Sumstats Metabolomics

Serum metabolome GWAS in Han Chinese; portal lists browse/download for 2,854 serum metabolites in 3,795 individuals on the Westlake Chinese Multi-omics GWAS Catalog (see publication for cohort and analysis details).

Serum metabolites are potential regulators for chronic diseases. To explore the genetic regulation of metabolites and their roles in chronic diseases, we quantified 2,759 serum metabolites and performed genome-wide association studies (GWASs) among Han Chinese individuals. We identified 184 study-wide significant (p < 1.81 × 10-11) metabolite quantitative trait loci (metaboQTLs), 88.59% (163) of which were novel. Notably, we identified Asian-ancestry-specific metaboQTLs, including the SNP rs2296651 for taurocholic acid and taurochenodesoxycholic acid. Leveraging the GWAS for 37 clinical traits from East Asians, Mendelian randomization analyses identified 906 potential causal relationships between metabolites and clinical traits, including 27 for type 2 diabetes and 38 for coronary artery disease. Integrating genetic regulation of the transcriptome and proteome revealed putative regulators of several metabolites. In summary, we depict a landscape of the genetic architecture of the serum metabolome among Han Chinese and provide insights into the role of serum metabolites in chronic diseases.

This table was initially published in Kastenmüller et al., Genetics of human metabolism: an update. Hum. Mol. Genet. 2015 and has been updated as of 23 April 2024.

Genome-wide association studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic associations with clinical end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic associations can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.