Using Environmental Correlations to Identify Loci Underlying Local Adaptation

By Graham Coop, David B Witonsky, A. Di Rienzo, J. K. Pritchard. Genetics, 2010

698 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bd9a3479f2a38aa4e5730b2bbd6a467d50b297f1

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In Genetics

Citations in the corpus

Isolation by environment (2014, methodology): “Several recent studies have already explored how landscape genomics can provide greater power and resolution for examining spatial patterns and adaptive variation (e.g. Coop et al. 2010; Lasky et al. 2012; Parchman et al. 2012; Vincent et al. 2013; Yoder et al. 2014).”
A new FST‐based method to uncover local adaptation using environmental variables (2015, methodology): “The rationale is that selective pressures should create associations between allele frequencies at the selected loci and the causal environmental variables (Coop et al. 2010).”
A practical guide to environmental association analysis in landscape genomics (2015, methodology): “Coop et al. (2010) developed a Bayesian approach, BAYENV, to assess evidence for correlations between loci and environmental factors.”
Applications of next-generation sequencing to the study of biological invasions (2015, background): “Caution must also be observed when interpreting results of selective regions influenced by the environment as GWSS merely identify correlations, and follow-up work is essential to investigate the interplay present (Coop et al., 2010).”
Testing for Associations between Loci and Environmental Gradients Using Latent Factor Mixed Models (2012, methodology): “2006; Zhou and Stephens 2012), and in tests based on empirical approaches (Coop et al. 2010; Poncet et al. 2010).”
Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions (2016, neutral): “There are multiple ways to characterize these covariances, such as co-variance in allele frequencies among populations (Coop et al. 2010; Günther and Coop 2013; Gautier 2015; table A1), a treelike population history (Bonhomme et al. 2010), and re-latedness or other latent factors in a mixed model.”
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data (2012, background): “[48] and Weir and Hill [49], although these authors did not explicitly model the variance-covariance matrix in terms of a tree.”
Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate (2013, methodology): “…per location and few locations, allowing for quantification of within-as well as among-population variation based on allele frequencies (e.g., Coop et al. 2010), some landscape genomic and clinal analyses are powerful when using seeds from just one or two parents per location and sampling a…”
Demographic History and Genetic Adaptation in the Himalayan Region Inferred from Genome-Wide SNP Genotypes of 49 Populations (2018, methodology): “Finally, to further validate our selection signals, we calculated genome-wide associations between allele frequency and altitude using BayEnv v2 (Coop et al. 2010; Gunther and Coop 2013), a Bayesian framework specifically designed to detect correlation between allele frequencies and environmental fa”
Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “2015), such as BayEnv (Coop et al. 2010; Günther and Coop 2013), LFMM (Frichot et al.”
Assisted Gene Flow to Facilitate Local Adaptation to Climate Change (2013, background): “Allele-environment associations can be used to detect single nucleotide or other polymorphisms associated with climatic or other environmental variables (e.g., Coop et al. 2010), much as analysis of phenotypic clines along environmental gradients can reflect local adaptation (Endler 1977).”