Integration of OMICS Data for Obesity

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.

Integration and Weighing of Omics Data for Obesity

“…In a prior study the eight highest scoring gene regions (ATP5O, ALK7, CR1, CR2, S100, GAPDH, TLR1 and TLR6) were discussed for their association to obesity. Since ATP5O, ALK7 and GADPH were among the previously discussed gene regions, the results of the second analysis prove to be consistent with our first study [32].…”

“…All of the remaining 7 identified gene regions (IFFO1, NCAPD2, DDX50, MAOB, ATM, STOX1 and PRRC2A) were also identified in our prior study [32], however, the gene regions were not discussed as they where not identified to be highest scoring regions but can be identified in the supplementary data.…”

“…We utilized the same data set we used for our prior study [32] which we generated through a search for various types of studies on Obesity (genome-wide association, meta-analysis, transcriptomic, proteomic studies, microRNA data and epigenetic studies) in online repositories, using GWAS Central (http://www.gwascentral.org) Medline database (www.ncbi.nlm.nih.gov/pubmed/) with search string (obesity) and (transcriptome OR proteome OR genome-wide OR microarray OR profiling OR epigenetics). Additionally, Gene Expression Omnibus (GEO) repository (http://www.ncbi.nlm.nih.gov/ geo/), ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) and Stanford Microarray Database (http://smd.stanford.edu) were searched up to find more suitable sources of data for inclusion in our data set.…”

“…Studies conducted in animals, children or elderly, and studies missing information of gender, age, study design and ethnicity were excluded from the data set. The data search was started from the Jan 01, 2000 to Jan 01, 2014 [32].…”

“…We already utilized an approach for integration of such multiorigin data based on positions of genetic alterations occurring in obesity [32,33]. For this second study we utilized the same data set but weighed each individual data source double and combined them with all the other data sources weighed single, which means we conducted 5 different analysis (genomic, transcriptomic, proteomic, RNA data, epigenetic) with the integration tool trying to verify our already found regions and/or identify new gene regions of interest.…”