一种跨平台方法鉴定了人类代谢和健康的遗传调控因子。
A cross-platform approach identifies genetic regulators of human metabolism and health.
机构信息
MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.
The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
出版信息
Nat Genet. 2021 Jan;53(1):54-64. doi: 10.1038/s41588-020-00751-5. Epub 2021 Jan 7.
Abstract
In cross-platform analyses of 174 metabolites, we identify 499 associations (P < 4.9 × 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.
摘要
在对 174 种代谢物的跨平台分析中，我们鉴定出 499 个关联（P<4.9×10），其特征为表现型多效性、等位基因异质性、大而非线性的效应以及非同义变异的富集。我们在 GLP2R （p.Asp470Asn）中发现了一个信号，它与较高的瓜氨酸水平、体重指数、空腹葡萄糖依赖性胰岛素释放肽和 2 型糖尿病共享，其潜在机制是β-arrestin 信号。研究表明，较高的丝氨酸水平可降低黄斑毛细血管扩张症 2 型（一种罕见的退行性视网膜疾病）的发生几率（降低 95%）和预测其发病风险。跨平台整合基因组和小分子数据，能够发现人类代谢物的调控因子，并转化为临床见解。
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