Microbiome
Mendelian randomisation associates specific gut taxa with several cancers
Metadata + PaperFren explanation · cc by · source: Europe PMC
Genetic instruments for gut microbiota taxa showed causal associations with breast, lung, colorectal, prostate, gastric, and head/neck cancers—sometimes in opposing directions for the same genus.
Study at a glance
- Design
- Mendelian randomisation — Two-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets
- N
- 211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary N
- Population
- Gut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia)
- Outcome
- Causal microbiome–cancer associations (stringent and sensitivity analyses)
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Eleven stringent microbiome-to-cancer causal relationships emerged, including Actinobacteria/Bifidobacteriales with higher breast cancer odds, Tyzzerella3 with higher lung adenocarcinoma but lower colorectal cancer risk, Ruminococcustorques group with lower prostate cancer risk, and Peptostreptococcaceae with higher gastric cancer risk. Reverse MR supported bidirectional Tyzzerella3–lung adenocarcinoma effects. Additional associations appeared at relaxed thresholds across datasets.
Methodology
Authors harmonized MiBioGen gut microbiome GWAS instruments (211 taxa) with cancer GWAS summary statistics for eight malignancies from IEU Open GWAS and consortia. They ran two-sample MR (IVW, MR-Egger, weighted median, MR-PRESSO) with Bonferroni thresholds, sensitivity analyses, and reverse MR to test directionality.
Limitations
MR assumes valid genetic instruments and European-ancestry GWAS limits generalizability; 16S-based taxa lack strain resolution, and mechanistic pathways from taxa to cancer remain unproven experimentally.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Genetic instruments for gut taxa link a small set of microbiome–cancer associations under Mendelian randomisation.
Using Mendelian randomisation on gut-taxa genetic instruments, eleven stringent microbiome-to-cancer associations were reported (for example higher breast-cancer odds with Actinobacteria/Bifidobacteriales signals; Tyzzerella3 linked with higher lung adenocarcinoma but lower colorectal cancer risk).
Evidence for the claim as stated.
Before type 1 diabetes autoimmunity, Bacteroides dorei can dominate children’s gut communities.
In children who later developed type 1 diabetes autoimmunity, Bacteroides dorei became dominant in the gut community before persistent autoantibodies (mean diagnosis age about 16.8 months).
Scope note — different disease and method — cancer MR in adults, not pediatric T1D timing
Limits the claim's scope: a different population, assay, or outcome.
In middle-aged men, gut composition associates with metabolites and metabolic-syndrome traits.
In middle-aged men, gut microbiota composition was associated with plasma metabolites and metabolic-syndrome–related traits, placing the microbiome alongside genetics and lifestyle as a correlated host factor.
Scope note — different outcome — cancer odds via MR, not metabolic metabolites
Limits the claim's scope: a different population, assay, or outcome.
Cancer MR, pediatric T1D timing, and adult metabolic-trait associations all involve gut microbiota, but they answer different disease and design questions. Treating them as one interchangeable “gut microbiome disease effect” collapses distinct estimands.
Evidence for the claim as stated.
Human observational and MR studies do not automatically generalise to bee antibiotic/pesticide disruptions or to enzyme-focused colonization experiments. Animal and insect systems test different host–microbe questions; they limit how far a human association travels rather than falsifying it.
Evidence for the claim as stated.
Polygenic score accuracy can differ a lot among groups that share a broad ancestry label.
Polygenic score accuracy can differ substantially among groups that share a broad ancestry label; performance tracks ascertainment and SES-related structure, not ancestry labels alone.
Scope note — different question — MR disease associations, not PGS prediction accuracy
Limits the claim's scope: a different population, assay, or outcome.
Liver eQTL maps link common variants to expression of disease-relevant metabolic genes.
Human liver eQTL mapping links common variants to expression of disease-relevant metabolic pathway genes, showing one route from GWAS-scale variation to molecular intermediates.
Scope note — different endpoint — cancer odds via MR, not liver expression QTLs
Limits the claim's scope: a different population, assay, or outcome.
Mendelian randomisation on gut taxa reports a limited set of microbiome–cancer links.
Using genetic instruments for gut microbiota taxa, Mendelian randomisation reported eleven stringent microbiome-to-cancer associations (directions sometimes opposing across cancers for related taxa)—an MR application that depends on GWAS-quality instruments and their assumptions.
Evidence for the claim as stated.
Human PGS accuracy, liver eQTLs, rare-variant synthetic-signal theory, livestock/plant GWAS, and microbiome–cancer MR all live under a GWAS umbrella, but they answer different estimands. Pooling them as one “GWAS finding” erases species, trait, and method limits.
Evidence for the claim as stated.
A genome-wide significant hit is not the same claim as a portable polygenic score or an MR causal estimate. Synthetic-signal work limits naive readings of peaks; PGS work limits naive portability; MR adds instrument assumptions on top of association.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
Cancer MR, pediatric T1D timing, and adult metabolic-trait associations all involve gut microbiota, but they answer different disease and design questions. Treating them as one interchangeable “gut microbiome disease effect” collapses distinct estimands.
Human observational and MR studies do not automatically generalise to bee antibiotic/pesticide disruptions or to enzyme-focused colonization experiments. Animal and insect systems test different host–microbe questions; they limit how far a human association travels rather than falsifying it.
- Supports · Antibiotics hurt bee gut and survival
- Supports · Pesticides reshape honey bee gut microbes
- Supports · Selective bacterial BSH shifts host metabolism
Human PGS accuracy, liver eQTLs, rare-variant synthetic-signal theory, livestock/plant GWAS, and microbiome–cancer MR all live under a GWAS umbrella, but they answer different estimands. Pooling them as one “GWAS finding” erases species, trait, and method limits.
- Supports · Do polygenic scores work equally within one ancestry?
- Supports · Genetics of gene expression in human liver
- Supports · Can rare variants fake common GWAS hits?
- Supports · Holstein GWAS across 31 dairy traits
A genome-wide significant hit is not the same claim as a portable polygenic score or an MR causal estimate. Synthetic-signal work limits naive readings of peaks; PGS work limits naive portability; MR adds instrument assumptions on top of association.
History
When this study was placed
Dated entries from the concept change log — when this paper was added or removed as support, challenge, or qualifier on a claim.
Placed as a scope qualifier on GWAS
Human liver eQTL mapping links common variants to expression of disease-relevant metabolic pathway genes, showing one route from GWAS-scale variation to molecular intermediates.
Placed as a scope qualifier on GWAS
Polygenic score accuracy can differ substantially among groups that share a broad ancestry label; performance tracks ascertainment and SES-related structure, not ancestry labels alone.
Placed as supporting evidence on GWAS
Using genetic instruments for gut microbiota taxa, Mendelian randomisation reported eleven stringent microbiome-to-cancer associations (directions sometimes opposing across cancers for related taxa)—an MR application that depends on GWAS-quality instruments and their assumptions.
Placed as supporting evidence on GWAS
Human PGS accuracy, liver eQTLs, rare-variant synthetic-signal theory, livestock/plant GWAS, and microbiome–cancer MR all live under a GWAS umbrella, but they answer different estimands. Pooling them as one “GWAS finding” erases species, trait, and method limits.
Placed as supporting evidence on GWAS
A genome-wide significant hit is not the same claim as a portable polygenic score or an MR causal estimate. Synthetic-signal work limits naive readings of peaks; PGS work limits naive portability; MR adds instrument assumptions on top of association.
Placed as supporting evidence on Microbiome
Using Mendelian randomisation on gut-taxa genetic instruments, eleven stringent microbiome-to-cancer associations were reported (for example higher breast-cancer odds with Actinobacteria/Bifidobacteriales signals; Tyzzerella3 linked with higher lung adenocarcinoma but lower colorectal cancer risk).
Placed as a scope qualifier on Microbiome
In children who later developed type 1 diabetes autoimmunity, Bacteroides dorei became dominant in the gut community before persistent autoantibodies (mean diagnosis age about 16.8 months).
Placed as a scope qualifier on Microbiome
In middle-aged men, gut microbiota composition was associated with plasma metabolites and metabolic-syndrome–related traits, placing the microbiome alongside genetics and lifestyle as a correlated host factor.
Placed as supporting evidence on Microbiome
Cancer MR, pediatric T1D timing, and adult metabolic-trait associations all involve gut microbiota, but they answer different disease and design questions. Treating them as one interchangeable “gut microbiome disease effect” collapses distinct estimands.
Placed as supporting evidence on Microbiome
Human observational and MR studies do not automatically generalise to bee antibiotic/pesticide disruptions or to enzyme-focused colonization experiments. Animal and insect systems test different host–microbe questions; they limit how far a human association travels rather than falsifying it.
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Same topic cluster — not a recommendation engine.