ArticleHGG advances2022
Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease.
Article in HGG advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Phenotypic spectrum and molecular characteristics of variants of uncertain significance in copy number variations associated with fetal cardiac malformations: a case series of 9 patients.Frontiers in pediatrics · 2026Article
- In Vivo Models of Cardiovascular Disease:Biomedicines · 2025Review
- Decoding congenital heart disease: a multi-omic framework for cardiac lineage and regulatory dysfunction.Frontiers in cell and developmental biology · 2025Review
- Variants of unknown significance in fetal heart malformations: distribution and impact on prenatal decision-making.Frontiers in pediatrics · 2025Article
- Molecular convergence of risk variants for congenital heart defects leveraging a regulatory map of the human fetal heart.medRxiv : the preprint server for health sciences · 2024Article
- Case-control association study of congenital heart disease from a tertiary paediatric cardiac centre from North India.BMC pediatrics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n
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Registered trials
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