A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation

By Stéphane Joost, Aurélie Bonin, M. W. Bruford, L. Després, C. Conord, G. Erhardt, P. Taberlet. Molecular Ecology, 2007

473 citations recorded by the source corpus.

The detection of adaptive loci in the genome is essential as it gives the possibility of understanding what proportion of a genome or which genes are being shaped by natural selection. Several statistical methods have been developed which make use of molecular data to reveal genomic regions under selection. In this paper, we propose an approach to address this issue from the environmental angle, in order to complement results obtained by population genetics. We introduce a new method to detect signatures of natural selection based on the application of spatial analysis, with the contribution of geographical information systems (GIS), environmental variables and molecular data. Multiple univariate logistic regressions were carried out to test for association between allelic frequencies at marker loci and environmental variables. This spatial analysis method (SAM) is similar to current population genomics approaches since it is designed to scan hundreds of markers to assess a putative association with hundreds of environmental variables. Here, by application to studies of pine weevils and breeds of sheep we demonstrate a strong correspondence between SAM results and those obtained using population genetics approaches. Statistical signals were found that associate loci with environmental parameters, and these loci behave atypically in comparison with the theoretical distribution for neutral loci. The contribution of this new tool is not only to permit the identification of loci under selection but also to establish hypotheses about ecological factors that could exert the selection pressure responsible. In the future, such an approach may accelerate the process of hunting for functional genes at the population level.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/526bfe3e3d5e7a7b4918779596720a60a2700564

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In Genetics

Citations in the corpus

Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate (2013, methodology): “Significant outliers may indicate divergent selection and a role in local adaptation as well as purifying selection for those loci that are less differentiated than expected (e.g., Beaumont and Nichols 1996; Bonin et al. 2007; Joost et al. 2007).”
Identifying future research needs in landscape genetics: where to from here? (2009, background): “…suggested for empirically exploring relationships between selection and environmental gradients to associate patterns in genes under selection with combinations of environmental variables (e.g., Vasemägi and Primmer 2005; Joost et al. 2007; Ouborg and Vriezen 2007; Holderegger and Wagner 2008).”
Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models (2012, methodology): “The first set of methods include a linear model (LM) and generalized linear model (GLM) (Joost et al. 2007), and the second set of methods included three empirical methods: a PCRM, PMTs (Smouse et al.”
Integrating GIS-based environmental data into evolutionary biology. (2008, background): “Furthermore, new spatial analyses for testing whether natural selection at the molecular level is associated with environmental variation [ 52 ] show much promise for identifying candidate genes that might promote the geographic spread of invasive and domesticated species.”
Divergent selection and heterogeneous genomic divergence (2009, background): “Zostera marina (marine flowering plant) Tidal creek vs. tidal flat 25 EST-derived and anonymous microsatellite markers Compare divergence between populations in different habitats for three independent population pairs 12% of loci outliers, with these exhibiting replicated divergence for population ”
Climate change, genetic markers and species distribution modelling (2015, unknown): “At the level of individual genetic markers, Joost et al. (2007) use similar regression approaches for associations of allelic frequencies at marker loci and environmental variables.”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, background): “Indeed, spectral data could be a convenient way of characterizing individual sampling localities or entire landscapes in terms of their environmental characteristics, and could therefore greatly facilitate broad-scale and data-intensive studies in adaptive landscape genetics (Joost et al., 2007; Hol”
Land ahead: using genome scans to identify molecular markers of adaptive relevance (2008, methodology): “In doing so, Joost et al. (2007) identified one group of populations of pine weevil at the southern edge of the species’ distribution area showing a deviating behaviour due to genetic drift possibly caused by a founder event.”
Seascape Genetics: Populations, Individuals, and Genes Marooned and Adrift (2013, unknown): “Increasingly, genomic methods and next generation sequencing are facilitating population genomics of non-model organisms (Luikart et al. 2003; Hohenlohe et al. 2010) and extensions into “landscape genomics” (Joost et al. 2007; Nielsen et al. 2009a; Manel et al. 2010).”
A framework for incorporating evolutionary genomics into biodiversity conservation and management (2015, background): “[176], logistic regression [177], generalised estimating equations [178] or other types of models [179-181].”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “EAA can be performed with various statistical approaches, including logistic regressions (Stucki et al. 2016; Joost et al. 2007; Carl and Kühn 2007), matrix correlations (Hancock et al.”
Sky islands of southwest China. I: an overview of phylogeographic patterns (2014, background): “[107] were able to detect the adaptive loci in the genome of the large pine weevil.”