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Epidemiologic pattern and diseasome exploration for physical performance: A new horizon for genetic and environmental cross-talk in health and disease

2nd International Conference on Influenza

Mohammad Reza Hashempour, A R Khoshdel, K Majidzadeh and M S Baniaghil

Aja University of Medical Sciences, Iran Golestan University of Medical Sciences, Iran Shahid Beheshti University of Medical Sciences, Iran

Posters & Accepted Abstracts: J Infect Dis Ther

DOI: 10.4172/2332-0877.C1.015

Abstract
Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. However, considering the interaction between the factors, a network of association and clustering would explain the influencing factors on health and disease. In this study we evaluated association of factors on physical performance. From a comprehensive search of the MeSH annotation of MEDLINE articles, NIH Genetic Association Database (GAD) and OMIM database, genetic and environmental etiological factors associated with physical performance were identified. Clustering of both genetic and environmental etiological factors puts genes in the context of environment in a quantitative manner. After extraction of genetic factors, associated diseases with those genes were searched. Finally a matrix of association was formed. The degree of associations was determined by pooling the published data and the network of â??etiomeâ? was constructed by Gephi. A 22 by 22 genegene interaction showed ACE gene with the highest centrality. Also 600 cells gene-disease matrix were illustrated including the degree of associations and 95% CIs. The diseasome of physical performance demonstrated interesting clusters of diseases and risk factors with an average degree of 7.4 and average clustering coefficient of 0.60. The network principally included two main clusters around diabetes and neoplastic diseases, while diabetes had the highest strength and centrality. The diseasome helps a better understanding of genetic and environmental factors attributed to physical performance in order to find effective treatments for linked factors. Diabetes and ACE gene polymorphism should take a paramount attention in this regard.
Biography

Email: hashempourm@yahoo.com

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