rs10076436 - HAND1 - CIR1P1

Magnitude 2.2 · 6 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

• Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease - Unknown journal (n.d.) · Unknown authors · PubMed 36918541

  ABSTRACT: The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanni

• 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

• Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits - Unknown journal (n.d.) · Unknown authors · PubMed 30679814

  ABSTRACT: Genome-wide association studies (GWAS) of quantitative electrocardiographic (ECG) traits in large consortia have identified more than 130 loci associated with QT interval, QRS duration, PR interval, and heart rate (RR interval). In the current study, we meta-analyzed genome-wide association results from 30,000 mostly Dutch samples on four ECG traits: PR interval, QRS duration, QT interval, and RR interval. SNP genotype data was imputed using the Genome of the Netherlands reference panel encompassing 19 million SNPs, including millions of rare SNPs (minor allele frequency < 5%). In addition to many known loci, we identified seven novel locus-trait associations: KCND3, NR3C1, and PLN for PR interval, KCNE1, SGIP1, and NFKB1 for QT interval, and ATP2A2 for QRS duration, of which

• Genome‐Wide Associations of Global Electrical Heterogeneity ECG Phenotype: The ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study) - Unknown journal (n.d.) · Unknown authors · PubMed 29622589

  ABSTRACT: Background ECG global electrical heterogeneity (GEH) is associated with sudden cardiac death. We hypothesized that a genome‐wide association study would identify genetic loci related to GEH. Methods and Results We tested genotyped and imputed variants in black (N=3057) and white (N=10 769) participants in the ARIC (Atherosclerosis Risk in Communities) study and CHS (Cardiovascular Health Study). GEH (QRS‐T angle, sum absolute QRST integral, spatial ventricular gradient magnitude, elevation, azimuth) was measured on 12‐lead ECGs. Linear regression models were constructed with each GEH variable as an outcome, adjusted for age, sex, height, body mass index, study site, and principal components to account for ancestry. GWAS identified 10 loci that showed genome‐wide signific

• Genetic and functional insights into the fractal structure of the heart - Unknown journal (n.d.) · Unknown authors · PubMed 32814899

  ABSTRACT: The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be avestige of embryonic development. The function of these trabeculae in adults and their genetic architecture are unknown. To investigate this, we performed a genome-wide association study using fractal analysis of trabecular morphology as an image-derived phenotype in 18,096 UK Biobank participants. We identified 16 significant loci containing genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborisation. Using biomechanical simulations and human observational data, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian ra

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