What question did this study set out to answer?

The study investigates genetic mechanisms behind myocardial infarction using eQTL and Mendelian randomization analyses.

March 14, 2026Open Access

Integrated expression quantitative trait loci and Mendelian randomization analyses of the candidate genes and pathways identified for myocardial infarction

Q: What is the clinical evidence from this study?

Study design: Other. Population: myocardial infarction (n=200641). Primary outcome: Associations between candidate genes and myocardial infarction (OR: 1.133 (ADM), 1.174 (BCL6), 1.290 (BNIP3L), 1.184 (CMTM2), 1.145 (DGAT2), 1.114 (HSPA6), 1.084 (IER3), 1.166 (IFNGR1), 1.162 (PLAUR), 1.054 (SERPINB8), 1.057 (VNN2), 0.909 (MRPL35), 0.887 (SNUPN), 95% CI 1.104-1.186 (ADM), 1.019-1.352 (BCL6), 1.050-1.586 (BNIP3L), 1.011-1.387 (CMTM2), 1.008-1.300 (DGAT2), 1.012-1.294 (HSPA6), 1.014-1.160 (IER3), 1.013-1.343 (IFNGR1), 1.049-1.288 (PLAUR), 1.009-1.100 (SERPINB8), 1.018-1.097 (VNN2), 0.848-0.975 (MRPL35), 0.795-0.990 (SNUPN), p=0.001 (ADM), 0.027 (BCL6), 0.015 (BNIP3L), 0.036 (CMTM2), 0.038 (DGAT2), 0.031 (HSPA6), 0.018 (IER3), 0.033 (IFNGR1), 0.004 (PLAUR), 0.017 (SERPINB8), 0.004 (VNN2), 0.007 (MRPL35), 0.033 (SNUPN)).

Resultado clave

The study identified 13 candidate genes associated with myocardial infarction, where ADM, BCL6, BNIP3L, CMTM2, DGAT2, HSPA6, IER3, IFNGR1, PLAUR, SERPINB8, and VNN2 increased the risk of MI (OR > 1) while MRPL35 and SNUPN decreased this risk (OR < 1).

Puntos clave

The study investigates genetic mechanisms behind myocardial infarction using eQTL and Mendelian randomization analyses.
Analyzed independent myocardial infarction datasets from Gene Expression Omnibus.
Conducted differential expression, eQTL, and Mendelian randomization analyses.
Performed GO and KEGG enrichment analyses on candidate genes.
Utilized gene set enrichment analysis to identify associated genes.
Validated gene expression levels using reverse transcription quantitative PCR in an in vitro MI model.
Identified 13 overlapping genes related to myocardial infarction from MR analysis and mRNA expression profiles.
Enrichment analyses revealed involvement of genes in mitochondrial apoptotic pathways.
Gene set enrichment analysis indicated associations with immunological responses and metabolic pathways.
Validated candidate gene expression levels confirmed through RT-qPCR.

PICO estructurado

Población

200,641 European individuals (including 12,801 myocardial infarction patients) from the UK Biobank for GWAS/MR analysis, 49 MI patients and 50 healthy controls from the Gene Expression Omnibus (GSE66360) for differential expression analysis, and an in vitro model of AC16 cells subjected to oxygen and glucose deprivation.

Intervención

Expression quantitative trait loci (eQTL) and Mendelian randomization (MR) analyses to identify genetic mechanisms of myocardial infarction, with in vitro validation using oxygen and glucose deprivation in AC16 cells.

Comparador

Healthy controls (for clinical datasets) and normoxic culture conditions (for in vitro AC16 cell model).

Resultado

Identification of candidate genes causally associated with myocardial infarction by overlapping Mendelian randomization analysis and mRNA expression profiles.surrogate

Integrated eQTL and Mendelian randomization analyses identified 13 candidate genes causally linked to myocardial infarction, providing potential novel targets for therapeutic intervention.

Resultado numérico

Estimación del efecto: OR: 1.133 (ADM), 1.174 (BCL6), 1.290 (BNIP3L), 1.184 (CMTM2), 1.145 (DGAT2), 1.114 (HSPA6), 1.084 (IER3), 1.166 (IFNGR1), 1.162 (PLAUR), 1.054 (SERPINB8), 1.057 (VNN2), 0.909 (MRPL35), 0.887 (SNUPN) (95% CI 1.104-1.186 (ADM), 1.019-1.352 (BCL6), 1.050-1.586 (BNIP3L), 1.011-1.387 (CMTM2), 1.008-1.300 (DGAT2), 1.012-1.294 (HSPA6), 1.014-1.160 (IER3), 1.013-1.343 (IFNGR1), 1.049-1.288 (PLAUR), 1.009-1.100 (SERPINB8), 1.018-1.097 (VNN2), 0.848-0.975 (MRPL35), 0.795-0.990 (SNUPN))

valor p: p=0.001 (ADM), 0.027 (BCL6), 0.015 (BNIP3L), 0.036 (CMTM2), 0.038 (DGAT2), 0.031 (HSPA6), 0.018 (IER3), 0.033 (IFNGR1), 0.004 (PLAUR), 0.017 (SERPINB8), 0.004 (VNN2), 0.007 (MRPL35), 0.033 (SNUPN)

Limitaciones

No bidirectional Mendelian randomization performed to rule out reverse causality.
Colocalization analysis not conducted to confirm shared causal variants.
Potential heterogeneity due to differences in sex, age, and health status.
Findings largely based on individuals of European descent.
Emphasizes bioinformatics analysis and in vitro experiments, limiting translational potential.

Resumen

Background Myocardial infarction (MI) is a myocardial necrosis event caused by an unstable ischemic state that reduces life expectancy primarily through cardiac functional impairment and cardiomyocyte death. The present study aims to investigate the genetic mechanisms underlying MI by integrating expression quantitative trait loci (eQTLs) and Mendelian randomization (MR) analyses. Methods We comprehensively analyzed independent MI datasets from the Gene Expression Omnibus database. The relationships between MI and the differentially expressed genes were evaluated through differential expression, eQTL, and MR analyses. Additionally, GO and KEGG enrichment analyses were performed to clarify the functional pathways of the candidate genes, and gene set enrichment analysis (GSEA) was used to identify the genes associated with MI. An in vitro model of MI was established by subjecting AC16 cells to oxygen and glucose deprivation, and the gene expression levels were validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results By comparing the results from the MR analysis and mRNA expression profiles, we identified 13 overlapping genes: MRPL35 , SNUPN , ADM , BCL6 , BNIP3L , CMTM2 , DGAT2 , HSPA6 , IER3 , IFNGR1 , PLAUR , SERPINB8 , and VNN2 . The GO and KEGG enrichment analyses revealed that these genes participate in essential biological processes, including mitochondrial apoptotic and mitochondrial organization regulatory pathways. GSEA demonstrated that the candidate genes were enriched in the NOD-like signaling pathways; immunological responses; and lysosomal, ribosomal, and metabolic pathways related to MI. Furthermore, the gene expression levels were verified through RT-qPCR. Conclusion This study highlights the potential of specific molecular pathways for targeted treatment of MI. Our work also warrants additional research efforts to elucidate the genetic mechanisms of MI.

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