Metabolomics, proteomics, and genomics analyses provide profound insight into human biology and disease pathophysiology. In this thesis, we explored the methodological challenges facing these OMICs technologies and illustrated their applications in epidemiological studies. In part one, we focused on some of the methodological challenges facing OMICs research. These challenges included handling missing data in metabolomics, measurement agreement between high throughput proteomic measurements with standard clinical measurements, and challenges in developing prediction models using metabolomic data. The second part of this thesis addressed various epidemiological research questions by utilizing genomic data and metabolomics measurements (Metabolon and Nightingale platforms) and using advanced data analysis methods. These studies provided important insights into the associations between metabolites and hepatic triglyceride content, the associations of between the size of cytosine-adenine-guanine nucleotide repeats in the huntingtin gene with metabolomic profile, and the associations of the man-made per- and polyfluoroalkyl substances (PFAS) with metabolite levels.