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Concept · biology

GWAS

Follow GWAS — see important new research and changes in evidence.

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Dated edits to this page's evidence: studies added or removed from a claim, claims added or withdrawn, and new explanations tagged here.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.

Show 4 earlier

  • Sep 14, 2026

    Every genome-wide significant SNP marks one common causal variant for the trait.

    • Added a misconception

  • Sep 14, 2026

    Mendelian randomisation using GWAS instruments proves the biological mechanism from exposure to disease.

    • Added a misconception

  • Sep 14, 2026

    GWAS findings from one model organism always transfer to humans.

    • Removed a misconception

  • Sep 3, 2026

    • Concept page published

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.

    1 supporting · 2 qualifying

    Supports

    1. 1Do polygenic scores work equally within one ancestry?

    Qualifies

    1. 1Holstein GWAS across 31 dairy traits— different species and trait system — dairy cattle GWAS, not human PGS accuracy
    2. 2Mendelian randomisation associates specific gut taxa with several cancers— different question — MR disease associations, not PGS prediction accuracy

    Study comparison

    StudyRoleDesignNPopulationOutcome
    Do polygenic scores work equally within one ancestry?2020SupportsComputational / 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 strataUK Biobank participants of broadly similar genetic ancestryWithin-ancestry variation in polygenic score prediction accuracy
    Holstein GWAS across 31 dairy traits2011Qualifiesdifferent species and trait system — dairy cattle GWAS, not human PGS accuracyComputational / modellingGWAS of ~46k SNPs against PTA traits in contemporary US HolsteinsN=1654 · 1,654 Holstein cows; 45,878 genotyped SNPsContemporary US Holstein cattle with predicted transmitting abilitiesAdditive SNP associations with production, health, and reproduction traits
    Mendelian randomisation associates specific gut taxa with several cancers2023Qualifiesdifferent question — MR disease associations, not PGS prediction accuracyMendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary NGut 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.

    1 supporting · 2 qualifying

    Supports

    1. 1Genetics of gene expression in human liver

    Qualifies

    1. 1Can rare variants fake common GWAS hits?— different question — how rare variants can create synthetic common signals
    2. 2Mendelian randomisation associates specific gut taxa with several cancers— different endpoint — cancer odds via MR, not liver expression QTLs

    Study comparison

    StudyRoleDesignNPopulationOutcome
    Genetics of gene expression in human liver2008SupportsCross-sectionalLiver expression and genotype profiling to map hepatic eQTLs and coexpression networksN=427 · Human liver cohort of 427 Caucasian subjectsHuman liver tissue donorsGenetic architecture of hepatic gene expression (eQTLs/networks)
    Can rare variants fake common GWAS hits?2010Qualifiesdifferent question — how rare variants can create synthetic common signalsComputational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signalsSimulation study (~30% of runs detect genome-wide associations) — no empirical sample NSimulated genealogies with rare disease allelesSynthetic genome-wide associations created by rare causal variants
    Mendelian randomisation associates specific gut taxa with several cancers2023Qualifiesdifferent endpoint — cancer odds via MR, not liver expression QTLsMendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary NGut 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.

    1 supporting · 2 qualifying

    Supports

    1. 1Can rare variants fake common GWAS hits?

    Qualifies

    1. 1Holstein GWAS across 31 dairy traits— empirical cattle GWAS peaks — not a synthetic-signal simulation
    2. 2Do polygenic scores work equally within one ancestry?— different question — PGS transportability, not signal synthesis

    Study comparison

    StudyRoleDesignNPopulationOutcome
    Can rare variants fake common GWAS hits?2010SupportsComputational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signalsSimulation study (~30% of runs detect genome-wide associations) — no empirical sample NSimulated genealogies with rare disease allelesSynthetic genome-wide associations created by rare causal variants
    Holstein GWAS across 31 dairy traits2011Qualifiesempirical cattle GWAS peaks — not a synthetic-signal simulationComputational / modellingGWAS of ~46k SNPs against PTA traits in contemporary US HolsteinsN=1654 · 1,654 Holstein cows; 45,878 genotyped SNPsContemporary US Holstein cattle with predicted transmitting abilitiesAdditive SNP associations with production, health, and reproduction traits
    Do polygenic scores work equally within one ancestry?2020Qualifiesdifferent question — PGS transportability, not signal synthesisComputational / 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 strataUK Biobank participants of broadly similar genetic ancestryWithin-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).

    1 supporting · 2 qualifying

    Supports

    1. 1Holstein GWAS across 31 dairy traits

    Qualifies

    1. 1Soybean 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.

    1 supporting · 2 qualifying

    Supports

    1. 1Mendelian randomisation associates specific gut taxa with several cancers

    Qualifies

    1. 1Do polygenic scores work equally within one ancestry?— different question — PGS accuracy within ancestry, not MR cancer odds
    2. 2Genetics of gene expression in human liver— different intermediate — liver eQTLs, not gut-taxa instruments

    Study comparison

    StudyRoleDesignNPopulationOutcome
    Mendelian randomisation associates specific gut taxa with several cancers2023SupportsMendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary NGut 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?2020Qualifiesdifferent question — PGS accuracy within ancestry, not MR cancer oddsComputational / 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 strataUK Biobank participants of broadly similar genetic ancestryWithin-ancestry variation in polygenic score prediction accuracy
    Genetics of gene expression in human liver2008Qualifiesdifferent intermediate — liver eQTLs, not gut-taxa instrumentsCross-sectionalLiver expression and genotype profiling to map hepatic eQTLs and coexpression networksN=427 · Human liver cohort of 427 Caucasian subjectsHuman liver tissue donorsGenetic 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.

  • Scope / different questions

    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.

    5 studies
    1. 1Do polygenic scores work equally within one ancestry?
    2. 2Genetics of gene expression in human liver
    3. 3Can rare variants fake common GWAS hits?
    4. 4Holstein GWAS across 31 dairy traits
    5. 5Mendelian randomisation associates specific gut taxa with several cancers

    Study comparison

    StudyRoleDesignNPopulationOutcome
    Do polygenic scores work equally within one ancestry?2020SupportsComputational / 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 strataUK Biobank participants of broadly similar genetic ancestryWithin-ancestry variation in polygenic score prediction accuracy
    Genetics of gene expression in human liver2008SupportsCross-sectionalLiver expression and genotype profiling to map hepatic eQTLs and coexpression networksN=427 · Human liver cohort of 427 Caucasian subjectsHuman liver tissue donorsGenetic architecture of hepatic gene expression (eQTLs/networks)
    Can rare variants fake common GWAS hits?2010SupportsComputational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signalsSimulation study (~30% of runs detect genome-wide associations) — no empirical sample NSimulated genealogies with rare disease allelesSynthetic genome-wide associations created by rare causal variants
    Holstein GWAS across 31 dairy traits2011SupportsComputational / modellingGWAS of ~46k SNPs against PTA traits in contemporary US HolsteinsN=1654 · 1,654 Holstein cows; 45,878 genotyped SNPsContemporary US Holstein cattle with predicted transmitting abilitiesAdditive SNP associations with production, health, and reproduction traits
    Mendelian randomisation associates specific gut taxa with several cancers2023SupportsMendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary NGut microbiome and cancer GWAS summary statistics (IEU OpenGWAS/consortia)Causal microbiome–cancer associations (stringent and sensitivity analyses)

  • Scope / different questions

    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.

    3 studies
    1. 1Can rare variants fake common GWAS hits?
    2. 2Do polygenic scores work equally within one ancestry?
    3. 3Mendelian randomisation associates specific gut taxa with several cancers

    Study comparison

    StudyRoleDesignNPopulationOutcome
    Can rare variants fake common GWAS hits?2010SupportsComputational / modellingGenealogical simulations of rare causal variants generating synthetic common-SNP GWAS signalsSimulation study (~30% of runs detect genome-wide associations) — no empirical sample NSimulated genealogies with rare disease allelesSynthetic genome-wide associations created by rare causal variants
    Do polygenic scores work equally within one ancestry?2020SupportsComputational / 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 strataUK Biobank participants of broadly similar genetic ancestryWithin-ancestry variation in polygenic score prediction accuracy
    Mendelian randomisation associates specific gut taxa with several cancers2023SupportsMendelian randomisationTwo-sample MR of MiBioGen gut taxa instruments against eight cancer GWAS summary sets211 taxa instruments; cancer GWAS sample sizes vary by malignancy — no single primary NGut 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.

    1. 1Do polygenic scores work equally within one ancestry?

  • 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.

    1. 1Can rare variants fake common GWAS hits?

  • 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.

    1. 1Mendelian randomisation associates specific gut taxa with several cancers

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.

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