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      Genetic causality and metabolite pathway identifying the relationship of blood metabolites and psoriasis

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          Abstract

          Background

          Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis.

          Materials and Methods

          We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary‐data‐based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0.

          Results

          In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results ( β = 0.001, < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis.

          Conclusion

          Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.

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          Most cited references45

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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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            LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

            Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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              Interpreting findings from Mendelian randomization using the MR-Egger method

              Mendelian randomization-Egger (MR-Egger) is an analysis method for Mendelian randomization using summarized genetic data. MR-Egger consists of three parts: (1) a test for directional pleiotropy, (2) a test for a causal effect, and (3) an estimate of the causal effect. While conventional analysis methods for Mendelian randomization assume that all genetic variants satisfy the instrumental variable assumptions, the MR-Egger method is able to assess whether genetic variants have pleiotropic effects on the outcome that differ on average from zero (directional pleiotropy), as well as to provide a consistent estimate of the causal effect, under a weaker assumption—the InSIDE (INstrument Strength Independent of Direct Effect) assumption. In this paper, we provide a critical assessment of the MR-Egger method with regard to its implementation and interpretation. While the MR-Egger method is a worthwhile sensitivity analysis for detecting violations of the instrumental variable assumptions, there are several reasons why causal estimates from the MR-Egger method may be biased and have inflated Type 1 error rates in practice, including violations of the InSIDE assumption and the influence of outlying variants. The issues raised in this paper have potentially serious consequences for causal inferences from the MR-Egger approach. We give examples of scenarios in which the estimates from conventional Mendelian randomization methods and MR-Egger differ, and discuss how to interpret findings in such cases. Electronic supplementary material The online version of this article (doi:10.1007/s10654-017-0255-x) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                quanbozhang@126.com
                wanghan@swjtu.edu.cn
                Journal
                Skin Res Technol
                Skin Res Technol
                10.1111/(ISSN)1600-0846
                SRT
                Skin Research and Technology
                John Wiley and Sons Inc. (Hoboken )
                0909-752X
                1600-0846
                04 July 2024
                July 2024
                : 30
                : 7 ( doiID: 10.1111/srt.v30.7 )
                : e13840
                Affiliations
                [ 1 ] Department of Cardiology Affiliated Hospital of Southwest Jiaotong University The Third People's Hospital of Chengdu Chengdu Sichuan China
                [ 2 ] Department of Geriatrics North Sichuan Medical College Nanchong Sichuan China
                [ 3 ] Department of Information Technology Affiliated Hospital of Southwest Jiaotong University The Third People's Hospital of Chengdu Chengdu Sichuan China
                [ 4 ] Department of Laboratory Medicine Affiliated Hospital of Southwest Jiaotong University The Third People's Hospital of Chengdu Chengdu Sichuan China
                Author notes
                [*] [* ] Correspondence

                Han Wang, Department of Cardiology, Affiliated Hospital of Southwest Jiaotong University, The Third People's Hospital of Chengdu , Chengdu, Sichuan, China.

                Email: wanghan@ 123456swjtu.edu.cn

                Quan‐Bo Zhang, Department of Geriatrics, North Sichuan Medical College, Nanchong, Sichuan, China.

                Email: quanbozhang@ 123456126.com

                Article
                SRT13840
                10.1111/srt.13840
                11224122
                38965811
                6235be06-cc49-47cb-bfaa-2a3a44ad7077
                © 2024 The Author(s). Skin Research and Technology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 18 March 2024
                : 17 June 2024
                Page count
                Figures: 5, Tables: 2, Pages: 10, Words: 4930
                Funding
                Funded by: Medical research project of Chengdu Health Commission
                Award ID: 2021206
                Categories
                Original Article
                Original Article
                Custom metadata
                2.0
                July 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.5 mode:remove_FC converted:05.07.2024

                blood metabolites,generalized summary‐data‐based mendelian randomization,linkage disequilibrium score,mendelian randomization,metabolic pathways,psoriasis

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