Integration of OMICS Data for Obesity
Caroline Brettfeld1, Ales Maver1, Eva Aumüller1, Borut Peterlin1, Alexander G. Haslberger2*
Affiliation
- 1Department of Human Genetics, University of Ljubljana
- 2Department for Nutrition Sciences, University of Vienna
Corresponding Author
Alexander G. Haslberger, University Doz. Min.R, Department of Nutritional Sciences, University of Vienna, Althanstrasse 14; UZA II, 1090 Vienna, Austria ; Tel: +43 6991 2211212; E-mail: alexander.haslberger@univie.ac.at
Citation
Haslberger, G.H., et al. Integration of OMICS Data for Obesity. (2015) J Diab Obes 2(2): 67- 75.
Copy rights
© 2015 Haslberger, G.H. This is an Open access article distributed under the terms of Creative Commons Attribution 4.0 International License.
Keywords
Abstract
Introduction: Obesity is a multifactorial condition that results from the interactions among genetic, dietary, environmental, and lifestyle factors. In our study, we have employed a novel integrative approach to identify mechanisms involved in human disease.
Method: In contrast to previous methodologies employed for integration of heterogeneous OMIC data, we based the integration on genomic positions of alterations in human disease. A data search for various types of studies on obesity (genome-wide association, meta-analysis, transcriptomic, proteomic studies and epigenetic studies) was conducted in literature sources and OMIC data repositories, using GWAS Central and Medline database with search string (obesity) AND (transcriptome OR proteome OR genome-wide OR microarray OR profiling OR epigenetics). Additionally, Gene Expression Omnibus (GEO) repository, Array Express and Stanford Microarray Database were searched to discover suitable sources of data for inclusion in our initial dataset.
Results and Discussion: As a result of the employed high through put technology, 71 high scoring regions were identified. We identified 8 high scoring gene regions (ATP5O, ALK7, CR1, CR2, S100, GAPDH, TLR1 and TLR6) that have not yet been associated to obesity. Interestingly, all of these genes were identified by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes to be implicated in the energy metabolism and the immune response, which are known to be involved in obesity.
Conclusion: In our study, we have performed a novel integrative approach to identify candidate regions and genes involved in human disease. The results showed that none of the high scoring genes that were identified were yet associated with obesity per se, but that they were found to be implicated in the immune response or the energy metabolism. Further research will be needed to validate the found gene regions for obesity.
