GWAS
Can rare variants fake common GWAS hits?
Open access · cc by · source: Europe PMC
Collections of rare causal variants can create synthetic genome-wide association signals at common SNPs.
Study at a glance
- Design
- Computational / modelling — Genealogical simulations of rare causal variants generating synthetic common-SNP GWAS signals
- N
- Simulation study (~30% of runs detect genome-wide associations) — no empirical sample N
- Population
- Simulated genealogies with rare disease alleles
- Outcome
- Synthetic genome-wide associations created by rare causal variants
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Even when individual rare alleles are uncommon (e.g., 0.005–0.02), as a disease class grows they can collectively create synthetic GWAS signals unless variants are extremely numerous and evenly spread.
Methodology
Modeled how rare disease-causing variants distributed in genealogies generate association signals detectable at common polymorphisms.
Limitations
Not every GWAS hit is synthetic; it shows a plausible generative mechanism for some signals.
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.
Scope note — different question — how rare variants can create synthetic common signals
Limits the claim's scope: a different population, assay, or outcome.
Rare variants can create synthetic GWAS signals at common markers when clustered.
Rare variants can collectively create synthetic genome-wide association signals at common markers when they are not extremely numerous and evenly spread—so a GWAS peak is not automatically a single common causal allele.
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.
Rare variants can generate a synthetic association at a common SNP. Modelling showed that even when individual rare disease-causing alleles are uncommon (for example 0.005–0.02), they can collectively create a GWAS signal at a common polymorphism unless the rare variants are extremely numerous and evenly spread. That is a generative mechanism for some hits, not a claim that every GWAS hit is synthetic.
Evidence for the claim as stated.
What GWAS is for splits across these papers: predicting a trait with a score, mapping breeding-relevant loci, or explaining why a common-SNP hit might not be the causal allele. The UK Biobank PGS paper is about within-ancestry transport of scores; the soybean and Holstein papers are about loci and networks for agronomy; the rare-variant paper is a caution about interpreting the hit itself. A student who treats 'GWAS' as one deliverable will mash those aims together.
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.
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.
What GWAS is for splits across these papers: predicting a trait with a score, mapping breeding-relevant loci, or explaining why a common-SNP hit might not be the causal allele. The UK Biobank PGS paper is about within-ancestry transport of scores; the soybean and Holstein papers are about loci and networks for agronomy; the rare-variant paper is a caution about interpreting the hit itself. A student who treats 'GWAS' as one deliverable will mash those aims together.
- Supports · Do polygenic scores work equally within one ancestry?
- Supports · Soybean GWAS networks for agronomy
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.
Removed as supporting evidence 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
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 supporting evidence on GWAS
Rare variants can collectively create synthetic genome-wide association signals at common markers when they are not extremely numerous and evenly spread—so a GWAS peak is not automatically a single common causal allele.
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.
Related papers in this topic
Same topic cluster — not a recommendation engine.