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      A proteogenomic signature of age-related macular degeneration in blood

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      Nature Communications
      Nature Publishing Group UK
      Eye diseases, Computational biology and bioinformatics, Predictive markers

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          Abstract

          Age-related macular degeneration (AMD) is one of the most common causes of visual impairment in the elderly, with a complex and still poorly understood etiology. Whole-genome association studies have discovered 34 genomic regions associated with AMD. However, the genes and cognate proteins that mediate the risk, are largely unknown. In the current study, we integrate levels of 4782 human serum proteins with all genetic risk loci for AMD in a large population-based study of the elderly, revealing many proteins and pathways linked to the disease. Serum proteins are also found to reflect AMD severity independent of genetics and predict progression from early to advanced AMD after five years in this population. A two-sample Mendelian randomization study identifies several proteins that are causally related to the disease and are directionally consistent with the observational estimates. In this work, we present a robust and unique framework for elucidating the pathobiology of AMD.

          Abstract

          Age related macular degeneration is a common cause of visual impairment in the elderly, but the etiology is not fully understood. Here, the authors use genetic data, serum proteomics, and AMD phenotypic data from a large Icelandic cohort to discover proteins altered in, causally related to AMD or signifying progression of advanced AMD.

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          Regression Shrinkage and Selection Via the Lasso

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            Regularization Paths for Generalized Linear Models via Coordinate Descent

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

              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
                valur@hjarta.is
                tony.walshe@agios.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 June 2022
                13 June 2022
                2022
                : 13
                : 3401
                Affiliations
                [1 ]GRID grid.420802.c, ISNI 0000 0000 9458 5898, Icelandic Heart Association, ; Holtasmari 1, IS-201 Kopavogur, Iceland
                [2 ]GRID grid.14013.37, ISNI 0000 0004 0640 0021, Faculty of Medicine, , University of Iceland, ; 101 Reykjavik, Iceland
                [3 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institutes for Biomedical Research, ; 22 Windsor Street, Cambridge, MA 02139 USA
                [4 ]GRID grid.419475.a, ISNI 0000 0000 9372 4913, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, ; Bethesda, MD USA
                [5 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institutes for Biomedical Research, ; 10675 John Jay Hopkins Drive, San Diego, CA 92121 USA
                [6 ]GRID grid.410540.4, ISNI 0000 0000 9894 0842, Department of Ophthalmology, , University Hospital, ; Reykjavik, Iceland
                [7 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Division of Epidemiology and Clinical Applications, National Eye Institute, , National Institutes of Health, ; Bethesda, MD USA
                Author information
                http://orcid.org/0000-0001-9982-0524
                http://orcid.org/0000-0002-7661-4872
                http://orcid.org/0000-0001-9158-0087
                http://orcid.org/0000-0003-4374-1178
                http://orcid.org/0000-0003-4733-414X
                http://orcid.org/0000-0002-7998-5433
                http://orcid.org/0000-0002-3238-7612
                http://orcid.org/0000-0002-2046-4350
                http://orcid.org/0000-0001-5130-8417
                http://orcid.org/0000-0001-5696-0084
                http://orcid.org/0000-0001-7055-8251
                Article
                31085
                10.1038/s41467-022-31085-x
                9192739
                35697682
                ac3f9258-a202-4b02-8640-093ae3b5eee5
                © The Author(s) 2022

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 August 2021
                : 1 June 2022
                Funding
                Funded by: The Icelandic Research Fund - RANNIS
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                eye diseases,computational biology and bioinformatics,predictive markers
                Uncategorized
                eye diseases, computational biology and bioinformatics, predictive markers

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