ArticleFrontiers in lupus2023
Modeling of horizontal pleiotropy identifies possible causal gene expression in systemic lupus erythematosus.
Article in Frontiers in lupus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Integrative mendelian randomization approaches for therapeutic target prioritisation in immune-mediated diseases.Scientific reports · 2026Article
- Mitochondrial Gene Regulation and Pain Susceptibility: A Multi-Omics Causal Inference Study.International journal of molecular sciences · 2025Article
- Mendelian randomization in cancer research: opportunities and challenges.Infectious agents and cancer · 2025Article
- Unlocking therapeutic potential: Targeting lymphocyte activation Gene-3 (LAG-3) with fibrinogen-like protein 1 (FGL1) in systemic lupus erythematosus.Journal of translational autoimmunity · 2024Review
- Pervasive Sharing of Causal Genetic Risk Factors Contributes to Clinical and Molecular Overlap between Sjögren's Disease and Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune condition with complex causes involving genetic and environmental factors. While genome-wide association studies (GWASs) have identified genetic loci associated with SLE, the functional genomic elements responsible for disease development remain largely unknown. Mendelian Randomization (MR) is an instrumental variable approach to causal inference based on data from observational studies, where genetic variants are employed as instrumental variables (IVs). Methods: This study utilized a two-step strategy to identify causal genes for SLE. In the first step, the classical MR method was employed, assuming the absence of horizontal pleiotropy, to estimate the causal effect of gene expression on SLE. In the second step, advanced probabilistic MR methods (PMR-Egger, MRAID, and MR-MtRobin) were applied to the genes identified in the first step, considering horizontal pleiotropy, to filter out false positives. PMR-Egger and MRAID analyses utilized whole blood expression quantitative trait loci (eQTL) and SLE GWAS summary data, while MR-MtRobin analysis used an independent eQTL dataset from multiple immune cell types along with the same SLE GWAS data. Results: The initial MR analysis identified 142 genes, including 43 outside of chromosome 6. Subsequently, applying the advanced MR methods reduced the number of genes with significant causal effects on SLE to 66. PMR-Egger, MRAID, and MR-MtRobin, respectively, identified 13, 7, and 16 non-chromosome 6 genes with significant causal effects. All methods identified expression of Conclusions: The findings from the three MR methods exhibited overlapping genes with causal effects on SLE, demonstrating consistent results. However, each method also uncovered unique genes due to different modelling assumptions and technical factors, highlighting the complementary nature of the approaches. Importantly, MRAID demonstrated a reduced percentage of causal genes from the Major Histocompatibility complex (MHC) region on chromosome 6, indicating its potential in minimizing false positive findings. This study contributes to unraveling the mechanisms underlying SLE by employing advanced probabilistic MR methods to identify causal genes, thereby enhancing our understanding of SLE pathogenesis.
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