“Genomic Analyses Implicate Noncoding de Novo Variants in Congenital Heart Disease”, Felix Richter, Sarah U. Morton, Seong Won Kim, Alexander Kitaygorodsky, Lauren K. Wasson, Kathleen M. Chen, Jian Zhou, Hongjian Qi, Nihir Patel, Steven R. DePalma, Michael Parfenov, Jason Homsy, Joshua M. Gorham, Kathryn B. Manheimer, Matthew Velinder, Andrew Farrell, Gabor Marth, Eric E. Schadt, Jonathan R. Kaltman, Jane W. Newburger, Alessandro Giardini, Elizabeth Goldmuntz, Martina Brueckner, Richard Kim, George A. Porter, Daniel J. Bernstein, Wendy K. Chung, Deepak Srivastava, Martin Tristani-Firouzi, Olga G. Troyanskaya, Diane E. Dickel, Yufeng Shen, Jonathan G. Seidman, Christine E. Seidman, Bruce D. Gelb2020-06-29 (rare mutations, genome sequencing; similar)⁠:

A genetic etiology is identified for 1/3^rd of patients with congenital heart disease (CHD), with 8% of cases attributable to coding de novo variants (DNVs). This highlights the important role that genetics plays in CHD, underscoring the importance of understanding both coding and noncoding genetic variations to unravel the complexities of this condition.

To assess the contribution of noncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Through the use of advanced neural network predictions, we aimed to understand the transcriptional impact of these noncoding DNVs. Our methods incorporate cutting-edge technology to analyze genetic variations, enabling a comprehensive evaluation of their potential role in CHD.

Neural network prediction of noncoding DNV transcriptional impact identified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; p = 8.7 × 10⁻⁴). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, p = 1 × 10⁻⁵). A statistically-significant overlap was observed between these transcription-based approaches (odds ratio (OR)=2.5, 95% confidence interval (CI) 1.1–5.0, p = 5.4 × 10⁻³). These findings illuminate the large accumulation of noncoding DNVs in CHD cases, suggesting their pivotal roles in the pathology of the disease.

CHD DNVs altered transcription levels in 5⁄31 enhancers assayed. Furthermore, we observed a DNV burden in RNA-binding-protein regulatory sites (OR = 1.13, 95% CI 1.1–1.2, p = 8.8 × 10⁻⁵). Our research uncovers the intricate landscape of noncoding DNVs in CHD, elucidating their functional consequences and establishing a foundation for future studies to explore their pathological mechanisms.

Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least as high as that observed for damaging coding DNVs. This discovery not only advances our understanding of the genetic underpinnings of CHD but also opens new avenues for diagnosis, management, and potentially therapeutic interventions targeted towards noncoding genetic variations.

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