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Complex Trait Mechanisms Informed by Metabolome-Mediated Gene Expression Analysis

For a paper appearing in eLife, researchers at the University of Lausanne and the Swiss Institute of Bioinformatics present a multi-omic Mendelian randomization (MR) approach for exploring complex trait-related gene expression patterns influenced by metabolites. After teasing out apparent ties between the transcriptome and metabolome in whole blood samples using expression quantitative trait locus and metabolite QTL data for thousands of individuals from prior studies, the team used transcript-metabolite and metabolite-trait relationships to tease out 216 "transcript-metabolite-trait causal triplets" with apparent ties to 26 complex phenotypes ranging from body traits or blood markers to cardiovascular features. "We show that … signals missed by transcriptome-wide MR are found, thanks to the increase in power conferred by integrating multiple omics layer," the authors explain, noting that "we developed a modular MR framework that has increased power over classical MR approaches to detect causal transcript-to-phenotype relationships when these are mediated by alteration of metabolite levels and is likely to become increasingly powerful upon release of larger [molecular QTL] datasets."