title: Using Network Methodology to Infer Population Substructure.
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.
authors.affiliation: Institute of Genomic Mathematics, University of Bonn, Bonn, Germany; Institute of Human Genetics, University of Bonn, Bonn, Germany.
authors.forename: Dmitry
authors.initials: D
authors.lastname: Prokopenko
authors.affiliation: Institute of Genomic Mathematics, University of Bonn, Bonn, Germany; Institute of Human Genetics, University of Bonn, Bonn, Germany.
authors.forename: Julian
authors.initials: J
authors.lastname: Hecker
authors.affiliation: Channing Laboratory, Brigham and Women's Hospital, Boston, United States of America.
authors.forename: Edwin
authors.initials: E
authors.lastname: Silverman
authors.affiliation: Institute of Human Genetics, University of Bonn, Bonn, Germany.
authors.forename: Markus M
authors.initials: MM
authors.lastname: Nöthen
authors.affiliation: Institute of Medical Biometrics, Informatics and Epidemiology, University of Bonn, Bonn, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schmid
authors.affiliation: Institute of Genomic Mathematics, University of Bonn, Bonn, Germany; Department of Biostatistics, Harvard School of Public Health, Boston, United States of America; Channing Laboratory, Brigham and Women's Hospital, Boston, United States of America; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
authors.forename: Christoph
authors.initials: C
authors.lastname: Lange
authors.affiliation: Institute of Genomic Mathematics, University of Bonn, Bonn, Germany; Institute of Human Genetics, University of Bonn, Bonn, Germany.
authors.forename: Heide
authors.initials: H
authors.lastname: Loehlein Fier
mesh_terms: D000465:Algorithms; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011153:Population / Q000235:genetics*