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      Pathway analysis with genome-wide association study (GWAS) data detected the association of atrial fibrillation with the mTOR signaling pathway

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          Abstract

          Background Genome-wide association studies (GWAS) have identified numerous loci associated with diseases and traits. However, the elucidation of disease mechanisms followed by drug development has remained a challenge owing to complex gene interactions. We performed pathway analysis with MAGENTA (Meta-Analysis Geneset Enrichment of variaNT Associations) to clarify the pathways in genetic background of AF. Methods The existing GWAS data were analyzed using MAGENTA. A microarray analysis was then performed for the identified pathways with human atrial tissues, followed by Gene-Set Enrichment Analysis (GSEA). Results MAGENTA identified two novel candidate pathways for AF pathogenesis, the CTCF (CCCTC-binding factor, p = 1.00 × 10−4, FDR q = 1.64 × 10−2) and mTOR pathways (mammalian target of rapamycin, p = 3.00 × 10−4, FDR q = 3.13 × 10−2). The microarray analysis with human atrial tissue using the GSEA indicated that the mTOR pathway was suppressed in AF cases compared with non-AF cases, validating the MAGENTA results, but not CTCF pathway. Conclusions MAGENTA identified a novel pathway, mTOR, followed by GSEA with human atrial tissue samples. mTOR pathway is a key interface that adapts the change of environments by pressure overload and metabolic perturbation. Our results indicate that the MTOR pathway is involved in the pathogenesis of AF, although the details of the basic mechanism remain unknown and further analysis for causal-relationship of mTOR pathway to AF is required. CTCF pathway is essential for construction of chromatin structure and transcriptional process. The gene-set components of CTCF overlap with those of mTOR in Biocarta.

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          Most cited references11

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          Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

          Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.
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            Prevalence, age distribution, and gender of patients with atrial fibrillation. Analysis and implications.

            The prevalence of atrial fibrillation (AF) is related to age. Anticoagulation is highly effective in preventing stroke in patients with AF, but the risk of hemorrhage may be increased in older patients. We reviewed the available epidemiologic data to define the age and sex distribution of people with AF. From four large recent population-based surveys, we estimated the overall age- and gender-specific prevalence of AF. These estimates were applied to the recent US census data to calculate the number of men and women with AF in each age group. There are an estimated 2.2 million people in the United States with AF, with a median age of about 75 years. The prevalence of AF is 2.3% in people older than 40 years and 5.9% in those older than 65 years. Approximately 70% of individuals with AF are between 65 and 85 years of age. The absolute number of men and women with AF is about equal. After age 75 years, about 60% of the people with AF are women. In contrast to people with AF in the general population, patients with AF in recent anticoagulation trials had a mean age of 69 years, and only 20% were older than 75 years. The risks and benefits of antithrombotic therapy in older individuals are important considerations in stroke prevention in AF.
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              Epidemiologic features of chronic atrial fibrillation: the Framingham study.

              In the Framingham Study 2325 men and 2866 women 30 to 62 years old at entry were followed biennially over 22 years for the development of chronic atrial fibrillation in relation to antecedent cardiovascular disease and risk factors. During surveillance, atrial fibrillation developed in 49 men and 49 women. The incidence rose sharply with age but did not differ significantly between the sexes. Overall, there was a 2.0 per cent chance that the disorder would develop in two decades. Atrial fibrillation usually followed the development of overt cardiovascular disease. Only 18 men and 12 women (31 per cent) had chronic atrial fibrillation in the absence of cardiovascular disease. Cardiac failure and rheumatic heart disease were the most powerful predictive precursors, with relative risks in excess of sixfold. Hypertensive cardiovascular disease was the most common antecedent disease, largely because of its frequency in the general population. Among the risk factors for cardiovascular disease, diabetes and electrocardiographic evidence of left ventricular hypertrophy were related to the occurrence of atrial fibrillation. The development of chronic atrial fibrillation was associated with a doubling of overall mortality and of mortality from cardiovascular disease.
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                Author and article information

                Journal
                IJC Heart & Vasculature
                IJC Heart & Vasculature
                Elsevier BV
                23529067
                September 2019
                September 2019
                : 24
                : 100383
                Article
                10.1016/j.ijcha.2019.100383
                22a8aca2-6f45-4e50-979c-45a684ac76e6
                © 2019

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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