Optimizing sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations

Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species’ genetic diversity across the landscape or even over its whole range. T...

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Bibliographic Details
Published in:Molecular ecology resources 2022-01, Vol.22 (1), p.137-152
Main Authors: Paril, Jefferson F., Balding, David J., Fournier‐Level, Alexandre
Format: Article
Language:English
Subjects:
Design optimization
Ecology
Gene mapping
Genetic diversity
Genetic effects
genome‐wide association
Genomic analysis
genomic prediction
Genomics
individual sequencing
landscape genomics
Natural populations
Outbreeding
pool sequencing
Population genetics
Populations
Quantitative trait loci
Research Design
Sampling
Sampling designs
simulation
Species
Species diversity
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Summary:Mapping the genes underlying ecologically relevant traits in natural populations is fundamental to develop a molecular understanding of species adaptation. Current sequencing technologies enable the characterization of a species’ genetic diversity across the landscape or even over its whole range. The relevant capture of the genetic diversity across the landscape is critical for a successful genetic mapping of traits and there are no clear guidelines on how to achieve an optimal sampling and which sequencing strategy to implement. Here we determine, through simulation, the sampling scheme that maximizes the power to map the genetic basis of a complex trait in an outbreeding species across an idealized landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that quantitative trait locus detection power and prediction accuracy are higher when more populations over the landscape are sampled and this is more cost‐effectively done with pool sequencing than with individual sequencing. Additionally, we recommend sampling populations from areas of high genetic diversity. As progress in sequencing enables the integration of trait‐based functional ecology into landscape genomics studies, these findings will guide study designs allowing direct measures of genetic effects in natural populations across the environment.
ISSN:1755-098X
1755-0998
DOI:10.1111/1755-0998.13458