Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics

Abstract

A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis‐based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

European wildcat populations are subdivided into five main biogeographic groups: consequences of Pleistocene climate changes or recent anthropogenic fragmentation?

“…Landscape genetic analyses may lead to describe patterns of gene flow across ecological corridors and eventually identify local adaptations (Joost et al. 2013). …”

A framework for incorporating evolutionary genomics into biodiversity conservation and management

“…For widespread species whose geographic ranges encompass environmental gradients, the association of allelic variation among populations (or individuals) with environmental factors can also be an indicator of local adaptation, as long as neutral patterns of genetic variation are taken into account [147,[173][174][175]. Several statistical approaches have been developed to test for such associations, many of which incorporate information on demographic history utilising general linear models [176], logistic regression [177], generalised estimating equations [178] or other types of models [179][180][181].…”

The population genomic signature of environmental selection in the widespread insect-pollinated tree species Frangula alnus at different geographical scales

“…Although landscape genomics approaches considerably increase the likelihood of finding weak and polygenic selection (De Mita et al, 2013), correlations among environmental variables (that is, spatial autocorrelation), and lack of knowledge regarding the true drivers of selection, may increase the amount of false positive results (De Villemereuil et al, 2014). In addition, it is assumed that there was sufficient time for selection to drive patterns of allele frequency distributions caused by the 1 environmental agents of selection (Joost et al, 2013). Yet when used together, population genomic and landscape genomic approaches may complement each other in terms of power and accuracy (Joost et al, 2013;De Villemereuil et al, 2014;Weinig et al, 2014).…”

“…Although the identification of loci that are under selection remains challenging in non-model species without reference genome sequences, several methods exist that aim to detect genetic variation putatively driven by selective forces (Seeb et al, 2011;Schoville et al, 2012;Jones et al, 2013;Joost et al, 2013;Weinig et al, 2014). First, the genome can be screened for outlier loci, by identifying genetic marker variation deviating from overall expected neutral distributions Excoffier et al, 2009).…”

“…In addition, it is assumed that there was sufficient time for selection to drive patterns of allele frequency distributions caused by the 1 environmental agents of selection (Joost et al, 2013). Yet when used together, population genomic and landscape genomic approaches may complement each other in terms of power and accuracy (Joost et al, 2013;De Villemereuil et al, 2014;Weinig et al, 2014).…”