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Research method

Genome-Wide Association Study (GWAS)

7 studies

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A genome-wide association study tests hundreds of thousands to millions of common polymorphisms for a statistical link to a trait, without nominating a gene in advance. The output is a set of loci — and, increasingly, polygenic scores built from those loci — whose p-values and effect sizes describe association in the sample that was genotyped. Association is not a demonstration that the typed SNP is causal, and prediction accuracy is not a constant of a trait: it moves with how the sample was ascertained.

Researchers reach for GWAS when a trait looks heritable and they want a map of common-variant signal, or a score that predicts the trait in a held-out group. It answers 'which common markers track this phenotype in this population?' Its main limitation is that a genome-wide hit can be a tag for something else — rare variants, population structure, or a gene not yet tested — and a polygenic score trained in one slice of a biobank can fail in another slice with the same continental ancestry label.

Studies

7

Findings

4

7 supporting · 0 challenging · 0 qualifying citations

Open tensions

2

Currently

What we know

  1. 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.
  2. Crop and livestock GWAS in this set return many significant markers, LD that stretches for hundreds of kilobases, and networks rather than single genes. A soybean protein/oil scan used 31,954 QC SNPs covering about 86% of the genome, with euchromatic LD (r²) falling to 0.2 by about 360 kbp and protein ranging roughly 35–50% across accessions; a later 809-accession soybean scan tied GWAS loci into agronomic networks; a Holstein scan of ~46k SNPs across 31 PTA traits found thousands of genome-wide significant additive effects and pleiotropic SNPs.
  3. 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.
  4. 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.

Largest unresolved question

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.

Procedure

How it works

The main stages of the method. Specific protocols and analysis choices vary between studies.

  1. Genotype common polymorphisms

    Hundreds of thousands to millions of SNPs are typed, without nominating a gene in advance. Rare variants, structural alleles, and untyped causal mutations are seen only if a typed marker tags them.

  2. Phenotype the sample

    The trait — height, education, soybean protein, a transmitting ability, liver expression — is measured in the people or lines that were genotyped. Hits inherit that phenotype's quality and how the sample was ascertained.

  3. Test association genome-wide

    Each marker is tested for a statistical link to the trait, with a multiple-testing threshold. A genome-wide significant SNP is a marker on a haplotype, not a demonstration that the typed base is causal.

  4. Report loci or build a polygenic score

    Papers map breeding-relevant loci, train a score that predicts a held-out group, or ask why a common-SNP hit might be synthetic. Prediction accuracy is not a constant of a trait; it moves with ascertainment even inside one ancestry label.

  5. Do not treat a hit as the causal mutation

    GWAS answers which common markers track this phenotype in this population. LD can stretch for hundreds of kilobases, an eQTL is not a clinical endpoint, and a score that works in one biobank slice can fail in another with the same continental ancestry label.

Common misconceptions

  • A genome-wide significant SNP is the causal mutation.

    It is a marker that tags a haplotype. LD in soybean euchromatin stretched to ~360 kbp at r² = 0.2, and rare-variant clustering can create a synthetic common-SNP signal. Functional follow-up is still required.

  • If a polygenic score works in people of a given ancestry, it works for everyone with that ancestry label.

    UK Biobank PGS accuracy varied across strata of similar genetic ancestry because of ascertainment and SES-related structure, not because the ancestry label changed.

  • Finding an eQTL for a disease-related gene proves the SNP causes the disease.

    Liver eQTL and LCL methylation papers map genetic influence on expression or methylation. They do not, by themselves, prove each associated variant causes a clinical endpoint.

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