Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration

Research output: Contribution to journalJournal articleResearchpeer-review

  • Xikun Han
  • Ines Lains
  • Jun Li
  • Jinglun Li
  • Yiheng Chen
  • Bing Yu
  • Qibin Qi
  • Eric Boerwinkle
  • Robert Kaplan
  • Bharat Thyagarajan
  • Martha Daviglus
  • Charlotte E Joslin
  • Jianwen Cai
  • Deirdre K Tobias
  • Eric Rimm
  • Alberto Ascherio
  • Karen Costenbader
  • Elizabeth Karlson
  • Lorelei Mucci
  • A Heather Eliassen
  • Oana Zeleznik
  • John Miller
  • Demetrios G Vavvas
  • Ivana K Kim
  • Rufino Silva
  • Joan Miller
  • Frank Hu
  • Walter Willett
  • Jessica Lasky-Su
  • Peter Kraft
  • J Brent Richards
  • Stuart MacGregor
  • Deeba Husain
  • Liming Liang

Age-related macular degeneration (AMD) is a leading cause of blindness in older adults. Investigating shared genetic components between metabolites and AMD can enhance our understanding of its pathogenesis. We conduct metabolite genome-wide association studies (mGWASs) using multi-ethnic genetic and metabolomic data from up to 28,000 participants. With bidirectional Mendelian randomization analysis involving 16,144 advanced AMD cases and 17,832 controls, we identify 108 putatively causal relationships between plasma metabolites and advanced AMD. These metabolites are enriched in glycerophospholipid metabolism, lysophospholipid, triradylcglycerol, and long chain polyunsaturated fatty acid pathways. Bayesian genetic colocalization analysis and a customized metabolome-wide association approach prioritize putative causal AMD-associated metabolites. We find limited evidence linking urine metabolites to AMD risk. Our study emphasizes the contribution of plasma metabolites, particularly lipid-related pathways and genes, to AMD risk and uncovers numerous putative causal associations between metabolites and AMD risk.

Original languageEnglish
Article number101085
JournalCell Reports Medicine
ISSN2666-3791
DOIs
Publication statusAccepted/In press - 2023
Externally publishedYes

Bibliographical note

Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.

ID: 358111737