States -- New York|
|Source:||DAI-B 80/09(E), Dissertation Abstracts International|
|Subjects:||Genetics, Medicine, Bioinformatics|
|Keywords:||Congenital heart disease, De novo variant, Noncoding, Rnaseq, Whole genome sequencing|
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