GWAS meta-analysis for disease prediction reveals genetic basis of atrial fibrillation and paves the road for personalized medicine

Atrial fibrillation is an arrhythmia that presents with symptoms of palpitations and shortness of breath from irregular and rapid heartbeats due to abnormal electrical excitation of cardiac muscle cells. Its prevalence is increasing with ageing populations and its improvement has become an important medical and health economic issue, as it causes heart failure and stroke due to reduced cardiac function and thrombus formation.

Team leader Kaoru Ito and visiting researcher Kazuo Miyazawa of the RIKEN Center for Integrative Medical Sciences, Professor Issei Komuro, Project Assistant Professor Seitaro Nomura and Project Research Associate Masamichi Ito of the Graduate School of Medicine, the University of Tokyo, Professor Yoichiro Kamatani of the Graduate School of Frontier Sciences, the University of Tokyo, Program-Specific Associate Professor Shinya Oki of the Graduate School of Medicine, Kyoto University, and Koichi Ozaki, Director of the Medical Genome Center, the National Center for Geriatrics and Gerontology, conducted the world's largest cross-ethnic genome-wide association study (GWAS) that meta-analyzed the Japanese GWAS using the genome data of Biobank Japan and that of Europeans to identify new disease susceptibility loci. Further analysis using ChIP-seq data revealed that the transcription factor ERRg is involved in regulating the expression of genes associated with atrial fibrillation (AF). The group's research was published in the online edition of Nature Genetics.

They constructed a genetic risk score (PRS) from the results of the meta-analysis, which is useful for predicting disease. They found that the higher the PRS, the younger the age of onset of AF, that there was a strong association between cardioembolic stroke and PRS, and that there was a strong association between PRS in AF and death from stroke.

The study combines analyses of large-scale genomic data with gene expression and transcription factor data to identify a novel genetic basis for the development of atrial fibrillation. These findings are expected to lead to the development of biomarkers for disease pathogenesis and new therapeutic targets. Furthermore, PRS based on genomic data was shown to be useful not only predicting disease but also for various pathologies and prognoses. In the future, the application of the findings of the researchers' genome analysis to actual medical practice is anticipated to contribute to the realization of precision medicine based on individual pathological conditions.