scholarly journals Fast and flexible estimation of effective migration surfaces

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Joseph Marcus ◽  
Wooseok Ha ◽  
Rina Foygel Barber ◽  
John Novembre

Spatial population genetic data often exhibits ‘isolation-by-distance,’ where genetic similarity tends to decrease as individuals become more geographically distant. The rate at which genetic similarity decays with distance is often spatially heterogeneous due to variable population processes like genetic drift, gene flow, and natural selection. Petkova et al., 2016 developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here, we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field model in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). With simulations, we show conditions under which FEEMS can accurately recover effective migration surfaces with complex gene-flow histories, including those with anisotropy. We apply FEEMS to population genetic data from North American gray wolves and show it performs favorably in comparison to EEMS, with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.

Author(s):  
Joseph H. Marcus ◽  
Wooseok Ha ◽  
Rina Foygel Barber ◽  
John Novembre

AbstractAn important feature in spatial population genetic data is often “isolation-by-distance,” where genetic differentiation tends to increase as individuals become more geographically distant. Recently, Petkova et al. (2016) developed a statistical method called Estimating Effective Migration Surfaces (EEMS) for visualizing spatially heterogeneous isolation-by-distance on a geographic map. While EEMS is a powerful tool for depicting spatial population structure, it can suffer from slow runtimes. Here we develop a related method called Fast Estimation of Effective Migration Surfaces (FEEMS). FEEMS uses a Gaussian Markov Random Field in a penalized likelihood framework that allows for efficient optimization and output of effective migration surfaces. Further, the efficient optimization facilitates the inference of migration parameters per edge in the graph, rather than per node (as in EEMS). When tested with coalescent simulations, FEEMS accurately recovers effective migration surfaces with complex gene-flow histories, including those with anisotropy. Applications of FEEMS to population genetic data from North American gray wolves shows it to perform comparably to EEMS, but with solutions obtained orders of magnitude faster. Overall, FEEMS expands the ability of users to quickly visualize and interpret spatial structure in their data.


2014 ◽  
Author(s):  
Desislava Petkova ◽  
John Novembre ◽  
Matthew Stephens

Genetic data often exhibit patterns that are broadly consistent with "isolation by distance" - a phenomenon where genetic similarity tends to decay with geographic distance. In a heterogeneous habitat, decay may occur more quickly in some regions than others: for example, barriers to gene flow can accelerate the genetic differentiation between groups located close in space. 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 quantify and visualize variation in effective migration across the habitat, which can be used to identify potential barriers to gene flow, from geographically indexed large-scale genetic data. Our approach uses a population genetic model to relate underlying migration rates to expected pairwise genetic dissimilarities, and estimates migration rates by matching these expectations to the observed dissimilarities. We illustrate the potential and limitations of our method using simulations and data from elephant, human, and Arabidopsis thaliana populations. The resulting visualizations highlight important features of the spatial population structure that are difficult to discern using existing methods for summarizing genetic variation such as principal components analysis.


Author(s):  
Andrei Semikhodskii ◽  
Yevgeniy Krassotkin ◽  
Tatiana Makarova ◽  
Vladislav Zavarin ◽  
Viktoria Ilina ◽  
...  

2021 ◽  
pp. 1-6
Author(s):  
Safia A. Messaoudi ◽  
Saranya R. Babu ◽  
Abrar B. Alsaleh ◽  
Mohammed Albujja ◽  
Noora R. Al-Snan ◽  
...  

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0220620 ◽  
Author(s):  
Noora R. Al-Snan ◽  
Safia Messaoudi ◽  
Saranya R. Babu ◽  
Moiz Bakhiet

2019 ◽  
Vol 19 (5) ◽  
pp. 1374-1377
Author(s):  
Mahmut Aydın ◽  
Igor S. Kryvoruchko ◽  
Muhammet Şakiroğlu

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