Expressive

rs10054375 - HAND1 - CIR1P1

Magnitude 2.0 · 4 studies on file

Reported associations

  • Genetic evidence for T-wave area from 12-lead electrocardiograms to monitor cardiovascular diseases in patients taking diabetes medications. - Human genetics (2024) · Qi M, Zhang H, Xiu X, He D, Cooper DN, Yang Y, Zhao H · PubMed 38507016

    Aims Many studies indicated use of diabetes medications can influence the electrocardiogram (ECG), which remains the simplest and fastest tool for assessing cardiac functions. However, few studies have explored the role of genetic factors in determining the relationship between the use of diabetes medications and ECG trace characteristics (ETC). Methods Genome-wide association studies (GWAS) were performed for 168 ETCs extracted from the 12-lead ECGs of 42,340 Europeans in the UK Biobank. The genetic correlations, causal relationships, and phenotypic relationships of these ETCs with medication usage, as well as the risk of cardiovascular diseases (CVDs), were estimated by linkage disequilibrium score regression (LDSC), Mendelian randomization (MR), and regression model, respectively. Resul

  • The Genetic Makeup of the Electrocardiogram - Unknown journal (n.d.) · Unknown authors · PubMed 32916098

    ABSTRACT: SUMMARY The electrocardiogram (ECG) is one of the most useful non-invasive diagnostic tests for a wide array of cardiac disorders. Traditional approaches to analyzing ECGs focus on individual segments. Here, we performed comprehensive deep phenotyping of 77,190 ECGs in the UK Biobank across the complete cycle of cardiac conduction, resulting in 500 spatial-temporal datapoints, across 10 million genetic variants. In addition to characterizing polygenic risk scores for the traditional ECG segments, we identified over 300 genetic loci that are statistically associated with the high-dimensional representation of the ECG. We established the genetic ECG signature for dilated cardiomyopathy, associated the BAG3, HSPB7/CLCNKA, PRKCA, TMEM43, and OBSCN loci with disease risk and confirmed

  • Multi-ethnic Genome-wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits - Unknown journal (n.d.) · Unknown authors · PubMed 32602732

    ABSTRACT: Background - We examined how expanding electrocardiographic (ECG) trait genome-wide association studies (GWAS) to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. Methods - We decomposed 10-second, 12-lead ECGs from 34,668 multiethnic participants (15% African American; 30% Hispanic/Latino) into six contiguous, physiologically-distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and two composite, conventional (PR interval and QT interval) interval-scale traits and conducted multivariable-adjusted, trait-specific univariate GWAS using 1000-G imputed SNPs. Evidence of shared genetic effects was evaluated by aggregating

  • Cross-modal autoencoder framework learns holistic representations of cardiovascular state - Unknown journal (n.d.) · Unknown authors · PubMed 37105979

    ABSTRACT: A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop

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