Obesity is a complex chronic disease influenced by numerous genetic and environmental factors. It is a major risk factor for various chronic diseases, making an understanding of its molecular mechanisms essential. Despite extensive research, the genetic basis of obesity remains incompletely understood. Therefore, this master's thesis aimed to identify more robust candidate genes associated with human obesity through positional genomic integration of data from different omics levels and to assess their biological significance. Using predefined search strings, we reviewed the literature on genomics (DNA level), including common and rare variants, transcriptomics, epigenomics, and proteomics. A total of 23 studies met the inclusion criteria. Positional genomic integration of data from different omics levels identified three significant intervals containing the candidate genes ABCG1 and LGALS3BP, as well as 126 nominally significant intervals encompassing 115 distinct genes. Genomic distribution analysis revealed that most of these genes were located on chromosome 1, while biological pathway enrichment analysis indicated their involvement in inflammatory processes, immune responses, cell signaling, and metabolism. Integration of data from individual omics levels identified 2,527 significant intervals encompassing 263 distinct genes, most of which were located on chromosome X. The genes ABCG1 and LGALS3BP were each identified at two omics levels, further supporting their status as robust candidate genes for obesity. A protein–protein interaction (PPI) network was constructed and analyzed for the 263 genes, revealing significant enrichment of protein–protein interactions, key interaction hubs, and functional modules primarily associated with oxygen transport, complement activation, and regulation of lipid storage. The involvement of obesity candidate genes in interconnected biological processes further supports the complex, multifactorial nature of the disease. The proposed candidate genes provide a basis for further functional investigations and may contribute to a better understanding of the molecular mechanisms underlying obesity.
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