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@ARTICLE{Prokopenko:137987,
      author       = {Prokopenko, Dmitry and Hecker, Julian and Silverman, Edwin
                      and Nöthen, Markus M and Schmid, Matthias and Lange,
                      Christoph and Loehlein Fier, Heide},
      title        = {{U}sing {N}etwork {M}ethodology to {I}nfer {P}opulation
                      {S}ubstructure.},
      journal      = {PLOS ONE},
      volume       = {10},
      number       = {6},
      issn         = {1932-6203},
      address      = {San Francisco, California, US},
      publisher    = {PLOS},
      reportid     = {DZNE-2020-04309},
      pages        = {e0130708},
      year         = {2015},
      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.},
      keywords     = {Algorithms / Humans / Models, Genetic / Polymorphism,
                      Single Nucleotide / Population: genetics},
      cin          = {U T4 Researchers - Bonn},
      ddc          = {610},
      cid          = {I:(DE-2719)7000008},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:26098940},
      pmc          = {pmc:PMC4476755},
      doi          = {10.1371/journal.pone.0130708},
      url          = {https://pub.dzne.de/record/137987},
}