Integration and Weighing of Omics Data for Obesity

Objective: OMICs research has become of great interest over the last years and enabled the research community to acquisition an increasing amount of data. In a prior study by our group, we have employed a novel positional integrative approach. For this second study we utilized the same integration method but weighed each individual data source trying to verify our already found regions and/or identify new gene regions of interest for obesity. 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 and employed an additional weighing step. A data search for various types of studies on Obesity (genome-wide association, meta-analysis, transcriptomic, proteomic studies and epigenetic studies) was conducted to establish the initial data set. For this study, five different weighing settings were used, to individually double the input of genomic data, transcriptomic data, proteomic data, microRNA data and epigenetic data in comparison to all the other data sources. Results and discussion: The analysis identified ten gene regions (ATP5O, ALK7, GAPDH, IFFO1, NCAPD2, DDX50, MAOB, ATM, STOX1 and PRRC2A). ATP5O, ALK7 and GADPH were also identified in our prior study as the highest scoring gene regions and prove to be consistent in our data set. Even though, the ten high ranked and discussed genes could not be directly linked to obesity nine of them are associated with Type 2 Diabetes or a neurodegenerative disease. Both disorders show a higher prevalence in obese individuals compared to lean. Conclusion: In this study, we applied a new method of positional integrational analysis of different OMIC-layers and an additional validation step through weighing. our study provides a basis for further research to elucidate underlying mechanisms of these associations and identify new targets for preventive and therapeutic interventions. Integration and Weighing of Omics Data for Obesity Caroline Brettfeld1, Ales Maver2, Eva Aumuller1, Borut Peterlin2 and Alexander G Haslberger1* 1Department for Nutrition Sciences, University of Vienna, Austria 2Department of Human Genetics, University of Ljubljana, Austria *Corresponding author: Alexander G Haslberger, Department for Nutrition Sciences, University of Vienna, Austria, Tel: +43 6991 2211212; E-mail: [email protected] Received July 12, 2016; Accepted July 21, 2016; Published July 27, 2016 Citation: Brettfeld C, Maver A, Aumuller E, Peterlin B, Haslberger AG (2016) Integration and Weighing of Omics Data for Obesity. J Diabetes Metab 7: 690. doi: 10.4172/2155-6156.1000690 Copyright: © 2016 Brettfeld C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.