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@INPROCEEDINGS{Prokopenko:145644,
      author       = {Prokopenko, D. and Hecker, J. and Silverman, E. and Pagano,
                      M. and Noethen, M. and Lange, Christoph and Loehlein Fie,
                      H.},
      title        = {{A}36: {U}tilizing the {J}accard {I}ndex to {R}eveal
                      {P}opulation{S}tratification in {S}equencing {D}ata: {A}
                      {S}imulation{S}tudy and an {A}pplication to the 1000
                      {G}enomes{P}roject},
      journal      = {Human heredity},
      volume       = {79},
      number       = {1},
      issn         = {0001-5652},
      reportid     = {DZNE-2020-00974},
      pages        = {44},
      year         = {2015},
      abstract     = {Population stratification is one of the major sources of
                      confounding in genetic association studies, potentially
                      causing falsepositive and false-negative results. The
                      effectiveness of existingadjustment approaches which are
                      mostly built on the estimationof the genetic
                      variance/covariance matrix is unclear for rare variants,
                      since those variants are genetically much ‘younger’
                      andmight represent a different pattern of population
                      structure.Here, we present a novel approach for the
                      identification of population substructure in high
                      density-genotyping data/next generation sequencing data. The
                      approach exploits the co-appearances of rare genetic
                      variants in individuals. The method can beapplied to all
                      available genetic loci, does not require linkage
                      disequilibrium (LD) pruning, and is computationally fast.
                      Usingsequencing data from the 1000 Genomes Project, the
                      features of the approach are illustrated and compared to
                      existing methodology (i.e. EIGENSTRAT). We find that our
                      approach works particularly well for genetic loci with very
                      small minor allele frequencies. The results suggest that the
                      inclusion of rare-variantdata/sequencing data in our
                      approach provides a much higherresolution-picture of
                      population-substructure than it can be obtained with
                      existing methodology. Furthermore, we performedextensive
                      simulation studies based on the minor allele frequencies of
                      the European populations. We find scenarios where ourmethod
                      was able to control the type 1 error more precisely
                      andshowed higher power.},
      month         = {Apr},
      date          = {2015-04-16},
      organization  = {43rd European Mathematical Genetics
                       Meeting 2015, Brest (France), 16 Apr
                       2015 - 17 Apr 2015},
      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)1 / PUB:(DE-HGF)16},
      url          = {https://pub.dzne.de/record/145644},
}