Genome- and Phenome-Wide Analyses of Cardiac Conduction Identifies Markers of Arrhythmia Risk

Study Questions:

Can integration of genomic data with electronic medical records (EMRs) be used to validate genotype-phenotype associations and to better predict disease susceptibility?


A genome-wide association study (GWAS) was performed to identify genomic markers of QRS duration in 5,272 individuals without cardiac disease selected from EMR algorithms at five sites in the Electronic Medical Records and Genomics (eMERGE) network. The most significant loci were evaluated within the Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium QRS GWAS meta-analysis.


Twenty-three single-nucleotide polymorphisms (SNPs) in five loci, previously described by CHARGE, were replicated in the eMERGE samples; 18 SNPs were in the c’some 3 SCN5A and SCN10A loci, where the most significant SNPs were rs1805126 in SCN5A with p = 1.2 × 10−8 (eMERGE) and p = 2.5 × 10−20 (CHARGE), and rs6795970 in SCN10A with p = 6 × 10−6 (eMERGE) and p = 5 × 10−27 (CHARGE). The other loci were in NFIA, near CDKN1A, and near C6orf204. Phenome-wide association studies (PheWAS) on variants in these five loci in 13,859 subjects were performed to search for diagnoses associated with these markers. PheWAS identified atrial fibrillation and cardiac arrhythmias as the most common associated diagnoses with SCN10A and SCN5A variants. SCN10A variants were also associated with subsequent development of atrial fibrillation and arrhythmia in the original 5,272 “heart-healthy” study population.


The authors concluded that DNA biobanks coupled to EMRs not only provide a platform for GWAS, but also may allow broad interrogation of the longitudinal incidence of disease associated with genetic variants. The PheWAS approach implicated sodium channel variants modulating QRS duration in subjects without cardiac disease as predictors of subsequent arrhythmias.


DNA databases linked to EMRs may provide a powerful, unbiased approach to link genotypes with phenotypes. This approach appears valid, as previous SNP findings related to QRS duration were replicated in this study. Furthermore, SCN10A SNPs were associated with development of future cardiac arrhythmias. This study highlights the potential of genomic/EMR-based studies to identify loci responsible for common phenotypic variability and for prediction of disease susceptibility.

Keywords: Electronic Health Records, Polymorphism, Single Nucleotide, Phenotype, Genome-Wide Association Study

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