The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study

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Abstract

Background

Age-related macular degeneration (AMD) is a leading cause of vision loss. Whereas lipids have been studied extensively to understand their effects on cardiovascular diseases, their relationship with AMD remains unclear.

Methods

Two-sample Mendelian randomization (MR) analyses were performed to systematically evaluate the causal relationships between eight serum lipid biomarkers, consisting of apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL-C), direct low-density lipoprotein cholesterol (LDL-C), lipoprotein A [Lp(a)], triglycerides (TG) and non-HDL cholesterol (non-HDL-C), and the risk of different AMD stages and subtypes. We derived 64–407 genetic instruments for eight serum lipid biomarkers in 419 649 participants of European descent from the UK Biobank cohort. We conducted genome-wide association studies (GWAS) for 12 711 advanced AMD cases [8544 choroidal neovascularization (CNV) and 2656 geographic atrophy (GA) specific AMD subtypes] and 5336 intermediate AMD cases with 14 590 controls of European descent from the International AMD Genomics Consortium.

Results

Higher genetically predicted HDL-C and ApoA1 levels increased the risk of all AMD subtypes. LDL-C, ApoB, CHOL and non-HDL-C levels were associated with decreased risk of intermediate and GA AMD but not with CNV. Genetically predicted TG levels were associated with decreased risk of different AMD subtypes. Sensitivity analyses revealed no evidence for directional pleiotropy effects. In our multivariable MR analyses, adjusting for the effects of correlated lipid biomarkers yielded similar results.

Conclusion

These results suggest the role of lipid metabolism in drusen formation and particularly in AMD development at the early and intermediate stages. Mechanistic studies are warranted to investigate the utility of lipid pathways for therapeutic treatment in preventing AMD.

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Most cited references 59

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PLINK: a tool set for whole-genome association and population-based linkage analyses.

Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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The UK Biobank resource with deep phenotyping and genomic data

Clare Bycroft, Colin Freeman, Desislava Petkova … (2018)

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Author and article information

Contributors

Xikun Han: (View ORCID Profile)

Jue-Sheng Ong: (View ORCID Profile)

Puya Gharahkhani: (View ORCID Profile)

Stuart MacGregor: (View ORCID Profile)

Journal

Title: International Journal of Epidemiology

Publisher: Oxford University Press (OUP)

ISSN (Print): 0300-5771

ISSN (Electronic): 1464-3685

Publication date Created: February 01 2021

Publication date Created: March 03 2021

Publication date Created: November 19 2020

Publication date Other: February 01 2021

Publication date (Print): March 03 2021

Publication date (Electronic): November 19 2020

Volume: 50

Issue: 1

Pages: 325-336

Affiliations

[1 ]Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia

[2 ]School of Medicine, University of Queensland, Brisbane, Australia

[3 ]Menzies Institute for Medical Research, University of Tasmania, Australia

[4 ]Centre for Eye Research Australia, University of Melbourne, Australia

Article

DOI: 10.1093/ije/dyaa178

PubMed ID: 33211829

SO-VID: 43cb6089-6148-452e-88d3-af7f351d2612

License:

https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study

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Abstract

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Most cited references 59

PLINK: a tool set for whole-genome association and population-based linkage analyses.

The UK Biobank resource with deep phenotyping and genomic data

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

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