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      Mendelian randomization analyses in ocular disease: a powerful approach to causal inference with human genetic data

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          Abstract

          Ophthalmic epidemiology is concerned with the prevalence, distribution and other factors relating to human eye disease. While observational studies cannot avoid confounding factors from interventions, human eye composition and structure are unique, thus, eye disease pathogenesis, which greatly impairs quality of life and visual health, remains to be fully explored. Notwithstanding, inheritance has had a vital role in ophthalmic disease. Mendelian randomization (MR) is an emerging method that uses genetic variations as instrumental variables (IVs) to avoid confounders and reverse causality issues; it reveals causal relationships between exposure and a range of eyes disorders. Thus far, many MR studies have identified potentially causal associations between lifestyles or biological exposures and eye diseases, thus providing opportunities for further mechanistic research, and interventional development. However, MR results/data must be interpreted based on comprehensive evidence, whereas MR applications in ophthalmic epidemiology have some limitations worth exploring. Here, we review key principles, assumptions and MR methods, summarise contemporary evidence from MR studies on eye disease and provide new ideas uncovering aetiology in ophthalmology.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12967-022-03822-9.

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          Most cited references172

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          Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data

          Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed.
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            Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis.

            Glaucoma is the leading cause of global irreversible blindness. Present estimates of global glaucoma prevalence are not up-to-date and focused mainly on European ancestry populations. We systematically examined the global prevalence of primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG), and projected the number of affected people in 2020 and 2040. Systematic review and meta-analysis. Data from 50 population-based studies (3770 POAG cases among 140,496 examined individuals and 786 PACG cases among 112 398 examined individuals). We searched PubMed, Medline, and Web of Science for population-based studies of glaucoma prevalence published up to March 25, 2013. Hierarchical Bayesian approach was used to estimate the pooled glaucoma prevalence of the population aged 40-80 years along with 95% credible intervals (CrIs). Projections of glaucoma were estimated based on the United Nations World Population Prospects. Bayesian meta-regression models were performed to assess the association between the prevalence of POAG and the relevant factors. Prevalence and projection numbers of glaucoma cases. The global prevalence of glaucoma for population aged 40-80 years is 3.54% (95% CrI, 2.09-5.82). The prevalence of POAG is highest in Africa (4.20%; 95% CrI, 2.08-7.35), and the prevalence of PACG is highest in Asia (1.09%; 95% CrI, 0.43-2.32). In 2013, the number of people (aged 40-80 years) with glaucoma worldwide was estimated to be 64.3 million, increasing to 76.0 million in 2020 and 111.8 million in 2040. In the Bayesian meta-regression model, men were more likely to have POAG than women (odds ratio [OR], 1.36; 95% CrI, 1.23-1.52), and after adjusting for age, gender, habitation type, response rate, and year of study, people of African ancestry were more likely to have POAG than people of European ancestry (OR, 2.80; 95% CrI, 1.83-4.06), and people living in urban areas were more likely to have POAG than those in rural areas (OR, 1.58; 95% CrI, 1.19-2.04). The number of people with glaucoma worldwide will increase to 111.8 million in 2040, disproportionally affecting people residing in Asia and Africa. These estimates are important in guiding the designs of glaucoma screening, treatment, and related public health strategies. Copyright © 2014 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.
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              An Atlas of Genetic Correlations across Human Diseases and Traits

              Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
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                Author and article information

                Contributors
                syyangxh@scut.edu.cn
                liuleijiao@163.com
                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central (London )
                1479-5876
                26 December 2022
                26 December 2022
                2022
                : 20
                : 621
                Affiliations
                [1 ]GRID grid.412449.e, ISNI 0000 0000 9678 1884, Department of Epidemiology, School of Public Health, , China Medical University, ; Shenyang, Liaoning China
                [2 ]GRID grid.413405.7, ISNI 0000 0004 1808 0686, Guangdong Eye Institute, Department of Ophthalmology, , Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, ; Guangzhou, 510080 China
                [3 ]GRID grid.413405.7, ISNI 0000 0004 1808 0686, Guangdong Cardiovascular Institute, , Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, ; Guangzhou, China
                [4 ]GRID grid.412449.e, ISNI 0000 0000 9678 1884, Department of Mathematics, School of Fundamental Sciences, , China Medical University, ; Shenyang, Liaoning China
                Author information
                http://orcid.org/0000-0001-9466-7591
                Article
                3822
                10.1186/s12967-022-03822-9
                9793675
                36572895
                00b87734-ef7b-404d-8c80-40681b822139
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 April 2022
                : 11 December 2022
                Funding
                Funded by: Science and Technology Program of Guangzhou, China
                Award ID: 202002020049
                Award Recipient :
                Funded by: Project of Special Research on Cardiovascular Diseases
                Award ID: 2020XXG007
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81870663
                Award ID: 82171075
                Award Recipient :
                Funded by: Outstanding Young Talent Trainee Program of Guangdong Provincial People’s Hospital
                Award ID: KJ012019087
                Award Recipient :
                Funded by: ), Guangdong Provincial People’s Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province
                Award ID: KJ012019457
                Award Recipient :
                Funded by: Talent Introduction Fund of Guangdong Provincial People’s Hospital
                Award ID: Y012018145
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100018559, Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program;
                Award ID: RC190146
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2022

                Medicine
                causality,eye disease,instrumental variables,mendelian randomization
                Medicine
                causality, eye disease, instrumental variables, mendelian randomization

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