GWAS
Do polygenic scores work equally within one ancestry?
Open access · cc by · source: Europe PMC
Even within a relatively homogeneous ancestry group, PGS prediction accuracy differs substantially across socio-economic and related strata.
Study at a glance
- Design
- Computational / modelling — Within-ancestry stratification of PGS accuracy in UK Biobank (education, height, BMI, etc.)
- N
- N=408434 · 408,434 UK Biobank participants passing QC; analyses often within White British strata
- Population
- UK Biobank participants of broadly similar genetic ancestry
- Outcome
- Within-ancestry variation in polygenic score prediction accuracy
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
Major within-ancestry differences in PGS accuracy among groups with similar genetic ancestry; accuracy depends on ascertainment/SES-related structure, not ancestry labels alone.
Methodology
Evaluated PGS prediction accuracy across strata in UK Biobank individuals of similar ancestry, focusing on traits like education, height, and BMI.
Limitations
Does not claim PGS are clinically actionable for all traits.
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.
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.
Evidence for the claim as stated.
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.
Scope note — different question — PGS transportability, not signal synthesis
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.
Scope note — different question — PGS accuracy within ancestry, not MR cancer odds
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.
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.
Polygenic scores are not equally accurate even inside one ancestry label. In UK Biobank individuals of similar genetic ancestry, prediction accuracy for traits such as education, height and BMI differed across strata; accuracy tracked ascertainment and SES-related structure rather than ancestry labels alone.
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 · Soybean GWAS networks for agronomy
- Supports · Can rare variants fake common GWAS hits?
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 supporting evidence 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 a scope qualifier 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 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.
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.