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      Genome-Wide Divergence in the West-African Malaria Vector Anopheles melas

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          Abstract

          Anopheles melas is a member of the recently diverged An. gambiae species complex, a model for speciation studies, and is a locally important malaria vector along the West-African coast where it breeds in brackish water. A recent population genetic study of An. melas revealed species-level genetic differentiation between three population clusters. An. melas West extends from The Gambia to the village of Tiko, Cameroon. The other mainland cluster, An. melas South, extends from the southern Cameroonian village of Ipono to Angola. Bioko Island, Equatorial Guinea An. melas populations are genetically isolated from mainland populations. To examine how genetic differentiation between these An. melas forms is distributed across their genomes, we conducted a genome-wide analysis of genetic differentiation and selection using whole genome sequencing data of pooled individuals (Pool-seq) from a representative population of each cluster. The An. melas forms exhibit high levels of genetic differentiation throughout their genomes, including the presence of numerous fixed differences between clusters. Although the level of divergence between the clusters is on a par with that of other species within the An. gambiae complex, patterns of genome-wide divergence and diversity do not provide evidence for the presence of pre- and/or postmating isolating mechanisms in the form of speciation islands. These results are consistent with an allopatric divergence process with little or no introgression.

          Most cited references53

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          The genome sequence of the malaria mosquito Anopheles gambiae.

          Anopheles gambiae is the principal vector of malaria, a disease that afflicts more than 500 million people and causes more than 1 million deaths each year. Tenfold shotgun sequence coverage was obtained from the PEST strain of A. gambiae and assembled into scaffolds that span 278 million base pairs. A total of 91% of the genome was organized in 303 scaffolds; the largest scaffold was 23.1 million base pairs. There was substantial genetic variation within this strain, and the apparent existence of two haplotypes of approximately equal frequency ("dual haplotypes") in a substantial fraction of the genome likely reflects the outbred nature of the PEST strain. The sequence produced a conservative inference of more than 400,000 single-nucleotide polymorphisms that showed a markedly bimodal density distribution. Analysis of the genome sequence revealed strong evidence for about 14,000 protein-encoding transcripts. Prominent expansions in specific families of proteins likely involved in cell adhesion and immunity were noted. An expressed sequence tag analysis of genes regulated by blood feeding provided insights into the physiological adaptations of a hematophagous insect.
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            Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

            High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.
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              Testing for ancient admixture between closely related populations.

              One enduring question in evolutionary biology is the extent of archaic admixture in the genomes of present-day populations. In this paper, we present a test for ancient admixture that exploits the asymmetry in the frequencies of the two nonconcordant gene trees in a three-population tree. This test was first applied to detect interbreeding between Neandertals and modern humans. We derive the analytic expectation of a test statistic, called the D statistic, which is sensitive to asymmetry under alternative demographic scenarios. We show that the D statistic is insensitive to some demographic assumptions such as ancestral population sizes and requires only the assumption that the ancestral populations were randomly mating. An important aspect of D statistics is that they can be used to detect archaic admixture even when no archaic sample is available. We explore the effect of sequencing error on the false-positive rate of the test for admixture, and we show how to estimate the proportion of archaic ancestry in the genomes of present-day populations. We also investigate a model of subdivision in ancestral populations that can result in D statistics that indicate recent admixture.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                27 July 2016
                September 2016
                : 6
                : 9
                : 2867-2879
                Affiliations
                [* ]Department of Entomology, Texas A&M University, College Station, Texas 77843
                []Department of Poultry Science, Texas A&M University, College Station, Texas 77843
                []Medical Research Council Unit, Banjul, Fajara, The Gambia
                [§ ]Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway
                [** ]Medical Care Development International, Malabo, Equatorial Guinea
                Author notes
                [1 ]Corresponding author: Department of Entomology, Texas A&M University, 2475 TAMU, Heep Center Room 412, College Station, TX 77843. E-mail: kcdeitz@ 123456tamu.edu
                Article
                GGG_031906
                10.1534/g3.116.031906
                5015944
                27466271
                fbdf4f6b-1198-435e-9787-5e65a88427a0
                Copyright © 2016 Deitz et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 June 2016
                : 09 July 2016
                Page count
                Figures: 4, Tables: 3, Equations: 0, References: 76, Pages: 13
                Categories
                Investigations

                Genetics
                anopheles melas,anopheles gambiae,malaria,population genomics,pool-seq
                Genetics
                anopheles melas, anopheles gambiae, malaria, population genomics, pool-seq

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