BACKGROUND: Electrocardiographic traits are important, substantially heritable determinants of risk of arrhythmias and sudden cardiac death. METHODS AND RESULTS: In this study, 3 population-based cohorts (n=10,526) genotyped with the Illumina HumanCVD Beadchip and 4 quantitative electrocardiographic traits (PR interval, QRS axis, QRS duration, and QTc interval) were evaluated for single-nucleotide polymorphism associations. Six gene regions contained single nucleotide polymorphisms associated with these traits at P<10(-6), including SCN5A (PR interval and QRS duration), CAV1-CAV2 locus (PR interval), CDKN1A (QRS duration), NOS1AP, KCNH2, and KCNQ1 (QTc interval). Expression quantitative trait loci analyses of top associated single-nucleotide polymorphisms were undertaken in human heart and aortic tissues. NOS1AP, SCN5A, IGFBP3, CYP2C9, and CAV1 showed evidence of differential allelic expression. We modeled the effects of ion channel activity on electrocardiographic parameters, estimating the change in gene expression that would account for our observed associations, thus relating epidemiological observations and expression quantitative trait loci data to a systems model of the ECG. CONCLUSIONS: These association results replicate and refine the mapping of previous genome-wide association study findings for electrocardiographic traits, while the expression analysis and modeling approaches offer supporting evidence for a functional role of some of these loci in cardiac excitation/conduction.
BACKGROUND: Electrocardiographic traits are important, substantially heritable determinants of risk of arrhythmias and sudden cardiac death. METHODS AND RESULTS: In this study, 3 population-based cohorts (n=10,526) genotyped with the Illumina HumanCVD Beadchip and 4 quantitative electrocardiographic traits (PR interval, QRS axis, QRS duration, and QTc interval) were evaluated for single-nucleotide polymorphism associations. Six gene regions contained single nucleotide polymorphisms associated with these traits at P<10(-6), including SCN5A (PR interval and QRS duration), CAV1-CAV2 locus (PR interval), CDKN1A (QRS duration), NOS1AP, KCNH2, and KCNQ1 (QTc interval). Expression quantitative trait loci analyses of top associated single-nucleotide polymorphisms were undertaken in human heart and aortic tissues. NOS1AP, SCN5A, IGFBP3, CYP2C9, and CAV1 showed evidence of differential allelic expression. We modeled the effects of ion channel activity on electrocardiographic parameters, estimating the change in gene expression that would account for our observed associations, thus relating epidemiological observations and expression quantitative trait loci data to a systems model of the ECG. CONCLUSIONS: These association results replicate and refine the mapping of previous genome-wide association study findings for electrocardiographic traits, while the expression analysis and modeling approaches offer supporting evidence for a functional role of some of these loci in cardiac excitation/conduction.
Authors: Tamara T Koopmann; Michiel E Adriaens; Perry D Moerland; Roos F Marsman; Margriet L Westerveld; Sean Lal; Taifang Zhang; Christine Q Simmons; Istvan Baczko; Cristobal dos Remedios; Nanette H Bishopric; Andras Varro; Alfred L George; Elisabeth M Lodder; Connie R Bezzina Journal: PLoS One Date: 2014-05-20 Impact factor: 3.240
Authors: Xiaoming Zhang; Jin-Young Yoon; Michael Morley; Jared M McLendon; Kranti A Mapuskar; Rebecca Gutmann; Haider Mehdi; Heather L Bloom; Samuel C Dudley; Patrick T Ellinor; Alaa A Shalaby; Raul Weiss; W H Wilson Tang; Christine S Moravec; Madhurmeet Singh; Anne L Taylor; Clyde W Yancy; Arthur M Feldman; Dennis M McNamara; Kaikobad Irani; Douglas R Spitz; Patrick Breheny; Kenneth B Margulies; Barry London; Ryan L Boudreau Journal: J Clin Invest Date: 2018-02-19 Impact factor: 14.808
Authors: Jie Zheng; Santiago Rodriguez; Charles Laurin; Denis Baird; Lea Trela-Larsen; Mesut A Erzurumluoglu; Yi Zheng; Jon White; Claudia Giambartolomei; Delilah Zabaneh; Richard Morris; Meena Kumari; Juan P Casas; Aroon D Hingorani; David M Evans; Tom R Gaunt; Ian N M Day Journal: Bioinformatics Date: 2016-09-01 Impact factor: 6.937