Integrating genetics and metabolomics from multi-ethnic and multi-fluid data reveals putative mechanisms for age-related macular degeneration
Xikun Han, Inês Laíns, Jun Li, Jinglun Li, Yiheng Chen, Bing Yu, Qibin Qi, Eric Boerwinkle, Robert C. Kaplan, Bharat Thyagarajan, Martha L. Daviglus, Charlotte E. Joslin, Jianwen Cai, Marta Guasch‐Ferré, Deirdre K. Tobias, Eric B. Rimm, Alberto Ascherio, Karen H. Costenbader, Elizabeth W. Karlson, Lorelei A. Mucci, A. Heather Eliassen, Oana A. Zeleznik, John B. Miller, Demetrios G. Vavvas, Ivana K. Kim, Rufino Silva, Joan W. Miller, Frank B. Hu, Walter C. Willett, Jessica Lasky‐Su, Peter Kraft, J. Brent Richards, Stuart MacGregor, Deeba Husain, Liming Liang
Abstract
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.