Multi-omics Mendelian Randomization Data for Mitochondrial Genes and Endometriosis

Sha Wang's dataset contains results from a multi-omics bioinformatics analysis investigating causal relationships between mitochondrial genes and endometriosis. The data likely includes results from Mendelian randomization analyses, machine learning feature selection, and immune cell correlation assessments. It was last updated on 2026-05-04.

Use Cases

• Validate causal relationships between mitochondrial genes and endometriosis based on Mendelian randomization results.
• Train or test predictive models for endometriosis based on the identified feature genes GPD2 and MRPS6.
• Investigate correlations between mitochondrial dysfunction and immune cell infiltration (e.g., macrophages, NK cells) in endometriosis.
• Perform functional enrichment analysis on gene sets related to steroid biosynthesis and fatty acid elongation pathways.

Strengths

• Analysis integrates data from multiple sources, including eQTL, pQTL, and mQTL data from the eQTLGen, DECODE, and Brisbane databases.
• Identifies 128 mitochondrial genes with significant causal relationships to endometriosis and five key feature genes (PHYH, GPD2, C12orf65, MRPS6, RPL21).
• Includes experimental validation via immunohistochemistry on patient samples, supporting transcriptomic findings.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small (15.9 KB), indicating it likely contains summary results rather than raw genomic data.

Provenance

Source

figshare, author Sha Wang.

Collection Method

Bioinformatics analysis incorporating GWAS data from FinnGen, eQTL/pQTL/mQTL data, and machine learning on gene expression datasets GSE51981 and GSE7305.

Time Range

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Freshness

Last updated 2026-05-04 05:30:47; freshness should be verified.

Geography

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