Genetic influences on human blood metabolites in the Japanese population

Genetic mapping of serum metabolome to chronic diseases among Han Chinese

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.

Understanding the Genetic Architecture of Vitamin Status Biomarkers in the Genome-Wide Association Study Era: Biological Insights and Clinical Significance

Vitamins play an intrinsic role in human health and are targets for clinical intervention through dietary or pharmacological approaches. Biomarkers of vitamin status are complex traits, measurable phenotypes that arise from an interplay between dietary and other environmental factors with a genetic component that is polygenic, meaning many genes are plausibly involved. Studying these genetic influences will improve our knowledge of fundamental vitamin biochemistry, refine estimates of the effects of vitamins on human health, and may in future prove clinically actionable. Here, we evaluate genetic studies of circulating and excreted biomarkers of vitamin status in the era of hypothesis-free genome-wide association studies (GWAS) that have provided unprecedented insights into the genetic architecture of these traits. We found that the most comprehensive and well-powered GWAS currently available were for circulating status biomarkers of vitamin A, C, D, and a subset of the B vitamins (B9 and B12). The biology implicated by GWAS of measured biomarkers of each vitamin is then discussed, both in terms of key genes and higher-order processes. Across all major vitamins, there were genetic signals revealed by GWAS that could be directly linked with known vitamin biochemistry. We also outline how genetic variants associated with vitamin status biomarkers have been already extensively used to estimate causal effects of vitamins on human health outcomes, which is particularly important given the large number of randomized control trials of vitamin related interventions with null findings. Finally, we discuss the current evidence for the clinical applicability of findings from vitamin GWAS, along with future directions for the field to maximize the utility of these data.

Protocol for genome-wide association study of human blood metabolites

Genetic variations influence the levels of blood metabolites. We present analytical pipelines for assessing genetic influences on human blood metabolites. We describe steps for the normalization of metabolome data, genome-wide association studies, and the identification of metabolite quantitative trait loci (mQTLs). We then detail procedures for functional enrichment analysis of mQTLs. This protocol could be applicable to other quantitative traits, such as clinical measurements or proteome data. For complete details on the use and execution of this protocol, please refer to Iwasaki et al.1.

Gene-metabolite annotation with shortest reactional distance enhances metabolite genome-wide association studies results

Metabolite genome-wide association studies (mGWAS) have advanced our understanding of the genetic control of metabolite levels. However, interpreting these associations remains challenging due to a lack of tools to annotate gene-metabolite pairs beyond the use of conservative statistical significance threshold. Here, we introduce the shortest reactional distance (SRD) metric, drawing from the comprehensive KEGG database, to enhance the biological interpretation of mGWAS results. We applied this approach to three independent mGWAS, including a case study on sickle cell disease patients. Our analysis reveals an enrichment of small SRD values in reported mGWAS pairs, with SRD values significantly correlating with mGWAS p values, even beyond the standard conservative thresholds. We demonstrate the utility of SRD annotation in identifying potential false negatives and inaccuracies within current metabolic pathway databases. Our findings highlight the SRD metric as an objective, quantitative and easy-to-compute annotation for gene-metabolite pairs, suitable to integrate statistical evidence to biological networks.