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. 2023 Jan 27:12:e82546.
doi: 10.7554/eLife.82546.

Phenome-wide Mendelian randomisation analysis identifies causal factors for age-related macular degeneration

Thomas H Julian  1   2 , Johnathan Cooper-Knock  3 , Stuart MacGregor  4 , Hui Guo  5 , Tariq Aslam  2   6 , Eleanor Sanderson  7 , Graeme C M Black  1   8 , Panagiotis I Sergouniotis  1   2   8   9
Affiliations

Phenome-wide Mendelian randomisation analysis identifies causal factors for age-related macular degeneration

Thomas H Julian et al. Elife. .

Abstract

Background: Age-related macular degeneration (AMD) is a leading cause of blindness in the industrialised world and is projected to affect >280 million people worldwide by 2040. Aiming to identify causal factors and potential therapeutic targets for this common condition, we designed and undertook a phenome-wide Mendelian randomisation (MR) study.

Methods: We evaluated the effect of 4591 exposure traits on early AMD using univariable MR. Statistically significant results were explored further using: validation in an advanced AMD cohort; MR Bayesian model averaging (MR-BMA); and multivariable MR.

Results: Overall, 44 traits were found to be putatively causal for early AMD in univariable analysis. Serum proteins that were found to have significant relationships with AMD included S100-A5 (odds ratio [OR] = 1.07, p-value = 6.80E-06), cathepsin F (OR = 1.10, p-value = 7.16E-05), and serine palmitoyltransferase 2 (OR = 0.86, p-value = 1.00E-03). Univariable MR analysis also supported roles for complement and immune cell traits. Although numerous lipid traits were found to be significantly related to AMD, MR-BMA suggested a driving causal role for serum sphingomyelin (marginal inclusion probability [MIP] = 0.76; model-averaged causal estimate [MACE] = 0.29).

Conclusions: The results of this MR study support several putative causal factors for AMD and highlight avenues for future translational research.

Funding: This project was funded by the Wellcome Trust (224643/Z/21/Z; 200990/Z/16/Z); the University of Manchester's Wellcome Institutional Strategic Support Fund (Wellcome ISSF) grant (204796/Z/16/Z); the UK National Institute for Health Research (NIHR) Academic Clinical Fellow and Clinical Lecturer Programmes; Retina UK and Fight for Sight (GR586); the Australian National Health and Medical Research Council (NHMRC) (1150144).

Keywords: AMD; Mendelian randomisation; Mendelian randomization; age-related macular degeneration; causal inference; human; macular degeneration; medicine; statistics.

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Conflict of interest statement

TJ, JC, HG, ES, GB, PS No competing interests declared, SM Cofounder of Seonix Bio Ltd; holds stock in Seonix Bio Ltd, TA Involved in advisory boards and has received grants and speaker fees from Allergan, Novartis, Bayer, Roche, Bausch and Lomb, Heidelberg, Topcon and Canon; received consulting fees from Thea Pharmaceuticals

Figures

Figure 1.
Figure 1.. Plot illustrating the correlations between the beta values for the metabolites considered in our Mendelian randomisation Bayesian model averaging (MR-BMA) analysis for early age-related macular degeneration (AMD).
This plot visually represents the correlation matrix between the genetic associations of the exposure variables with respect to their instruments. The traits are labelled according to their ‘Trait ID’; further information can be found in the Source data 1.
Figure 2.
Figure 2.. Plots outlining the top-ranking models with respect to their posterior probability in the first run of Mendelian randomisation Bayesian model averaging (MR-BMA) of lipid-related traits in early age-related macular degeneration (AMD).
Plots (A) and (C) present Cook’s distance while plots (B) and (D) present Cochran’s Q. Outlier instruments are annotated. The Cochran’s Q is a measure which serves to identify outlier variants with respect to the fit of the linear model. The Q-statistic is used to identify heterogeneity in a meta-analysis, and to pinpoint specific variants as outliers. The contribution of variants to the overall Q-statistic is measured (defined as the weighted squared difference between the observed and predicted association with the outcome) in order to identify outliers. Cook’s distance on the other hand is utilised to identify influential observations (i.e. those variants which have a strong association with the outcome). Such variants are removed from the analysis because they may have an undue influence over variable selection, leading to models which fit that variant well but others poorly.
Figure 3.
Figure 3.. Graph detailing the results of a Mendelian randomisation Bayesian model averaging (MR-BMA) analysis that aims to identify causal lipid-related risk factors for early age-related macular degeneration (AMD).
The studied phenotypes are ranked according to their marginal inclusion probability (MIP); four likely causal traits are highlighted.
See this image and copyright information in PMC

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