A cross-platform approach identifies genetic regulators of human metabolism and health
12 February 2021
HDR UK’s Dr Claudia Langenberg and Professor John Danesh, alongside researchers spanning multiple countries, have recently harnessed genomic and small molecule data from multiple platforms to uncover regulators of human metabolism and their relevance for disease.
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Metabolites play a significant role in many complex human diseases and, by extension, may play an important role in prediction, diagnosis and treatment. Genome-wide association studies (GWAS) of metabolites provide important insights into genetic variant-metabolite associations and, in some cases, the causes of disease (metabolic or otherwise). Still, existing studies tend to be limited in design, size, analytical strategy and single disease focus.
The impact of scale
Combining international large-scale population-based studies, including some unpublished data, enabled researchers to maximise the sample size for this study – allowing for a thorough investigation of 174 metabolites using blood test results and genetic information from approximately 85,000 people. The work uncovered hundreds of new genetic variants that regulate metabolite levels in the blood, some with clinical implications. For instance, the data revealed a strong link between high serine levels and protection from rare eye disease macular telangiectasia type 2.
This international collaboration also represents the largest study of its kind to-date and a proof-of-concept for combining GWAS statistics across platforms and cohorts – even where different measurement techniques have been used.
See for yourself
The data and code underpinning the results been made available for others to use – including an interactive website providing rapid access to large-scale omic results and resources. The hope is to encourage researchers & clinicians worldwide to test the relevance of genes and pathways uncovered for their diseases of interest.
Abstract: In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 10−10) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at GLP2R (p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.