GWAS
Genetics of gene expression in human liver
Open access · cc by · source: Europe PMC
A 427-person liver cohort maps eQTLs and expression networks that connect genetic variation to metabolic disease biology.
Study at a glance
- Design
- Cross-sectional — Liver expression and genotype profiling to map hepatic eQTLs and coexpression networks
- N
- N=427 · Human liver cohort of 427 Caucasian subjects
- Population
- Human liver tissue donors
- Outcome
- Genetic architecture of hepatic gene expression (eQTLs/networks)
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Liver eQTLs and coexpression networks reveal how common variants influence expression of disease-relevant metabolic pathways.
Methodology
Profiled gene expression and genotypes in hundreds of human liver samples to map genetic architecture of hepatic expression.
Limitations
Association of expression QTLs is not alone causal proof for every disease endpoint.
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.
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.
Evidence for the claim as stated.
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.
Scope note — different intermediate — liver eQTLs, not gut-taxa instruments
Limits the claim's scope: a different population, assay, or outcome.
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.
Expression and methylation can be treated as GWAS-style traits (eQTLs, mQTLs). Liver expression–genotype maps linked common variants to metabolic pathways; in 77 Yoruba LCLs, methylation variation tracked genetics and RNA-seq expression. Those maps are still associations, not proof that each variant causes a disease endpoint.
Evidence for the claim as stated.
Phenotype quality differs. Holstein analyses use processed predicted transmitting abilities, not raw farm records; soybean protein/oil was measured in limited field environments; liver eQTLs are expression, not disease. Hits inherit those phenotype choices.
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.
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.
Phenotype quality differs. Holstein analyses use processed predicted transmitting abilities, not raw farm records; soybean protein/oil was measured in limited field environments; liver eQTLs are expression, not disease. Hits inherit those phenotype choices.
- Supports · Holstein GWAS across 31 dairy traits
- Supports · Soybean GWAS for protein and oil
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
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.
Related papers in this topic
Same topic cluster — not a recommendation engine.