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Congenital heart defects genetic architecture in a small cohort: an integrated approach to prioritizing variants

Jul 2026 · Frontiers in Cardiovascular Medicine · Vol 13 · 0 citations · 32 references
Medicine

TL;DR

This study expands the understanding of the genetic landscape underlying congenital heart anomalies and emphasises the need for larger, deeply phenotyped cohorts to translate preliminary insights into clinically applicable predictors.

Abstract

Introduction Congenital heart defects (CHD) constitute a prevalent group of structural birth anomalies, characterised by substantial genetic heterogeneity and diverse clinical phenotypes. Methods To investigate the underlying genetic architecture, we performed whole-genome sequencing (WGS) in a cohort of 50 patients with echocardiographically confirmed CHD, followed by systematic variant identification and functional annotation. Results Our analysis reveals the limited discriminatory capacity of current genomic annotation databases and underscores the necessity of stratifying genetic risk assessments by specific CHD subtypes. By integrating clinical classifications, genomic data, and tissue-specific expression profiles, we identified novel coding and non-coding variants alongside putative regulatory signals that may contribute to CHD pathogenesis. Within cardiac-specific genes, we identified CHD subtype-specific genetic associations, including JARID2 with PDA, GOSR2/TBX18 with VSD, PCDHA9 with ASD, and a multi-gene signature (CREBBP, ZFPM2, SLC27A6, ADAM17, ETS1) with atrioventricular septal defects. Among coding variants in non-CHD-associated genes, we identified COL11A2 and PCOLCE2 as plausible collagen-related candidates for CHD pathogenesis. Discussion These findings reinforce the polygenic architecture of CHD and highlight the value of context-aware, phenotype-driven interpretation of genetic variants. Collectively, this study expands the understanding of the genetic landscape underlying congenital heart anomalies and emphasises the need for larger, deeply phenotyped cohorts to translate these preliminary insights into clinically applicable predictors.

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