Open AccessA cross-platform approach identifies genetic regulators of human metabolism and health
Author(s) -
Luca A. Lotta,
Maik Pietzner,
Isobel D. Stewart,
Laura B. L. Wittemans,
Chen Li,
Roberto Bonelli,
Johannes Raffler,
Emma K. Biggs,
Clare OliverWilliams,
Victoria P.W. Auyeung,
Jian’an Luan,
Eleanor Wheeler,
Ellie Paige,
Praveen Surendran,
Gregory A. Michelotti,
Robert A. Scott,
Stephen Burgess,
Verena Zuber,
Eleanor Sanderson,
Albert Koulman,
Fumiaki Imamura,
Nita G. Forouhi,
KayTee Khaw,
Julian L. Griffin,
Angela Wood,
Gabi Kastenmüller,
John Danesh,
Adam S. Butterworth,
Fiona M. Gribble,
Frank Reimann,
Melanie Bahlo,
Eric B. Fauman,
Nicholas J. Wareham,
Claudia Langenberg
Publication year - 2021
Publication title -
nature genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.861
H-Index - 573
eISSN - 1546-1718
pISSN - 1061-4036
DOI - 10.1038/s41588-020-00751-5
Subject(s) - biology , nonsynonymous substitution , genetics , pleiotropy , computational biology , gene , genome , phenotype
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.