Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast.
S. cerevisiae
epistasis
evolutionary biology
genetics
genomics
pleiotropy
polygenic traits
quantitative trait loci
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
11 02 2022
11 02 2022
Historique:
received:
16
09
2021
accepted:
11
02
2022
pubmed:
12
2
2022
medline:
7
4
2022
entrez:
11
2
2022
Statut:
epublish
Résumé
Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.
Identifiants
pubmed: 35147078
doi: 10.7554/eLife.73983
pii: 73983
pmc: PMC8979589
doi:
pii:
Banques de données
Dryad
['10.5061/dryad.1rn8pk0vd']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIGMS NIH HHS
ID : R01 GM104239
Pays : United States
Organisme : NCI NIH HHS
ID : U54 CA193461
Pays : United States
Informations de copyright
© 2022, Nguyen Ba et al.
Déclaration de conflit d'intérêts
AN, KL, AR, SG, DT, FM, MD No competing interests declared
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