Network-based hierarchical population structure analysis for large genomic data sets

Gili Greenbaum, Amir Rubin, Alan R. Templeton, Noah A. Rosenberg

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2020-2033
Number of pages14
JournalGenome Research
Volume29
Issue number12
DOIs
StatePublished - 1 Jan 2019

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

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