Concept
GWAS
A genome-wide association study (GWAS) scans many genetic variants across the genome for statistical association with a trait. Related tools in this library include polygenic scores built from GWAS weights and Mendelian randomisation that uses genetic instruments (often from GWAS) to probe putative causal links under explicit assumptions.
GWAS hits, polygenic scores, and MR estimates are easy to collapse into “genes cause the trait.” They answer different questions—discovery of associations, prediction accuracy, and instrument-based causal inference—with different failure modes.
Evidence
What the evidence shows
Drawn from 7 studies in this library. Each finding starts with a plain-language takeaway, then the denser detail. Supports means evidence for a finding; Challenges means evidence against a stated position; Qualifies marks scope with a short note on each study’s contribution. Challenged positions are labeled — they are not findings.
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.
- Holstein GWAS across 31 dairy traits— different species and trait system — dairy cattle GWAS, not human PGS accuracy
- Mendelian randomisation associates specific gut taxa with several cancers— different question — MR disease associations, not PGS prediction accuracy
Study Role Design N Population Outcome Do polygenic scores work equally within one ancestry? Supports Computational / modellingWithin-ancestry stratification of PGS accuracy in UK Biobank (education, height, BMI, etc.) N=408434 · 408,434 UK Biobank participants passing QC; analyses often within White British strata UK Biobank participants of broadly similar genetic ancestry Within-ancestry variation in polygenic score prediction accuracy Holstein GWAS across 31 dairy traits Qualifiesdifferent species and trait system — dairy cattle GWAS, not human PGS accuracy Computational / modellingGWAS of ~46k SNPs against PTA traits in contemporary US Holsteins N=1654 · 1,654 Holstein cows; 45,878 genotyped SNPs Contemporary US Holstein cattle with predicted transmitting abilities Additive SNP associations with production, health, and reproduction traits Mendelian randomisation associates specific gut taxa with several cancers Qualifiesdifferent question — MR disease associations, not PGS prediction accuracy Mendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets 211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary N Gut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia) Causal microbiome–cancer associations (stringent and sensitivity analyses) 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.
- Can rare variants fake common GWAS hits?— different question — how rare variants can create synthetic common signals
- Mendelian randomisation associates specific gut taxa with several cancers— different endpoint — cancer odds via MR, not liver expression QTLs
Study Role Design N Population Outcome Genetics of gene expression in human liver Supports Cross-sectionalLiver expression and genotype profiling to map hepatic eQTLs and coexpression networks N=427 · Human liver cohort of 427 Caucasian subjects Human liver tissue donors Genetic architecture of hepatic gene expression (eQTLs/networks) Can rare variants fake common GWAS hits? Qualifiesdifferent question — how rare variants can create synthetic common signals Computational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signals Simulation study (~30% of runs detect genome-wide associations) — no empirical sample N Simulated genealogies with rare disease alleles Synthetic genome-wide associations created by rare causal variants Mendelian randomisation associates specific gut taxa with several cancers Qualifiesdifferent endpoint — cancer odds via MR, not liver expression QTLs Mendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets 211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary N Gut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia) Causal microbiome–cancer associations (stringent and sensitivity analyses) 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.
- Holstein GWAS across 31 dairy traits— empirical cattle GWAS peaks — not a synthetic-signal simulation
- Do polygenic scores work equally within one ancestry?— different question — PGS transportability, not signal synthesis
Study Role Design N Population Outcome Can rare variants fake common GWAS hits? Supports Computational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signals Simulation study (~30% of runs detect genome-wide associations) — no empirical sample N Simulated genealogies with rare disease alleles Synthetic genome-wide associations created by rare causal variants Holstein GWAS across 31 dairy traits Qualifiesempirical cattle GWAS peaks — not a synthetic-signal simulation Computational / modellingGWAS of ~46k SNPs against PTA traits in contemporary US Holsteins N=1654 · 1,654 Holstein cows; 45,878 genotyped SNPs Contemporary US Holstein cattle with predicted transmitting abilities Additive SNP associations with production, health, and reproduction traits Do polygenic scores work equally within one ancestry? Qualifiesdifferent question — PGS transportability, not signal synthesis Computational / modellingWithin-ancestry stratification of PGS accuracy in UK Biobank (education, height, BMI, etc.) N=408434 · 408,434 UK Biobank participants passing QC; analyses often within White British strata UK Biobank participants of broadly similar genetic ancestry Within-ancestry variation in polygenic score prediction accuracy Large dairy GWAS recover many additive SNP effects, some in genes and some elsewhere.
Large GWAS of Holstein dairy traits recovered thousands of genome-wide significant additive effects, with many top SNPs in genes and some SNPs associated with multiple traits (pleiotropy).
- Soybean GWAS networks for agronomy— different species — soybean agronomic networks, not dairy PTA traits
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.
- Do polygenic scores work equally within one ancestry?— different question — PGS accuracy within ancestry, not MR cancer odds
- Genetics of gene expression in human liver— different intermediate — liver eQTLs, not gut-taxa instruments
Study Role Design N Population Outcome Mendelian randomisation associates specific gut taxa with several cancers Supports Mendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets 211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary N Gut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia) Causal microbiome–cancer associations (stringent and sensitivity analyses) Do polygenic scores work equally within one ancestry? Qualifiesdifferent question — PGS accuracy within ancestry, not MR cancer odds Computational / modellingWithin-ancestry stratification of PGS accuracy in UK Biobank (education, height, BMI, etc.) N=408434 · 408,434 UK Biobank participants passing QC; analyses often within White British strata UK Biobank participants of broadly similar genetic ancestry Within-ancestry variation in polygenic score prediction accuracy Genetics of gene expression in human liver Qualifiesdifferent intermediate — liver eQTLs, not gut-taxa instruments Cross-sectionalLiver expression and genotype profiling to map hepatic eQTLs and coexpression networks N=427 · Human liver cohort of 427 Caucasian subjects Human liver tissue donors Genetic architecture of hepatic gene expression (eQTLs/networks)
Open questions
Tensions and limits
Some items are genuine disagreements on the same question. Others mark different assays, populations, or outcomes — limits on how far one study travels — not a forced fight between papers.
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.
- Do polygenic scores work equally within one ancestry?
- Genetics of gene expression in human liver
- Can rare variants fake common GWAS hits?
- Holstein GWAS across 31 dairy traits
- Mendelian randomisation associates specific gut taxa with several cancers
Study Role Design N Population Outcome Do polygenic scores work equally within one ancestry? Supports Computational / modellingWithin-ancestry stratification of PGS accuracy in UK Biobank (education, height, BMI, etc.) N=408434 · 408,434 UK Biobank participants passing QC; analyses often within White British strata UK Biobank participants of broadly similar genetic ancestry Within-ancestry variation in polygenic score prediction accuracy Genetics of gene expression in human liver Supports Cross-sectionalLiver expression and genotype profiling to map hepatic eQTLs and coexpression networks N=427 · Human liver cohort of 427 Caucasian subjects Human liver tissue donors Genetic architecture of hepatic gene expression (eQTLs/networks) Can rare variants fake common GWAS hits? Supports Computational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signals Simulation study (~30% of runs detect genome-wide associations) — no empirical sample N Simulated genealogies with rare disease alleles Synthetic genome-wide associations created by rare causal variants Holstein GWAS across 31 dairy traits Supports Computational / modellingGWAS of ~46k SNPs against PTA traits in contemporary US Holsteins N=1654 · 1,654 Holstein cows; 45,878 genotyped SNPs Contemporary US Holstein cattle with predicted transmitting abilities Additive SNP associations with production, health, and reproduction traits Mendelian randomisation associates specific gut taxa with several cancers Supports Mendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets 211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary N Gut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia) Causal microbiome–cancer associations (stringent and sensitivity analyses) 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.
- Can rare variants fake common GWAS hits?
- Do polygenic scores work equally within one ancestry?
- Mendelian randomisation associates specific gut taxa with several cancers
Study Role Design N Population Outcome Can rare variants fake common GWAS hits? Supports Computational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signals Simulation study (~30% of runs detect genome-wide associations) — no empirical sample N Simulated genealogies with rare disease alleles Synthetic genome-wide associations created by rare causal variants Do polygenic scores work equally within one ancestry? Supports Computational / modellingWithin-ancestry stratification of PGS accuracy in UK Biobank (education, height, BMI, etc.) N=408434 · 408,434 UK Biobank participants passing QC; analyses often within White British strata UK Biobank participants of broadly similar genetic ancestry Within-ancestry variation in polygenic score prediction accuracy Mendelian randomisation associates specific gut taxa with several cancers Supports Mendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets 211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary N Gut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia) Causal microbiome–cancer associations (stringent and sensitivity analyses)
Common misconceptions
If two groups share a continental ancestry label, their polygenic scores should predict equally well.
Within-ancestry PGS accuracy can still differ sharply with ascertainment and SES-related structure; ancestry labels alone do not guarantee equal performance.
Every genome-wide significant SNP marks one common causal variant for the trait.
Rare variants can create synthetic association at common markers under plausible allele-frequency configurations; functional follow-up is still required.
Mendelian randomisation using GWAS instruments proves the biological mechanism from exposure to disease.
MR supports a directional association under instrument assumptions. The microbiome–cancer MR paper notes unproven experimental pathways, ancestry limits, and coarse taxon resolution.
Exam-style questions
Short-answer questions that ask you to explain or compare, not recall.
A classmate treats a dairy GWAS hit and a human PGS accuracy result as the same kind of evidence. How do you separate them?
The cattle paper reports genome-wide significant associations for breeding traits (often on processed PTAs). The PGS paper asks how well a score predicts within human subgroups that share an ancestry label. Different species, estimands, and uses—scope, not interchangeable ‘GWAS proof’.
How can rare variants qualify a confident reading of a common GWAS peak without erasing all GWAS findings?
They show a generative way some peaks can arise without a single common causal allele. That limits overinterpretation of peaks; it does not say every hit is synthetic or that association studies are useless.
What would most strengthen an MR claim that a gut taxon increases a specific cancer’s risk?
Stronger instruments and sensitivity analyses, strain-resolved exposures, experimental pathway evidence, and replication beyond the original ancestry GWAS—while keeping the cancer endpoint specific.
The studies
6 studies in this library bear on GWAS, ordered by citations.
- Genetics of gene expression in human liver
A 427-person liver cohort maps eQTLs and expression networks that connect genetic variation to metabolic disease biology.
- Can rare variants fake common GWAS hits?
Collections of rare causal variants can create synthetic genome-wide association signals at common SNPs.
- Do polygenic scores work equally within one ancestry?
Even within a relatively homogeneous ancestry group, PGS prediction accuracy differs substantially across socio-economic and related strata.
- Mendelian randomisation associates specific gut taxa with several cancers
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.
- Holstein GWAS across 31 dairy traits
Genome-wide SNP association in U.S. Holsteins links many loci to production, health, fertility, and type traits.
- Soybean GWAS networks for agronomy
Large-scale soybean resequencing GWAS maps genetic networks underlying major agronomic traits.
Learn alongside
Change log
What changed
Dated edits to this page's evidence: studies added or removed from a claim, claims added or withdrawn, and new explanations tagged here. Rewordings are not listed.
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.
- Removed a supporting study: Can rare variants fake common GWAS hits?
- Removed a supporting study: Do polygenic scores work equally within one ancestry?
- Added a scope qualifier: Mendelian randomisation associates specific gut taxa with several cancers
- Added a scope qualifier: Can rare variants fake common GWAS hits?
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.
- New claim
- Added a supporting study: Do polygenic scores work equally within one ancestry?
- Added a scope qualifier: Holstein GWAS across 31 dairy traits
- Added a scope qualifier: Mendelian randomisation associates specific gut taxa with several cancers
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.
- New claim
- Added a supporting study: Can rare variants fake common GWAS hits?
- Added a scope qualifier: Holstein GWAS across 31 dairy traits
- Added a scope qualifier: Do polygenic scores work equally within one ancestry?
Large GWAS of Holstein dairy traits recovered thousands of genome-wide significant additive effects, with many top SNPs in genes and some SNPs associated with multiple traits (pleiotropy).
- New claim
- Added a supporting study: Holstein GWAS across 31 dairy traits
- Added a scope qualifier: a-genome-wide-association-study-of-seed-protein-and-oil-content-in-2015
- Added a scope qualifier: Soybean GWAS networks for agronomy
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.
- New claim
- Added a supporting study: Mendelian randomisation associates specific gut taxa with several cancers
- Added a scope qualifier: Genetics of gene expression in human liver
- Added a scope qualifier: Do polygenic scores work equally within one ancestry?
Major within-ancestry differences in PGS accuracy among groups with similar genetic ancestry; accuracy depends on ascertainment/SES-related structure, not ancestry labels alone.
- Claim withdrawn
Liver eQTLs and coexpression networks reveal how common variants influence expression of disease-relevant metabolic pathways.
- Claim withdrawn
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.
- Claim withdrawn
Thousands of genome-wide significant additive effects; many top SNPs fall in genes; some SNPs are pleiotropic.
- Claim withdrawn
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.
- Added a supporting study: Holstein GWAS across 31 dairy traits
- Added a supporting study: Mendelian randomisation associates specific gut taxa with several cancers
- Marked as a scope tension, not a disagreement
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.
- New tension
- Added a supporting study: Mendelian randomisation associates specific gut taxa with several cancers
- Added a supporting study: Can rare variants fake common GWAS hits?
- Added a supporting study: Do polygenic scores work equally within one ancestry?
If two groups share a continental ancestry label, their polygenic scores should predict equally well.
- Added a misconception
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Every genome-wide significant SNP marks one common causal variant for the trait.
- Added a misconception
Mendelian randomisation using GWAS instruments proves the biological mechanism from exposure to disease.
- Added a misconception
GWAS findings from one model organism always transfer to humans.
- Removed a misconception
- Concept page published