Analysis of population structure in natural populations using genetic data is a common practice in ecological and evolutionary studies. With large genomic data sets of populations now appearing more frequently across the taxonomic spectrum, it is becoming increasingly possible to reveal many hierarchical levels of structure, including fine-scale genetic clusters. To analyze these data sets, methods need to be appropriately suited to the challenges of extracting multilevel structure from whole-genome data. Here, we present a network-based approach for constructing population structure representations from genetic data. The use of community-detection algorithms from network theory generates a natural hierarchical perspective on the representation that the method produces. The method is computationally efficient, and it requires relatively few assumptions regarding the biological processes that underlie the data. We show the approach by analyzing population structure in the model plant species Arabidopsis thaliana and in human populations. These examples illustrate how networkbased approaches for population structure analysis are well-suited to extracting valuable ecological and evolutionary information in the era of large genomic data sets.
Bibliographical noteFunding Information:
We thank Arun Durvasula, Andrea Fulgione, and Angela Hancock for help with the A. thaliana data, and Jonathan Kang for help with the HGDP data set. We also thank Ellie E. Armstrong, Danny Hendler, and Stefan Prost for helpful discussions. This study was supported by National Institutes of Health grant R01HG005855 awarded to N.A.R.
© 2019 Greenbaum et al.
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