Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (633 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The use of the genetic variation of known functions or phenotypes to correlate the causal effects of those functions or phenotypes with a disease outcome.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Analysis, Mendelian Randomization.
Where it fits in the MeSH classification
- Broader topics
- Molecular Epidemiology
Related concepts
- Genetic Variation
Research landscape
PubMed indexes 14,568 records tagged with the MeSH heading “Mendelian Randomization Analysis” (counts retrieved 2026-10-29). 13,407 of them (92.0%) were published from 2020 onward, and the busiest period so far is 2020 onward with 13,407 records.
Reviews account for 1,360 records (9.3%) and meta-analyses for 979. Randomized controlled trials number 30 (0.2%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 0 |
| 2000-2009 | 7 |
| 2010-2019 | 1,221 |
| 2020 onward | 13,407 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 1,360 | 9.3% |
| Meta-analyses | 979 | 6.7% |
| Randomized controlled trials | 30 | 0.2% |
Method: PubMed query "Mendelian Randomization Analysis"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-29.
Reading the evidence in clinical research
Research methods determine how much a result can be trusted. Randomized trials guard against confounding, systematic reviews and meta-analyses combine many studies, and observational designs are valuable but can show only association. Reporting standards, trial registration and pre-specified outcomes reduce bias. When reading about a method, ask what problem it is designed to solve and which problems it does not solve.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Integrating Single-Cell Transcriptome-Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune-Related Drug Targets for Cancers | 2025 | Research article Design not recorded in structured metadata | 5 | cc by PMID 41216857 · DOI · PMC12697907 |
| Early diagnostic biomarkers for acute myocardial infarction unveiled by metabolomics, Mendelian randomization, and machine learning | 2026 | Research article Design not recorded in structured metadata | 4 | cc by PMID 41526548 · DOI · PMC12796025 |
| Disentangling the inverse relationship between cancer and Alzheimer's or Parkinson's disease: A systematic review on Mendelian randomization studies | 2025 | Systematic review Design not recorded in structured metadata | 4 | cc by PMID 41241298 · DOI · PMC12766696 |
Integrating Single-Cell Transcriptome-Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune-Related Drug Targets for Cancers [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Single-cell expression quantitative trait loci data offer promising opportunities to inform immune-related drug development in cancer.
- Closing excerpt
- These results demonstrate how combining genetic, transcriptomic and clinical trial evidence can reduce pleiotropic bias, and prioritize immune-related drug targets for cancer prevention.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (203 words)
Single-cell expression quantitative trait loci data offer promising opportunities to inform immune-related drug development in cancer. However, pleiotropy can complicate causal inference. We introduce MR-DEG, a framework that integrates Mendelian randomization (MR) and differential expressed gene (DEG) to strengthen causal inference. Using eight conventional MR and colocalization methods, we estimated effects of 11 021 dynamic gene expression profiles during CD4+ T cell activation on the risk of six cancer types. This identified 1000 gene-cancer pairs with putative effects (https://www.omicsharbour.com/sc-eqtl-mr). Of these 1000 pairs, 517 involved 205 unique genes that were differentially expressed in relevant cancer tissues based on single-cell RNA-sequencing data. Of these 517 pairs, 265 were classified as likely causal using the conventional MR methods. After applying MR-DEG to the remaining 252 potentially pleiotropic pairs, an additional 89 were classified as likely causal. Sixty-four and 391 of the 1000 original pairs exhibited time- and non-time dependent effects on cancer risk, respectively. Integrating the 1000 gene-cancer pairs of MR findings and clinical trial evidence, we identified 200 pairs corresponding to 33 unique genes that encode drug targets under clinical investigation. These results demonstrate how combining genetic, transcriptomic and clinical trial evidence can reduce pleiotropic bias, and prioritize immune-related drug targets for cancer prevention.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41216857 · DOI · PMC12697907
Early diagnostic biomarkers for acute myocardial infarction unveiled by metabolomics, Mendelian randomization, and machine learning [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Acute myocardial infarction (AMI) remains a leading cause of global cardiovascular morbidity and mortality.
- Closing excerpt
- In conclusion, this sequential multi-omics investigation successfully identifies and validates 10 gene biomarkers related to AMI, offering new perspectives for early precision diagnosis and insights into the disease's pathogenesis, alongside potential therapeutic targets.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (205 words)
Acute myocardial infarction (AMI) remains a leading cause of global cardiovascular morbidity and mortality. Limitations in current diagnostic methods hinder early detection and intervention, creating an urgent need for novel early diagnostic biomarkers. This study employed an integrated multi-omics approach, combining metabolomics, Mendelian randomization (MR), and transcriptomics data to identify potential AMI biomarkers. Plasma metabolomic profiling revealed 174 differentially abundant metabolites. Subsequent MR analysis pinpointed a key causal metabolite, L-arachidoyl carnitine (carnitine C20:0). Genes associated with this metabolite were retrieved from the GeneCards database and cross-referenced with differentially expressed genes from the GEO database, leading to the identification of 10 candidate biomarker genes: ACSL1, PYGL, DYSF, MGAM, SLC7A7, SULF2, KCNJ2, CYP1B1, NCF2, and SLC22A4. By constructing and evaluating 80 machine learning models, the Enet[alpha = 0.1] model was determined to have the optimal diagnostic performance. The diagnostic potential of these ten genes was further corroborated by logistic regression with tenfold cross-validation. Additionally, immune cell infiltration analysis using the CIBERSORT algorithm uncovered potential associations between the candidate genes and specific immune cell subpopulations. In conclusion, this sequential multi-omics investigation successfully identifies and validates 10 gene biomarkers related to AMI, offering new perspectives for early precision diagnosis and insights into the disease's pathogenesis, alongside potential therapeutic targets.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41526548 · DOI · PMC12796025
Disentangling the inverse relationship between cancer and Alzheimer's or Parkinson's disease: A systematic review on Mendelian randomization studies [3]
Abstract reading summary — source excerpts
- Opening excerpt
- IntroductionAlthough studies have reported an inverse relationship between cancer and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), findings remain inconsistent.
- Closing excerpt
- However, further research is required to confirm causality.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (225 words)
IntroductionAlthough studies have reported an inverse relationship between cancer and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), findings remain inconsistent. Observational studies are limited by survival bias and reverse causation. To better understand the relationship, we conducted a systematic review of Mendelian randomization (MR) studies examining both directions-assessing cancer as a risk factor for AD or PD, as well as AD or PD as exposures influencing cancer risk.MethodsWe systematically reviewed MR studies investigating the causal relation between cancer and either AD or PD. Cancer could be specified as either an exposure or an outcome of interest. Articles published until August 2024 were identified, screened, and abstracted by two reviewers following the "Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)" guidelines.ResultsTwelve studies met the inclusion criteria, comprising data from approximately 10,000 individuals and examining over 20 cancer types in relation to AD and PD risk. Of these, seven studies focused on AD, three on PD, and two examined both. Among nine studies on AD, an inverse association between several cancers and AD was reported, especially with breast cancer (overall and estrogen receptor-positive), with reduced odds (OR ConclusionThese results highlight inverse associations between AD and breast cancers, potentially implicating hormonal signaling pathways. Despite variations in methods and GWAS datasets, consistent protective trends were observed. However, further research is required to confirm causality.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41241298 · DOI · PMC12766696
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Mendelian randomization: genetic anchors for causal inference in epidemiological studies [4]
- Research article Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement [5]
- Research article Avoiding bias from weak instruments in Mendelian randomization studies [6]
- Research article Mendelian Randomization [7]
- Research article Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study [8]
- Research article A review of instrumental variable estimators for Mendelian randomization [9]
References
- Zheng J, Yang Q, Liu H, Zhao H, Wang S, Liu Y, et al.. Integrating Single-Cell Transcriptome-Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune-Related Drug Targets for Cancers. . 2025. PMID 41216857 · DOI · PMC12697907
- Fan H, Fu X, Guo Q, Jia F, Wei XY, Liu J, et al.. Early diagnostic biomarkers for acute myocardial infarction unveiled by metabolomics, Mendelian randomization, and machine learning. . 2026. PMID 41526548 · DOI · PMC12796025
- Aung KZ, Zin SS, Wu X, Myint ZW, Karanth S, Estus S, et al.. Disentangling the inverse relationship between cancer and Alzheimer's or Parkinson's disease: A systematic review on Mendelian randomization studies. . 2025. PMID 41241298 · DOI · PMC12766696
- Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014. PMID 25064373 · DOI · PMC4170722
- Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al.. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021. PMID 34698778 · DOI
- Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011. PMID 21414999 · DOI
- Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization. JAMA. 2017. PMID 29164242 · DOI
- Voight BF, Peloso GM, Orho-Melander M, Frikke-Schmidt R, Barbalic M, Jensen MK, et al.. Plasma HDL cholesterol and risk of myocardial infarction: a mendelian randomisation study. Lancet. 2012. PMID 22607825 · DOI · PMC3419820
- Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017. PMID 26282889 · DOI · PMC5642006
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-29.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.