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Visualizing spatial population structure with estimated effective migration surfaces.

Petkova D, Novembre J, Stephens M - Nat. Genet. (2015)

Bottom Line: We use the concept of 'effective migration' to model the relationship between genetics and geography.Our approach uses a population genetic model to relate effective migration rates to expected genetic dissimilarities.We illustrate its potential and limitations using simulations and data from elephant, human and Arabidopsis thaliana populations.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, The University of Chicago, Chicago, Illinois, USA.

ABSTRACT
Genetic data often exhibit patterns broadly consistent with 'isolation by distance'-a phenomenon where genetic similarity decays with geographic distance. In a heterogeneous habitat, this may occur more quickly in some regions than in others: for example, barriers to gene flow can accelerate differentiation between neighboring groups. We use the concept of 'effective migration' to model the relationship between genetics and geography. In this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to visualize variation in effective migration across a habitat from geographically indexed genetic data. Our approach uses a population genetic model to relate effective migration rates to expected genetic dissimilarities. We illustrate its potential and limitations using simulations and data from elephant, human and Arabidopsis thaliana populations. The resulting visualizations highlight important spatial features of population structure that are difficult to discern using existing methods for summarizing genetic variation.

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EEMS analysis of African elephant data 30. (a) African elephant samples are collected from two subspecies in five biogeographic regions: the forest elephant subspecies (in green) inhabits the west and central regions; the savanna elephant subspecies (in orange) inhabits the north, east and south regions. (b) Estimated effective migration rates for forest and savanna samples analyzed jointly. (c,d) Estimated effective migration rates for savanna and forest, respectively.
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Figure 4: EEMS analysis of African elephant data 30. (a) African elephant samples are collected from two subspecies in five biogeographic regions: the forest elephant subspecies (in green) inhabits the west and central regions; the savanna elephant subspecies (in orange) inhabits the north, east and south regions. (b) Estimated effective migration rates for forest and savanna samples analyzed jointly. (c,d) Estimated effective migration rates for savanna and forest, respectively.

Mentions: The African elephant provides a helpful illustration because the subspecies structure is clear and strongly correlated with geography: its primary feature is the low effective gene flow between forest and savanna elephants despite their geographic proximity. Correspondingly, the estimated effective migration surface is dominated by a strong barrier between their habitats (Fig. 4b). To a degree, EEMS captures its winding shape, though our method, based on Voronoi tessellations, is better adapted to visualize barriers with simpler structure. This is also an empirical example of an effective barrier to migration due to a non-equilibrium history of drift after divergence (as in Figure 3b).


Visualizing spatial population structure with estimated effective migration surfaces.

Petkova D, Novembre J, Stephens M - Nat. Genet. (2015)

EEMS analysis of African elephant data 30. (a) African elephant samples are collected from two subspecies in five biogeographic regions: the forest elephant subspecies (in green) inhabits the west and central regions; the savanna elephant subspecies (in orange) inhabits the north, east and south regions. (b) Estimated effective migration rates for forest and savanna samples analyzed jointly. (c,d) Estimated effective migration rates for savanna and forest, respectively.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4696895&req=5

Figure 4: EEMS analysis of African elephant data 30. (a) African elephant samples are collected from two subspecies in five biogeographic regions: the forest elephant subspecies (in green) inhabits the west and central regions; the savanna elephant subspecies (in orange) inhabits the north, east and south regions. (b) Estimated effective migration rates for forest and savanna samples analyzed jointly. (c,d) Estimated effective migration rates for savanna and forest, respectively.
Mentions: The African elephant provides a helpful illustration because the subspecies structure is clear and strongly correlated with geography: its primary feature is the low effective gene flow between forest and savanna elephants despite their geographic proximity. Correspondingly, the estimated effective migration surface is dominated by a strong barrier between their habitats (Fig. 4b). To a degree, EEMS captures its winding shape, though our method, based on Voronoi tessellations, is better adapted to visualize barriers with simpler structure. This is also an empirical example of an effective barrier to migration due to a non-equilibrium history of drift after divergence (as in Figure 3b).

Bottom Line: We use the concept of 'effective migration' to model the relationship between genetics and geography.Our approach uses a population genetic model to relate effective migration rates to expected genetic dissimilarities.We illustrate its potential and limitations using simulations and data from elephant, human and Arabidopsis thaliana populations.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, The University of Chicago, Chicago, Illinois, USA.

ABSTRACT
Genetic data often exhibit patterns broadly consistent with 'isolation by distance'-a phenomenon where genetic similarity decays with geographic distance. In a heterogeneous habitat, this may occur more quickly in some regions than in others: for example, barriers to gene flow can accelerate differentiation between neighboring groups. We use the concept of 'effective migration' to model the relationship between genetics and geography. In this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to visualize variation in effective migration across a habitat from geographically indexed genetic data. Our approach uses a population genetic model to relate effective migration rates to expected genetic dissimilarities. We illustrate its potential and limitations using simulations and data from elephant, human and Arabidopsis thaliana populations. The resulting visualizations highlight important spatial features of population structure that are difficult to discern using existing methods for summarizing genetic variation.

Show MeSH
Related in: MedlinePlus