title: Powerful methods for detecting introgressed regions from population genomic data.
abstract: Understanding the types and functions of genes that are able to cross species boundaries-and those that are not-is an important step in understanding the forces maintaining species as largely independent lineages across the remainder of the genome. With large next-generation sequencing data sets we are now able to ask whether introgression has occurred across the genome, and multiple methods have been proposed to detect the signature of such events. Here, we introduce a new summary statistic that can be used to test for introgression, RNDmin , that makes use of the minimum pairwise sequence distance between two population samples relative to divergence to an outgroup. We find that our method offers a modest increase in power over other, related tests, but that all such tests have high power to detect introgressed loci when migration is recent and strong. RNDmin is robust to variation in the mutation rate, and remains reliable even when estimates of the divergence time between sister species are inaccurate. We apply RNDmin to population genomic data from the African mosquitoes Anopheles quadriannulatus and A. arabiensis, identifying three novel candidate regions for introgression. Interestingly, one of the introgressed loci is on the X chromosome, but outside of an inversion separating these two species. Our results suggest that significant, but rare, sharing of alleles is occurring between species that diverged more than 1 million years ago, and that application of these methods to additional systems are likely to reveal similar results.
authors.affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA.
authors.forename: Benjamin K
authors.initials: BK
authors.lastname: Rosenzweig
authors.affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA.|Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, 48109, USA.
authors.forename: James B
authors.initials: JB
authors.lastname: Pease
authors.affiliation: Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA.|Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN, 46556, USA.
authors.forename: Nora J
authors.initials: NJ
authors.lastname: Besansky
authors.affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN, 47405, USA.|Department of Biology, Indiana University, Bloomington, IN, 47405, USA.
authors.forename: Matthew W
authors.initials: MW
authors.lastname: Hahn
mesh_terms: D000818:Animals; D000852:Anopheles / Q000235:genetics*; D007446:Chromosome Inversion; D003198:Computer Simulation; D019143:Evolution, Molecular; D049810:Genetic Speciation; D005828:Genetics, Population; D023281:Genomics / Q000379:methods*; D006824:Hybridization, Genetic; D008957:Models, Genetic; D015233:Models, Statistical; D014960:X Chromosome / Q000235:genetics
keywords: adaptation; bioinfomatics; genomics; population genetics; speciation