Journal ArticleDOI
Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations
Stéphane De Mita,Anne-Céline Thuillet,Nourollah Ahmadi,Stéphanie Manel,Joëlle Ronfort,Yves Vigouroux +5 more
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TLDR
In this paper, the authors provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection, and investigate the power and robustness of eight methods to identify loci potentially under selection.Abstract:
Thanks to genome-scale diversity data, present-day studies can provide a detailed view of how natural and cultivated species adapt to their environment and particularly to environmental gradients. However, due to their sensitivity, up-to-date studies might be more sensitive to undocumented demographic effects such as the pattern of migration and the reproduction regime. In this study, we provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection. We simulated 100 populations along a selective gradient and explored different migration models, sampling schemes and rates of self-fertilization. We investigated the power and robustness of eight methods to detect loci potentially under selection: three designed to detect genotype–environment correlations and five designed to detect adaptive differentiation (based on FST or similar measures). We show that genotype– environment correlation methods have substantially more power to detect selection than differentiation-based methods but that they generally suffer from high rates of false positives. This effect is exacerbated whenever allele frequencies are correlated, either between populations or within populations. Our results suggest that, when the underlying genetic structure of the data is unknown, a number of robust methods are preferable. Moreover, in the simulated scenario we used, sampling many populations led to better results than sampling many individuals per population. Finally, care should be taken when using methods to identify genotype–environment correlations without correcting for allele frequency autocorrelation because of the risk of spurious signals due to allele frequency correlations between populations.read more
Citations
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Journal ArticleDOI
Ecotypes of an ecologically dominant prairie grass (Andropogon gerardii) exhibit genetic divergence across the U.S. Midwest grasslands' environmental gradient
Miranda M. Gray,Paul St. Amand,Nora M. Bello,Matthew Galliart,Mary Knapp,Karen A. Garrett,Theodore J. Morgan,Sara G. Baer,Brian R. Maricle,Eduard D. Akhunov,Loretta C. Johnson +10 more
TL;DR: In this article, the authors characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest grasslands and assess the association between outlier loci and climate.
Journal ArticleDOI
Adaptive genetic variation distinguishes Chilean blue mussels ( Mytilus chilensis ) from different marine environments.
TL;DR: This panel identified clusters of genetically related individuals and demonstrated that much of the differentiation could be attributed to the three major regions and environments: extreme conditions in Patagonia, inner bay influenced by aquaculture (Reloncaví), and outer bay (Chiloé Island).
Journal ArticleDOI
Relative contributions of neutral and non-neutral genetic differentiation to inform conservation of steelhead trout across highly variable landscapes
TL;DR: Broad geographic patterns of neutral and non‐neutral variation demonstrated here can be used to accommodate priorities for regional management and inform long‐term conservation of steelhead trout in the Columbia River region.
Journal ArticleDOI
Navigating the Interface Between Landscape Genetics and Landscape Genomics.
TL;DR: This work focuses on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used.
Journal ArticleDOI
Evaluating genomic data for management of local adaptation in a changing climate: A lodgepole pine case study.
Colin R. Mahony,Colin R. Mahony,Ian MacLachlan,Brandon M. Lind,Jeremy B. Yoder,Jeremy B. Yoder,Tongli Wang,Sally N. Aitken +7 more
TL;DR: This study demonstrates that genomic data are most useful when paired with phenotypic data, but can also fill some of the traditional roles of phenotypesic data in management of species for which Phenotypic trials are not feasible.
References
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Yoav Benjamini,Yosef Hochberg +1 more
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