Untargeted Metabolome- and Transcriptome-Wide Association Study Suggests Causal Genes Modulating Metabolite Concentrations in Urine.

Gene products can affect the concentrations of small molecules (aka "metabolites"), and conversely, some metabolites can modulate the concentrations of gene transcripts. While many specific instances of this interplay have been revealed, a global approach to systematically uncover human gene-metabolite interactions is still lacking. We performed a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome using untargeted metabolome features, extracted from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples, and gene expression levels, quantified from RNA-Seq of lymphoblastoid cell lines (LCL) from 555 healthy individuals. We identified 20 study-wide significant associations corresponding to 15 genes, of which 5 associations (with 2 genes) were confirmed with follow-up NMR data. Using metabomatching, we identified the metabolites corresponding to metabolome features associated with the genes, namely, N-acetylated compounds with ALMS1 and trimethylamine (TMA) with HPS1. Finally, Mendelian randomization analysis supported a potential causal link between the expression of genes in both the ALMS1- and HPS1-loci and their associated metabolite concentrations. In the case of HPS1, we additionally observed that TMA concentration likely exhibits a reverse causal effect on HPS1 expression levels, indicating a negative feedback loop. Our study highlights how the integration of metabolomics, gene expression, and genetic data can pinpoint causal genes modulating metabolite concentrations.

Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression.

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.