TY  - JOUR
AU  - Prokopenko, Dmitry
AU  - Hecker, Julian
AU  - Silverman, Edwin
AU  - Nöthen, Markus M
AU  - Schmid, Matthias
AU  - Lange, Christoph
AU  - Loehlein Fier, Heide
TI  - Using Network Methodology to Infer Population Substructure.
JO  - PLOS ONE
VL  - 10
IS  - 6
SN  - 1932-6203
CY  - San Francisco, California, US
PB  - PLOS
M1  - DZNE-2020-04309
SP  - e0130708
PY  - 2015
AB  - 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.
KW  - Algorithms
KW  - Humans
KW  - Models, Genetic
KW  - Polymorphism, Single Nucleotide
KW  - Population: genetics
LB  - PUB:(DE-HGF)16
C6  - pmid:26098940
C2  - pmc:PMC4476755
DO  - DOI:10.1371/journal.pone.0130708
UR  - https://pub.dzne.de/record/137987
ER  -