rs1194743 - LNCAROD

Magnitude 2.0 · 5 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

• Genome-wide association and Mendelian randomization analysis provide insights into the shared genetic architecture between high-dimensional electrocardiographic features and ischemic heart disease. - Human genetics (2024) · Wang X, Qi M, Zhang H, Yang Y, Zhao H · PubMed 38180560

  Observational studies have revealed that ischemic heart disease (IHD) has a unique manifestation on electrocardiographic (ECG). However, the genetic relationships between IHD and ECG remain unclear. We took 12-lead ECG as phenotypes to conduct genome-wide association studies (GWAS) for 41,960 samples from UK-Biobank (UKB). By leveraging large-scale GWAS summary of ECG and IHD (downloaded from FinnGen database), we performed LD score regression (LDSC), Mendelian randomization (MR), and polygenic risk score (PRS) regression to explore genetic relationships between IHD and ECG. Finally, we constructed an XGBoost model to predict IHD by integrating PRS and ECG. The GWAS identified 114 independent SNPs significantly (P value < 5 × 10-8/800, where 800 denotes the number of ECG features)

• Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease - Nature communications (2023) · Young WJ, Haessler J, Benjamins JW, Repetto L, Yao J, Isaacs A, Harper AR, Ramirez J, Garnier S, van Duijvenboden S, Baldassari AR, Concas MP, Duong T, Foco L, Isaksen JL, Mei H, Noordam R, Nursyifa C, Richmond A, Santolalla ML, Sitlani CM, Soroush N, Thériault S, Trompet S, Aeschbacher S, Ahmadizar F, Alonso A, Brody JA, Campbell A, Correa A, Darbar D, De Luca A, Deleuze JF, Ellervik C, Fuchsberger C, Goel A, Grace C, Guo X, Hansen T, Heckbert SR, Jackson RD, Kors JA, Lima-Costa MF, Linneberg A, Macfarlane PW, Morrison AC, Navarro P, Porteous DJ, Pramstaller PP, Reiner AP, Risch L, Schotten U, Shen X, Sinagra G, Soliman EZ, Stoll M, Tarazona-Santos E, Tinker A, Trajanoska K, Villard E, Warren HR, Whitsel EA, Wiggins KL, Arking DE, Avery CL, Conen D, Girotto G, Grarup N, Hayward C, Jukema JW, Mook-Kanamori DO, Olesen MS, Padmanabhan S, Psaty BM, Pattaro C, Ribeiro ALP, Rotter JI, Stricker BH, van der Harst P, van Duijn CM, Verweij N, Wilson JG, Orini M, Charron P, Watkins H, Kooperberg C, Lin HJ, Wilson JF, Kanters JK, Sotoodehnia N, Mifsud B, Lambiase PD, Tereshchenko LG, Munroe PB · 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 - Nature communications (2023) · Radhakrishnan A, Friedman SF, Khurshid S, Ng K, Batra P, Lubitz SA, Philippakis AA, Uhler C · 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

• Multi-ethnic Genome-wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits - Circulation. Genomic and precision medicine (2021) · Baldassari AR, Sitlani CM, Highland HM, Arking DE, Buyske S, Darbar D, Gondalia R, Graff M, Guo X, Heckbert SR, Hindorff LA, Hodonsky CJ, Ida Chen YD, Kaplan RC, Peters U, Post W, Reiner AP, Rotter JI, Shohet RV, Seyerle AA, Sotoodehnia N, Tao R, Taylor KD, Wojcik GL, Yao J, Kenny EE, Lin HJ, Soliman EZ, Whitsel EA, North KE, Kooperberg C, Avery CL · 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

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