A cross-platform approach identifies genetic regulators of human metabolism and health.
Luca A LottaMaik PietznerIsobel D StewartLaura B L WittemansChen LiRoberto BonelliJohannes RafflerEmma K BiggsClare Oliver-WilliamsVictoria P W Au YeungJian'an LuanEleanor WheelerEllie PaigePraveen SurendranGregory A MichelottiRobert A ScottStephen BurgessVerena ZuberEleanor C M SandersonAlbert KoulmanFumiaki ImamuraNita G ForouhiKay-Tee Khawnull nullJulian L GriffinAngela M WoodGabi KastenmüllerJohn DaneshAdam S ButterworthFiona M GribbleFrank ReimannMelanie BahloEric B FaumanNicholas J WarehamClaudia LangenbergPublished in: Nature genetics (2021)
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.
Keyphrases
- small molecule
- endothelial cells
- type diabetes
- body mass index
- optical coherence tomography
- high throughput
- diabetic retinopathy
- genome wide
- public health
- induced pluripotent stem cells
- blood glucose
- healthcare
- transcription factor
- insulin resistance
- cardiovascular disease
- copy number
- mental health
- ms ms
- protein protein
- skeletal muscle
- adipose tissue
- physical activity
- metabolic syndrome
- risk assessment
- glycemic control
- climate change
- deep learning