Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder

Yang, Huiru; Liu, Di; Zhao, Chuntao; Feng, Bowen; Lu, Wenjin; Yang, Xiaohan; Xu, Minglu; Zhou, Weizhu; Jing, Huiquan; Yang, Jingyun

doi:10.1038/s41398-021-01348-0

Cited by 25 publications

(19 citation statements)

References 48 publications

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“…BTN3A2 is the gene most closely connected with treatment response according to a genome-wide methylation analysis of DNA in RA patients receiving anti-rheumatic therapy for the first time (52). BTN3A2 has also shown a pleiotropic association with MDD (53). TNF stimulates neurons to produce CCL2, CCL7, and CXCL10.…”

Section: Discussionmentioning

confidence: 99%

Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis

Zhou

Sun²,

Tang³

et al. 2023

Front. Immunol.

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BackgroundRheumatoid arthritis (RA) and depression are prevalent diseases that have a negative impact on the quality of life and place a significant economic burden on society. There is increasing evidence that the two diseases are closely related, which could make the disease outcomes worse. In this study, we aimed to identify diagnostic markers and analyzed the therapeutic potential of key genes.MethodsWe assessed the differentially expressed genes (DEGs) specific for RA and Major depressive disorder (MDD) and used weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules by obtaining the Gene expression profile data from Gene Expression Omnibus (GEO) database. By using the STRING database, a protein–protein interaction (PPI) network constructed and identified key genes. We also employed two types of machine learning techniques to derive diagnostic markers, which were assessed for their association with immune cells and potential therapeutic effects. Molecular docking and in vitro experiments were used to validate these analytical results.ResultsIn total, 48 DEGs were identified in RA with comorbid MDD. The PPI network was combined with WGCNA to identify 26 key genes of RA with comorbid MDD. Machine learning-based methods indicated that RA combined with MDD is likely related to six diagnostic markers: AURKA, BTN3A2, CXCL10, ERAP2, MARCO, and PLA2G7. CXCL10 and MARCO are closely associated with diverse immune cells in RA. However, apart from PLA2G7, the expression levels of the other five genes were associated with the composition of the majority of immune cells in MDD. Molecular docking and in vitro studies have revealed that Aucubin (AU) exerts the therapeutic effect through the downregulation of CXCL10 and BTN3A2 gene expression in PC12 cells.ConclusionOur study indicates that six diagnostic markers were the basis of the comorbidity mechanism of RA and MDD and may also be potential therapeutic targets. Further mechanistic studies of the pathogenesis and treatment of RA and MDD may be able to identify new targets using these shared pathways.

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Section: Discussionmentioning

confidence: 99%

Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis

Zhou

Sun²,

Tang³

et al. 2023

Front. Immunol.

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“…The MHC region is a complex genomic region due to its linkage disequilibrium and its roles in multiple diseases [40]. Some researchers ignored this region in their MR studies [41], but concerns have been raised about possible pleiotropy when using MHC instruments.…”

Section: Discussionmentioning

confidence: 99%

Exploring the possible causal role of the immune response to varicella-zoster virus on multiple traits: a phenome-wide Mendelian randomization study

Lophatananon

Mekli

et al. 2022

Preprint

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Background The immune response to infections could be largely driven by the individuals genes, especially in the major histocompatibility complex (MHC) region. Varicella-zoster virus (VZV) is a highly communicable pathogen. In addition to infection, the re-activations of VZV can be a potential causal factor for multiple traits. Identification of VZV immune response-related health conditions can therefore help elucidate the aetiology of certain diseases. Methods A phenome-wide Mendelian randomization study (MR-PheWAS) of anti-VZV Immunoglobulin G (IgG) level with 1,370 traits was conducted to explore the potential causal role of VZV-specific immunity on multiple traits using the UK Biobank cohort. For each trait, we performed Mendelian randomization (MR) analyses using five methods with or without instrumental variables (IVs) in the MHC region. Results We identified 49 single nucleotide polymorphisms (SNPs) associated with anti-VZV IgG level as IVs, five of which were located in the MHC region. Statistical evidence for a causal effect of anti-VZV IgG level on 75 traits was found in at least three of the MR methods, 60 traits if IVs in the MHC region were removed from MR analysis. With or without the MHC IVs, we found that a higher anti-VZV IgG level led to increased risk of 14 diseases and lower risks of 9 diseases. In addition, anti-VZV IgG level was causally associated with 5 biomarker-related traits. Conclusions Anti-VZV IgG was causally associated with multiple traits. MHC IVs had a substantial impact on MR causal inference, and therefore, should not be neglected from analysis.

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“…Results for the Compulsive and Neurodevelopmental factors were limited, likely reflecting the lower power for the traits defining these factors. Hits for the Internalizing factor included RPL31P12 , a gene previously shown to be potentially causally associated with MDD 34 and a top hit for a cross-trait analysis of MDD and insomnia. 35 Another Internalizing hit, RAB27B , was recently associated with MDD, risk for Alzheimer’s disease, and a range of associated outcomes, including increased tangles, β-amyloid and cognitive decline.…”

Section: Discussionmentioning

confidence: 99%

T-SEM of 11 Major Psychiatric Disorders: Identification of Gene Expression Patterns for Cross-Disorder Risk and Drug Repurposing

Grotzinger¹,

Singh

Lam

et al. 2022

Preprint

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Importance: Psychiatric disorders display high levels of comorbidity and genetic overlap, necessitating multivariate approaches for parsing convergent and divergent psychiatric risk pathways. Identifying gene expression patterns underlying cross-disorder risk also stands to propel drug discovery and repurposing in the face of rising levels of polypharmacy. Objective: To identify gene expression patterns underlying genetic convergence and divergence across psychiatric disorders along with existing pharmacological interventions that target these genes. Design: This genomic study applied a multivariate transcriptomic method, Transcriptome-wide Structural Equation Modeling (T-SEM), to investigate gene expression patterns associated with four genomic factors indexing shared risk across 11 major psychiatric disorders. Follow-up tests, including overlap with gene sets for other outcomes and phenome-wide association studies, were conducted to better characterize T-SEM results. Public databases describing drug-gene pairs were used to identify drugs that could be repurposed to target genes found to be associated with cross-disorder risk. Main Outcomes and Measures: Gene expression patterns associated with genomic factors or disorder-specific risk and existing drugs that target these genes. Results: In total, T-SEM identified 451 genes whose expression was associated with the genomic factors and 41 genes with disorder-specific effects. We find the most hits for a Thought Disorders factor defined by bipolar disorder and schizophrenia. We identify 39 existing pharmacological interventions that could be repurposed to target gene expression hits for this same factor. Conclusions and Relevance: The findings from this study shed light on patterns of gene expression associated with genetic overlap and uniqueness across psychiatric disorders. Future versions of the multivariate drug repurposing framework outlined here have the potential to identify novel pharmacological interventions for increasingly common, comorbid psychiatric presentations.

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Mendelian randomization integrating GWAS and eQTL data revealed genes pleiotropically associated with major depressive disorder

Cited by 25 publications

References 48 publications

Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis

Potential diagnostic markers and therapeutic targets for rheumatoid arthritis with comorbid depression based on bioinformatics analysis

Exploring the possible causal role of the immune response to varicella-zoster virus on multiple traits: a phenome-wide Mendelian randomization study

T-SEM of 11 Major Psychiatric Disorders: Identification of Gene Expression Patterns for Cross-Disorder Risk and Drug Repurposing

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