June 5, 2020 Conference

  


TEST
Title
Genomic-Metabolomic Associations in Age-Related Macular Degeneration (AMD)

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Author(s)
Dr. Ines Lains (Presenter)
Shujian Zhu, Harvard School of Public Health
Wonil Chung, Harvard School of Public Health
Rachel S Kelly, Brigham and Women's Hospital
Dr. Archana Nigalye, Massachusetts Eye and Ear
Raviv Katz, Massachusetts Eye and Ear Infirmary
Dr. John Miller, Mass Eye and Ear Infirmary
Dr. Demetrios Vavvas, MEEI
Ivana K. Kim, MEEI
Dr. Joan Miller, Massachusetts Eye and Ear Infirmary
Jessica Lasky-Su, Brigham and Women's Hospital
Liming Liang, Harvard School of Public Health
Dr. Deeba Husain, Mass Eye and Ear
Abstract
Purpose: Age-related macular degeneration (AMD) is a multifactorial disease comprising environmental and genetic risk factors. Thirty-four loci with more than 7,000 single nucleotide polymorphisms (SNPs) have been linked with AMD risk, but the functional consequences of most of them remains to be established. The assessment of genetic-metabolite associations (i.e. metabolite quantitative trait loci, mQTL) can provide unique insights into causal mechanisms of AMD. This study aimed to analyze associations between established AMD risk SNPs and plasma metabolites (mQTL) in a cohort of AMD patients and controls. Methods: Prospective, cross-sectional, multicenter study (Boston, United States and Coimbra, Portugal). We included subjects with AMD and controls without any vitreoretinal disease (> 50 years old); AMD grading was performed according to the AREDS classification scheme. Fasting blood samples were collected and analyzed by ultra-performance liquid chromatography and high-resolution mass spectrometry for metabolomic profiling, and by an Illumina Omni express platform for SNPs profiling. Analyses of mQTL of endogenous metabolites were conducted using linear regression models adjusted for age, sex, smoking, 10 metabolites principal components (PCs) and 10 SNP PCs. These models were first performed for each cohort and then combined by meta-analysis. Results: We included 388 patients with AMD and 98 controls; after quality control, data on 544 plasma metabolites was considered. Meta-analysis identified 66 significant mQTL (p<10-5), correspondent to 9 metabolites and 7 genes. The most significant associations (false discovery rate < 0.05) were seen between SNPs in the LIPC gene and phosphatidylethanolamine metabolites, and SNPs in the ASPM gene and the branched-chain amino acids leucine, isoleucine and valine. Pathway analysis integrating all the metabolites and genes of interest mapped to the glycerophospholipid, as well as to the alanine, aspartate and glutamate metabolite pathways. No common mQTL were found between AMD cases and controls. Conclusion: To our knowledge, this is the first study on metabolomic-genomic associations in AMD. Our results suggest that AMD risk loci are associated with levels of specific lipid and amino acids plasma metabolites, furthering our understanding of their biological effect. This increases our understanding on the biological relevance of AMD-risk SNPs and offers new potential therapeutic targets, as we strive for precise treatment options for this blinding disease.
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