title: PopNet: A Markov Clustering Approach to Study Population Genetic Structure.
abstract: With the advent of low cost, high-throughput genome sequencing technology, population genomic data sets are being generated for hundreds of species of pathogenic, industrial, and agricultural importance. The challenge is how best to analyze and visually display these complex data sets to yield intuitive representations capable of capturing complex evolutionary relationships. Here we present PopNet, a novel computational method that identifies regions of shared ancestry in the chromosomes of related strains through clustering patterns of genetic variation. These relationships are subsequently visualized within a network by a novel implementation of chromosome painting. We apply PopNet to three diverse populations that feature differential rates of recombination and demonstrate its ability to capture evolutionary relationships as well as associate traits to specific loci. Compared with existing tools, PopNet provides substantial advances by both removing the need to predefine a single reference genome that can bias interpretation of population structure, as well as its ability to visualize multiple evolutionary relationships, such as recombination events and shared ancestry, across hundreds of strains.
authors.affiliation: Department of Biochemistry, University of Toronto, Toronto, ON, Canada.|Program in Molecular Medicine, Hospital for Sick Children, Toronto, ON, Canada.
authors.forename: Javi
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.
authors.forename: Asis
authors.initials: A
authors.lastname: Khan
authors.affiliation: Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.
authors.forename: Andrea
authors.initials: A
authors.lastname: Kennard
authors.affiliation: Molecular Parasitology Section, Laboratory of Parasitic Diseases, NIAID, National Institutes of Health, Bethesda, MD.
authors.forename: Michael E
authors.initials: ME
authors.lastname: Grigg
authors.affiliation: Department of Biochemistry, University of Toronto, Toronto, ON, Canada.|Program in Molecular Medicine, Hospital for Sick Children, Toronto, ON, Canada.|Departments of Computer Science and Molecular Genetics, University of Toronto, Toronto, ON, Canada.
authors.forename: John
authors.initials: J
authors.lastname: Parkinson
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods; D016000:Cluster Analysis; D014644:Genetic Variation / Q000235:genetics; D005828:Genetics, Population / Q000379:methods*; D016678:Genome / Q000235:genetics; D023281:Genomics / Q000379:methods*; D015810:Linkage Disequilibrium / Q000235:genetics; D008390:Markov Chains; D056186:Metagenomics / Q000379:methods; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011995:Recombination, Genetic / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*
keywords: chromosome painting; network visualization; population genomics; single nucleotide polymorphisms; software