mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite–Phenotype Associations

Chang, Le; Zhou, Guohui; Xia, Jianguo

doi:10.3390/metabo13070826

Cited by 14 publications

(3 citation statements)

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“…Based on the selections, the program searches for potential instrumental variables (i.e. SNPs) that are associated with both the metabolite from our large collections of the recent mGWAS studies ( 14 ) and the disease from the OpenGWAS database ( 17 ). The next step is to perform SNP filtering and harmonization to identify independent SNPs through linkage disequilibrium (LD) clumping ( 34 ).…”

Section: Causal Analysis Via Two-sample Mendelian Randomizationmentioning

confidence: 99%

“…Identification of causal links from this large number of metabolite-phenotype relations is a natural next step. It has become possible recently with the availability of many metabolomic genome-wide association studies (mGWAS) that link metabolites and genotypes ( 14–16 ). By integrating mGWAS data with comparable GWAS data that associate genotypes with various phenotypes ( 17 ), we can now estimate causal relationships between a metabolite and a phenotype of interest through Mendelian randomization (MR) ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

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MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

Pang,

Lu,

Zhou

et al. 2024

Nucleic Acids Research

Self Cite

185

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We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC–MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

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Section: Causal Analysis Via Two-sample Mendelian Randomizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

Pang,

Lu,

Zhou

et al. 2024

Nucleic Acids Research

Self Cite

185

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show abstract

“…As genetic variation originates from the parent and remains constant, it satis es the rationality of the time sequence and is not easily in uenced by traditional confounding factors, such as environment and behavior [10]. Therefore, in recent years, MR has been widely used to investigate causal relationships between exposure and outcomes [11]. In this study, we utilized a two-sample Mendelian randomization (TSMR) analysis to determine if liver iron content, percent liver fat, and liver volume are causally related to an increased risk of HCC.…”

Section: Introductionmentioning

confidence: 99%

Causal Relationship Between Liver Iron Content, Liver Fat Percentage, Liver Volume, and Hepatocellular Carcinoma: A Mendelian Randomization Study

Han,

Li,

Wang

et al. 2023

Preprint

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Objective To investigate the causal relationship between liver iron content, percent liver fat, liver volume, and hepatocellular carcinoma (HCC), using Mendelian randomization (MR). Methods Tools from public databases were utilized as instrumental variables to analyze the links between the exposure factors (liver iron content, percent liver fat, and liver volume) and the outcome (HCC) through two-sample Mendelian randomization analysis. The inverse-variance weighted method (IVW) was employed for the primary analysis, with MR-Egger regression, weighted median, simple mode, and weighted mode as the supplementary methods. The causal relationship among liver iron content, percent liver fat, liver volume, and HCC was evaluated using the effect indicator odds ratio (OR) and 95% confidence interval (CI). Leave-one-out analysis, heterogeneity tests, and horizontal pleiotropy tests were performed to validate the stability and reliability of the results. Results The IVW for liver iron content was OR = 1.0009, 95% CI: 1.0005–1.0013, using 10 single nucleotide polymorphisms (SNPs) as instrumental variables. The IVW for percent liver fat with 10 SNPs as instrumental variables was OR = 1.0015, 95% CI: 1.0012–1.0019. The IVW for liver volume was OR = 1.0009, 95% CI: 0.9996–1.0023, using 11 SNPs as instrumental variables. The IVW p-values for liver iron content and percent liver fat were less than 0.05, indicating a causal relationship with HCC. However, the IVW p-value for liver volume was greater than 0.05, suggesting no causal relationship between liver volume and HCC. Conclusion This study revealed that higher liver iron content and percent liver fat were associated with an increased risk of hepatocellular carcinoma. However, no causal relationship was established between liver volume and HCC.

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Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Zhu,

Chen,

Zhang

et al. 2023

Hum. Genet.

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mGWAS-Explorer 2.0: Causal Analysis and Interpretation of Metabolite–Phenotype Associations

Cited by 14 publications

References 70 publications

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

Causal Relationship Between Liver Iron Content, Liver Fat Percentage, Liver Volume, and Hepatocellular Carcinoma: A Mendelian Randomization Study

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

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