English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

More reliable methods are needed to uncover novel biomarkers associated with atrial fibrillation (AF). Our objective is to identify significant network modules and newly AF-associated genes by integrative genetic analysis approaches. The single nucleotide polymorphisms with nominal relevance significance from the AF-associated genome-wide association study (GWAS) data were converted into the GWAS discovery set using ProxyGeneLD, followed by merging with significant network modules constructed by weighted gene coexpression network analysis (WGCNA) from one expression profile data set, composed of left and right atrial appendages (LAA and RAA). In LAA, two distinct network modules were identified (blue:  p  = 0.0076; yellow:  p  = 0.023). Five AF-associated biomarkers were identified ( ERBB2 ,  HERC4 ,  MYH7 ,  MYPN , and  PBXIP1 ), combined with the GWAS test set. In RAA, three distinct network modules were identified and only one AF-associated gene  LOXL1  was determined. Using human LAA tissues by real-time quantitative polymerase chain reaction, the differentially expressive results of  ERBB2 ,  MYH7 , and  MYPN  were observed ( p  < 0.05). This study first demonstrated the feasibility of fusing GWAS with expression profile data by ProxyGeneLD and WGCNA to explore AF-associated genes. In particular, two newly identified genes  ERBB2  and  MYPN  via this approach contribute to further understanding the occurrence and development of AF, thereby offering preliminary data for subsequent studies.

frontiers in genetics

Identification of Atrial Fibrillation-Associated Genes ERBB2 and MYPN Using Genome-Wide Association and Transcriptome Expression Profile Data on Left–Right Atrial Appendages