A Multi-Trait Approach Identified Genetic Variants Including a Rare Mutation in RGS3 with Impact on Abnormalities of Cardiac Structure/Function. View Full Text


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Article Info

DATE

2019-12

AUTHORS

Akram Yazdani, Azam Yazdani, Raúl Méndez Giráldez, David Aguilar, Luca Sartore

ABSTRACT

Heart failure is a major cause for premature death. Given the heterogeneity of the heart failure syndrome, identifying genetic determinants of cardiac function and structure may provide greater insights into heart failure. Despite progress in understanding the genetic basis of heart failure through genome wide association studies, the heritability of heart failure is not well understood. Gaining further insights into mechanisms that contribute to heart failure requires systematic approaches that go beyond single trait analysis. We integrated a Bayesian multi-trait approach and a Bayesian networks for the analysis of 10 correlated traits of cardiac structure and function measured across 3387 individuals with whole exome sequence data. While using single-trait based approaches did not find any significant genetic variant, applying the integrative Bayesian multi-trait approach, we identified 3 novel variants located in genes, RGS3, CHD3, and MRPL38 with significant impact on the cardiac traits such as left ventricular volume index, parasternal long axis interventricular septum thickness, and mean left ventricular wall thickness. Among these, the rare variant NC_000009.11:g.116346115C > A (rs144636307) in RGS3 showed pleiotropic effect on left ventricular mass index, left ventricular volume index and maximal left atrial anterior-posterior diameter while RGS3 can inhibit TGF-beta signaling associated with left ventricle dilation and systolic dysfunction. More... »

PAGES

5845

References to SciGraph publications

  • 2013-09. N-Acetylcysteine Effects on Transforming Growth Factor-β and Tumor Necrosis Factor-α Serum Levels as Pro-Fibrotic and Inflammatory Biomarkers in Patients Following ST-Segment Elevation Myocardial Infarction in DRUGS IN R&D
  • 2015-12. Rare variants analysis using penalization methods for whole genome sequence data in BMC BIOINFORMATICS
  • 2018-12. Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population in NATURE COMMUNICATIONS
  • 2018-05. Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review in JOURNAL OF INHERITED METABOLIC DISEASE
  • 2014-12. MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification in BMC BIOINFORMATICS
  • 2011-02. 9p21 DNA variants associated with coronary artery disease impair interferon-γ signalling response in NATURE
  • 2017-12. GWAS of the electrocardiographic QT interval in Hispanics/Latinos generalizes previously identified loci and identifies population-specific signals in SCIENTIFIC REPORTS
  • 2006-10. The max-min hill-climbing Bayesian network structure learning algorithm in MACHINE LEARNING
  • 2014-12. Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline in BMC BIOINFORMATICS
  • 2012-09. A mixed-model approach for genome-wide association studies of correlated traits in structured populations in NATURE GENETICS
  • 2016-12. The Ensembl Variant Effect Predictor in GENOME BIOLOGY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-019-41362-3

    DOI

    http://dx.doi.org/10.1038/s41598-019-41362-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1113355936

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30971721


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