Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease

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...

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Published inPLoS genetics Vol. 18; no. 6; p. e1010252
Main Authors Xie, Yuhan, Jiang, Wei, Dong, Weilai, Li, Hongyu, Jin, Sheng Chih, Brueckner, Martina, Zhao, Hongyu
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.06.2022
Public Library of Science (PLoS)
Subjects
Age
Animals
Biology and Life Sciences
Cardiovascular disease
Case-Control Studies
Cohort Studies
Computer and Information Sciences
Congenital diseases
Coronary artery disease
Data analysis
Exome
Exome Sequencing
Genes
Genetic screening
Heart
Heart Defects, Congenital - genetics
Heart diseases
Humans
Medicine and Health Sciences
Mice
Parameter estimation
Power
Protein interaction
Proteins
Research and Analysis Methods
Statistics
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Summary: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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The authors declare that they have no competing interests.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1010252