Human metabolic individuality in biomedical and pharmaceutical research

By Karsten Suhre, So-Youn Shin, A. Petersen, R. Mohney, D. Meredith, Brigitte Wägele, E. Altmaier, P. Deloukas, J. Erdmann, E. Grundberg and 23 more. Nature, 2011

1,041 citations recorded by the source corpus.

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Subfields: Epidemiology, Genetics, Physiology

View Paper: https://www.semanticscholar.org/paper/6887133be2e2eb0a44056d642930308d29fb418a

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Citations in the corpus

An atlas of genetic influences on human blood metabolites (2014, neutral): “Associations p-values (–log10 scale) are plotted for a 1 Mb window surrounding the lead SNP rs6693388, and for associations of the SNP with linoleate (18:2n6)/ 5,8-tetradecadienoate (red), and with THEM4 expression in fat (black), skin (blue) and LCL (green) respectively.”
Systems genetics approaches to understand complex traits (2013, background): “The levels of these metabolites could be mapped to specific loci, and some of these loci co-mapped with GWAS-identified loci for diseases 46–48 .”
Innovation: Metabolomics: the apogee of the omics trilogy (2012, background): “Additionally, contemporary metabolic profiling experiments have the advantage of being complemented by genomic sequencing and proteomic screenin”
HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease (2015, background): “Note that the SNP has been correlated with MOBP expression in two brain tissues (29), MPRL15 expression in blood (39) and serum ratio of allantoin to quinate (40); all three of these studies were curated by GRASP and found by cross-referencing this SNP to its database (35,36) (Figure 3).”
Plasma Proteome Profiling to Assess Human Health and Disease. (2016, result): “This compares favorably with metabolomics studies, which are already performed at large scale in plasma cohorts (Suhre et al., 2011), while providing equally useful and complementary information.”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “Several systems genetics studies of metabolites in human plasma or serum have been reported (Gieger et al., 2008; Kettunen et al., 2012; Shin et al., 2014; Suhre et al., 2011; Yu et al., 2013).”
Connecting genetic risk to disease end points through the human blood plasma proteome (2016, positive): “We investigate samples from 1,000 individuals of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study 1,11 and replicate the results in 338 participants of the Qatar Metabolomics Study on Diabetes (QMDiab) with participants of Arab and Asian ethnicities 12 .”
Histone Methylation Dynamics and Gene Regulation Occur through the Sensing of One-Carbon Metabolism (2015, background): “In the future, the results from these models could further be integrated with genomics data to specifically define the genetic contributions, as have been identified to associate with serum metabolite levels (Suhre et al., 2011).”