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      Causal effect between total cholesterol and HDL cholesterol as risk factors for chronic kidney disease: a mendelian randomization study

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          Abstract

          Background

          While observational studies show an association between serum lipid levels and cardiovascular disease (CVD), intervention studies that examine the preventive effects of serum lipid levels on the development of CKD are lacking.

          Methods

          To estimate the role of serum lipid levels in the etiology of CKD, we conducted a two-sample mendelian randomization (MR) study on serum lipid levels. Single nucleotide polymorphisms (SNPs), which were significantly associated genome-wide with serum lipid levels from the GLGC and CKDGen consortium genome-wide association study (GWAS), including total cholesterol (TC, n = 187,365), triglyceride (TG, n = 177,861), HDL cholesterol (HDL-C, n = 187,167), LDL cholesterol (LDL-C, n = 173,082), apolipoprotein A1 (ApoA1, n = 20,687), apolipoprotein B (ApoB, n = 20,690) and CKD ( n = 117,165), were used as instrumental variables. None of the lipid-related SNPs was associated with CKD (all P > 0.05).

          Results

          MR analysis genetically predicted the causal effect between TC/HDL-C and CKD. The odds ratio (OR) and 95% confidence interval (CI) of TC within CKD was 0.756 (0.579 to 0.933) ( P = 0.002), and HDL-C was 0.85 (0.687 to 1.012) ( P = 0.049). No causal effects between TG, LDL-C- ApoA1, ApoB and CKD were observed. Sensitivity analyses confirmed that TC and HDL-C were significantly associated with CKD.

          Conclusions

          The findings from this MR study indicate causal effects between TC, HDL-C and CKD. Decreased TC and elevated HDL-C may reduce the incidence of CKD but need to be further confirmed by using a genetic and environmental approach.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12882-020-02228-3.

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          Most cited references33

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

          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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            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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              The MR-Base platform supports systematic causal inference across the human phenome

              Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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                Author and article information

                Contributors
                675666690@qq.com
                minlingyan08@163.com
                aishizhu@126.com
                ryjq123@sina.com
                1367385674@qq.com
                dgx920@163.com
                wangyong007-007@163.com
                2313385913@qq.com
                675666690@qq.com
                Journal
                BMC Nephrol
                BMC Nephrol
                BMC Nephrology
                BioMed Central (London )
                1471-2369
                20 January 2021
                20 January 2021
                2021
                : 22
                : 35
                Affiliations
                [1 ]GRID grid.477425.7, Departments of Cardiology, , Liuzhou People’s Hospital, ; 8 Wenchang Road, Liuzhou, 545006 Guangxi People’s Republic of China
                [2 ]GRID grid.477425.7, Departments of Nephrology, , Liuzhou People’s Hospital, ; 8 Wenchang Road, Liuzhou, 545006 Guangxi People’s Republic of China
                [3 ]GRID grid.459785.2, Departments of Cardiology, , The First People’s Hospital of Nanning, ; 89 Qixing Road, Nanning, 530022 Guangxi People’s Republic of China
                Author information
                http://orcid.org/0000-0001-6642-7005
                Article
                2228
                10.1186/s12882-020-02228-3
                7818570
                33472594
                8799bc98-197c-403d-b5c0-60c14b7ce5fa
                © The Author(s) 2021

                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
                : 16 September 2020
                : 29 December 2020
                Funding
                Funded by: Guangxi self-financing research projects
                Award ID: Z20190025
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Nephrology
                two-sample mendelian randomization,genome-wide association study,serum lipid levels,chronic kidney disease,causation

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