| Literature DB >> 33414548 |
Luca A Lotta1, Maik Pietzner1, Isobel D Stewart1, Laura B L Wittemans1,2, Chen Li1, Roberto Bonelli3,4, Johannes Raffler5, Emma K Biggs6, Clare Oliver-Williams7,8, Victoria P W Auyeung1, Jian'an Luan1, Eleanor Wheeler1, Ellie Paige9, Praveen Surendran7,10,11,12, Gregory A Michelotti13, Robert A Scott1, Stephen Burgess14,15, Verena Zuber14,16, Eleanor Sanderson17, Albert Koulman1,5,18, Fumiaki Imamura1, Nita G Forouhi1, Kay-Tee Khaw15, Julian L Griffin19, Angela M Wood7,10,11,20,21, Gabi Kastenmüller5, John Danesh7,10,11,20,22,23, Adam S Butterworth7,10,11,20,22,23, Fiona M Gribble6, Frank Reimann6, Melanie Bahlo3,4, Eric Fauman24, Nicholas J Wareham1, Claudia Langenberg25,26,27.
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.Entities:
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Year: 2021 PMID： 33414548 PMCID： PMC7612925 DOI： 10.1038/s41588-020-00751-5
Source DB: PubMed Journal: Nat Genet ISSN： 1061-4036 Impact factor: 41.307