Literature DB >> 25590050

Replication of SCN5A Associations with Electrocardio-graphic Traits in African Americans from Clinical and Epidemiologic Studies.

Janina M Jeff1, Kristin Brown-Gentry2, Robert Goodloe3, Marylyn D Ritchie4, Joshua C Denny5, Abel N Kho6, Loren L Armstrong7, Bob McClellan8, Ping Mayo9, Melissa Allen10, Hailing Jin11, Niloufar B Gillani, Nathalie Schnetz-Boutaud, Holli H Dilks, Melissa A Basford, Jennifer A Pacheco, Gail P Jarvik, Rex L Chisholm, Dan M Roden, M Geoffrey Hayes, Dana C Crawford.   

Abstract

The NAv1.5 sodium channel α subunit is the predominant α-subunit expressed in the heart and is associated with cardiac arrhythmias. We tested five previously identified SCN5A variants (rs7374138, rs7637849, rs7637849, rs7629265, and rs11129796) for an association with PR interval and QRS duration in two unique study populations: the Third National Health and Nutrition Examination Survey (NHANES III, n= 552) accessed by the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) and a combined dataset (n= 455) from two biobanks linked to electronic medical records from Vanderbilt University (BioVU) and Northwestern University (NUgene) as part of the electronic Medical Records & Genomics (eMERGE) network. A meta-analysis including all three study populations (n~4,000) suggests that eight SCN5A associations were significant for both QRS duration and PR interval (p<5.0E-3) with little evidence for heterogeneity across the study populations. These results suggest that published SCN5A associations replicate across different study designs in a meta-analysis and represent an important first step in utility of multiple study designs for genetic studies and the identification/characterization of genetic variants associated with ECG traits in African-descent populations.

Entities:  

Keywords:  African Americans; NHANES; eMERGE; electrocardiographic traits; electronic medical records; epidemiology; genetic association study

Year:  2014        PMID: 25590050      PMCID: PMC4290789          DOI: 10.1007/978-3-662-45523-4_76

Source DB:  PubMed          Journal:  Evol Comput Mach Learn Data Min Bioinform


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