Mexico is developing the basis for genomic medicine to improve healthcare of its population. The extensive study of genetic diversity and linkage disequilibrium structure of different populations has made it possible to develop tagging and imputation strategies to comprehensively analyze common genetic variation in association studies of complex diseases. We assessed the benefit of a Mexican haplotype map to improve identification of genes related to common diseases in the Mexican population. We evaluated genetic diversity, linkage disequilibrium patterns, and extent of haplotype sharing using genomewide data from Mexican Mestizos from regions with different histories of admixture and particular population dynamics. Ancestry was evaluated by including 1 Mexican Amerindian group and data from the HapMap. Our results provide evidence of genetic differences between Mexican subpopulations that should be considered in the design and analysis of association studies of complex diseases. In addition, these results support the notion that a haplotype map of the Mexican Mestizo population can reduce the number of tag SNPs required to characterize common genetic variation in this population. This is one of the first genomewide genotyping efforts of a recently admixed population in Latin America.

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

TCF7L2 is the most potent locus for type 2 diabetes (T2D) risk and the first locus to have been robustly reported by genomic linkage studies. TCF7L2 is a transcription factor that forms a basic part of the Wnt signaling pathway. This gene has highly conserved sequence regions that correspond to functional domains. The association of TCF7L2 with T2D is one of the most powerful genetically discovered in studies of complex diseases, as it has been consistently replicated in multiple populations with diverse genetic origins. The mechanisms over which TCF7L2 exerts its effect on T2D are still not well understood. In this article, we describe the main molecular mechanisms of how TCF7L2 is related to T2D. TCF7L2 variants associated with T2D risk exert an influence on the initial therapeutic success of the hypoglycemic oral agent sulfonylurea. Thus, it is important to know whether there are other TCF7L2 variants associated with T2D that can influence treatment with oral hypoglycemic agents. Resequencing of the TCF7L2 gene in diverse ethnic groups is required to reveal common and rare variations and their role in different pathologies and in adverse reactions to drugs. Identification of TCF7L2-susceptibility disease variants will permit, at a given moment, offering of therapies to patients according to their genotype.