Purpose:
Large-scale genome-wide association studies (GWAS) have reported
important single nucleotide polymorphisms (SNPs) with significant
associations with age-related macular degeneration (AMD). However, their
role in disease development remains elusive. This study aimed to assess
SNP–metabolite associations (i.e., metabolite quantitative trait loci
[met-QTL]) and to provide insights into the biological mechanisms of AMD
risk SNPs.
Design:
Cross-sectional multicenter study (Boston, Massachusetts, and
Coimbra, Portugal).
Participants:
Patients with AMD (n = 388) and control participants (n = 98) without
any vitreoretinal disease (> 50 years).
Methods:
Age-related macular degeneration grading was performed using color
fundus photographs according to the Age-Related Eye Disease Study
classification scheme. Fasting blood samples were collected and evaluated
with mass spectrometry for metabolomic profiling and Illumina OmniExpress
for SNPs profiling. Analyses of met-QTL of endogenous metabolites were
conducted using linear regression models adjusted for age, gender, smoking,
10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we
analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by
generating genetic risk scores and assessing their associations with
metabolites using linear regression models, accounting for the same
covariates. Modeling was performed first for each cohort, and then combined
by meta-analysis. Multiple comparisons were accounted for using the false
discovery rate (FDR).
Main Outcome Measures:
Plasma metabolite levels associated with AMD risk SNPs.
Results:
After quality control, data for 544 plasma metabolites were
included. Meta-analysis of data from all individuals (AMD patients and
control participants) identified 28 significant met-QTL (β =
0.016−0.083; FDR q-value < 1.14 ×
10
−2
), which corresponded to 5 metabolites and 2
genes:
ASPM
and
LIPC
. Polymorphisms in the
LIPC
gene were associated with phosphatidylethanolamine
metabolites, which are glycerophospholipids, and polymorphisms in the
ASPM
gene with branched-chain amino acids. Similar
results were observed when considering only patients with AMD. Genetic risk
score–metabolite associations further supported a global impact of
AMD risk SNPs on the plasma metabolome.
Conclusions:
This study demonstrated that genomic–metabolomic associations
can provide insights into the biological relevance of AMD risk SNPs. In
particular, our results support that the
LIPC
gene and the
glycerophospholipid metabolic pathway may play an important role in AMD,
thus offering new potential therapeutic targets for this disease.