Using Network Methodology to Infer Population Substructure

DSpace/Manakin Repository

Using Network Methodology to Infer Population Substructure

Citable link to this page


Title: Using Network Methodology to Infer Population Substructure
Author: Prokopenko, Dmitry; Hecker, Julian; Silverman, Edwin; Nöthen, Markus M.; Schmid, Matthias; Lange, Christoph; Loehlein Fier, Heide

Note: Order does not necessarily reflect citation order of authors.

Citation: Prokopenko, Dmitry, Julian Hecker, Edwin Silverman, Markus M. Nöthen, Matthias Schmid, Christoph Lange, and Heide Loehlein Fier. 2015. “Using Network Methodology to Infer Population Substructure.” PLoS ONE 10 (6): e0130708. doi:10.1371/journal.pone.0130708.
Full Text & Related Files:
Abstract: One of the main caveats of association studies is the possible affection by bias due to population stratification. Existing methods rely on model-based approaches like structure and ADMIXTURE or on principal component analysis like EIGENSTRAT. Here we provide a novel visualization technique and describe the problem of population substructure from a graph-theoretical point of view. We group the sequenced individuals into triads, which depict the relational structure, on the basis of a predefined pairwise similarity measure. We then merge the triads into a network and apply community detection algorithms in order to identify homogeneous subgroups or communities, which can further be incorporated as covariates into logistic regression. We apply our method to populations from different continents in the 1000 Genomes Project and evaluate the type 1 error based on the empirical p-values. The application to 1000 Genomes data suggests that the network approach provides a very fine resolution of the underlying ancestral population structure. Besides we show in simulations, that in the presence of discrete population structures, our developed approach maintains the type 1 error more precisely than existing approaches.
Published Version: doi:10.1371/journal.pone.0130708
Other Sources:
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search