ArticlePLoS genetics2022
Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease.
Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- A machine learning classifier to identify and prioritise genes associated with murine cardiac development.PLoS genetics · 2026Article
- Whole-exome sequencing uncovers the genetic complexity of bicuspid aortic valve in families with early-onset complications.American journal of human genetics · 2024Article
- Statistical methods for assessing the effects of de novo variants on birth defects.Human genomics · 2024Review
- Whole Exome Sequencing Uncovers the Genetic Complexity of Bicuspid Aortic Valve in Families with Early Onset Complications.medRxiv : the preprint server for health sciences · 2024Article
- Rare genomic copy number variants implicate new candidate genes for bicuspid aortic valve.PloS one · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD.
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