Complex genome-wide association studies (GWAS) that integrate omics technolo?gies encompassing transcriptomics, pro?teomics, metabolomics and epigenomics datasets will permit better understanding of the interplay between genetic predispo?sitions and environmental factors in the development and manifestation of complex chronic diseases. These data sets will help guide diagnostic, preventative and thera?peutic approaches in the next few years. Although GWAS have uncovered many risk loci to date, the small effect sizes and lack of information on the underlying biological processes can often provide obstacles in correlating the data with com?plex disease. One solution proposed in a recent study by Suhre et al. was to associate GWAS data with metabolic traits as func?tional intermediates [1], namely genetically determined metabotypes (GDMs) [2,3]. This approach has the potential to better guide personalized therapy.
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