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\begin{document}

\title{Optimising sampling design for the genomic analysis of quantitative
traits in natural populations}



\author[1]{Jefferson Paril}%
\author[1]{David Balding}%
\author[1]{Alexandre Fournier-Level}%
\affil[1]{The University of Melbourne}%


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  \date{May 24, 2024}


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\begin{abstract}
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. Here we determine through simulation, the sampling scheme that
maximises the power to map the genetic basis of a complex trait 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
performing a shallow sampling of more populations over the landscape
which is done best using pool sequencing. Populations should be
collected from areas of high genetic diversity and we recommend against
sampling from the margins of the species' range. 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.%
\end{abstract}%



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