ArticleNPJ systems biology and applications2018
A personalized, multiomics approach identifies genes involved in cardiac hypertrophy and heart failure.
Article in NPJ systems biology and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- Transcriptome remodeling of mouse hearts during postnatal cardiac maturation and under proteotoxic stress.Molecular biology reports · 2026Article
- A systems genetics approach to uncover mitochondrial drivers of heart failure reveals mitochondria-nuclear cross talk in genetically diverse mouse strains.bioRxiv : the preprint server for biology · 2025Article
- Emerging Technologies and Future Directions in Interorgan Crosstalk Cardiometabolic Research.Circulation research · 2025Review
- Notch2 Signaling Drives Cardiac Hypertrophy by Suppressing Purine Nucleotide Metabolism.Research (Washington, D.C.) · 2025Article
- Diesel exhaust particle extract elicits an oxPAPC-like transcriptomic profile in macrophages across multiple mouse strains.Environmental pollution (Barking, Essex : 1987) · 2024Article
- E3 ubiquitin ligase rififylin has yin and yang effects on rabbit cardiac transient outward potassium currents (IThe Journal of biological chemistry · 2024Article
- KDM3A knockdown regulates COMP, LOX, COL8A1 and ACOT1 genes in myocardial fibrosis.Bioinformation · 2024Article
- Article
- New strategies and therapies for the prevention of heart failure in high-risk patients.Clinical cardiology · 2022Review
- Multi-Omic Approaches to Identify Genetic Factors in Metabolic Syndrome.Comprehensive Physiology · 2021Article
- Integrative Analysis of Multi-Omics and Genetic Approaches-A New Level in Atherosclerotic Cardiovascular Risk Prediction.Biomolecules · 2021Review
- Stearoyl-CoA Desaturase (SCD) Induces Cardiac Dysfunction with Cardiac Lipid Overload and Angiotensin II AT1 Receptor Protein Up-Regulation.International journal of molecular sciences · 2021Article
- Multi-omics approaches for revealing the complexity of cardiovascular disease.Briefings in bioinformatics · 2021Review
- Cardioinformatics: the nexus of bioinformatics and precision cardiology.Briefings in bioinformatics · 2020Article
- Sex differences in human adipose tissue gene expression and genetic regulation involve adipogenesis.Genome research · 2020Article
- Article
- Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity.Nature biomedical engineering · 2020Article
- Multi omics analysis of fibrotic kidneys in two mouse models.Scientific data · 2019Article
- Relevance of Multi-Omics Studies in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2019Review
- The CaeLife · 2018Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
A traditional approach to investigate the genetic basis of complex diseases is to identify genes with a global change in expression between diseased and healthy individuals. However, population heterogeneity may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we investigate individual changes of gene expression when inducing hypertrophy and heart failure in 100 + strains of genetically distinct mice from the Hybrid Mouse Diversity Panel (HMDP). We find that genes whose expression fold-change correlates in a statistically significant way with the severity of the disease are either up or down-regulated across strains, and therefore missed by a traditional population-wide analysis of differential gene expression. Furthermore, those "fold-change" genes are enriched in human cardiac disease genes and form a dense co-regulated module strongly interacting with the cardiac hypertrophic signaling network in the human interactome. We validate our approach by showing that the knockdown of
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.