loading page

Optimising sampling design and sequencing strategy for the genomic analysis of quantitative traits in natural populations
  • Jefferson Paril,
  • David Balding,
  • Alexandre Fournier-Level
Jefferson Paril
The University of Melbourne

Corresponding Author:[email protected]

Author Profile
David Balding
The University of Melbourne
Author Profile
Alexandre Fournier-Level
The University of Melbourne
Author Profile


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 characterisation 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 maximises the power to map the genetic basis of a complex trait in an outbreeding species across an idealised landscape and draw genomic predictions for the trait, comparing individual and pool sequencing strategies. Our results show that QTL 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.
25 Feb 2021Submitted to Molecular Ecology Resources
25 Feb 2021Reviewer(s) Assigned
13 Mar 2021Review(s) Completed, Editorial Evaluation Pending
15 Apr 2021Editorial Decision: Revise Minor
03 May 2021Review(s) Completed, Editorial Evaluation Pending
03 May 20211st Revision Received
25 Jun 2021Editorial Decision: Accept