GWAS
Soybean GWAS networks for agronomy
Open access · cc by · source: Europe PMC
Large-scale soybean resequencing GWAS maps genetic networks underlying major agronomic traits.
Study at a glance
- Design
- Computational / modelling — GWAS of diverse soybean landraces/cultivars phenotyped across locations and years
- N
- N=809 · 809 accessions; >10 million SNPs/indels after imputation
- Population
- Diverse Glycine max landraces and cultivars
- Outcome
- Genetic networks underlying agronomical traits including flowering and yield components
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
GWAS loci connect into networks explaining flowering, yield components, and related agronomy.
Methodology
Sequenced/phenotyped 809 diverse landraces and cultivars and associated millions of SNPs with traits.
Limitations
Association is not proof of every causal polymorphism.
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.
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).
Scope note — different species — soybean agronomic networks, not dairy PTA traits
Limits the claim's scope: a different population, assay, or outcome.
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.
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.
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.
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.
Placed as a scope qualifier on GWAS
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).
Related papers in this topic
Same topic cluster — not a recommendation engine.