# Computational Prioritization of Coding and Non-Coding Variants in Congenital Heart Disease

> **NIH NIH R03** · BOSTON CHILDREN'S HOSPITAL · 2022 · $175,803

## Abstract

PROJECT SUMMARY:
Congenital heart disease (CHD) is the most common anomaly at birth, affecting 1% of infants. Damaging genic
variants contribute significantly to CHD risk but a likely genetic cause is identified in only 50% of patients. The
genetic basis for the remaining half of CHD is unknown. The Gabriella Miller Kids First (GMKF) and TOPMed
programs funded whole genome sequencing (WGS) to tests our hypothesis that variants undetected by whole
exome sequencing (WES) contribute to CHD. WGS from 1813 CHD trios (affected probands and parents)
provides a unique opportunity to define additional coding and noncoding variants that convey CHD risk.
First, coding variants in CHD sequencing data will be comprehensively analyzed. WGS allows for improved
detection of damaging coding variants that are not detected by WES, including structural variants and variants
outside WES capture regions. Therefore, in Aim 1, damaging structural, mosaic and single nucleotide variants
will be identified in WGS data. Novel CHD genes with a burden of damaging coding variants in CHD compared
to non-CHD cohorts will be identified. Second, integration of CHD cardiac tissue gene expression with WGS
data will to prioritize noncoding variants likely to impact developmental gene regulation. Aim 2a assesses the
potential contribution of rare noncoding variants adjacent to cardiac expression quantitative trait loci (eQTLs) to
CHD. In a parallel approach, Aim 2b will leverage 430 human cardiac developmental functional genomic
annotations including those ascertained from human induced pluripotent stem cells throughout differentiation
into cardiomyocytes. Human cardiac epigenetic landscape may be more successful in defining genetic
mechanisms of the dominant CHD that typifies human CHD, as mouse CHD is typically a recessive phenotype.
Available annotations include histone methylation and acetylation states, as well as chromatin accessibility
(ATACseq), chromosome conformation (Hi-C), and RNA expression. A neural net will be trained on CHD eQTL
variants to identify a subset of annotations that are able to separate eQTL from non-eQTL loci. Prioritized
functional annotations will be used to calculate a per-base regulatory score across the genome (EpiCard), and
score thresholds will be queried for a burden in the CHD cohort. Finally, Aim 3 addresses the role of common
genetic variants in CHD risk and phenotypic variance. Leveraging the power of the trio structure, common
variants over-transmitted to CHD probands will be identified. Over-transmitted loci will then be assessed for
association with CHD in a case-control study in a second CHD cohort. Functional modeling of prioritized
genes, variants and loci is essential; committed collaborators are already engaged in preliminary studies.
Together this proposal will employ innovative computational approaches to prioritize variants and loci
associated with CHD. These results will contribute towards the long-term objective of understanding th...

## Key facts

- **NIH application ID:** 10469306
- **Project number:** 5R03HL150412-02
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Sarah Uhler Morton
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $175,803
- **Award type:** 5
- **Project period:** 2021-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10469306

## Citation

> US National Institutes of Health, RePORTER application 10469306, Computational Prioritization of Coding and Non-Coding Variants in Congenital Heart Disease (5R03HL150412-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10469306. Licensed CC0.

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