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Genomic islands of speciation in Anopheles gambiae.

PLoS biology | Sep 9, 2005

The African malaria mosquito, Anopheles gambiae sensu stricto (A. gambiae), provides a unique opportunity to study the evolution of reproductive isolation because it is divided into two sympatric, partially isolated subtaxa known as M form and S form. With the annotated genome of this species now available, high-throughput techniques can be applied to locate and characterize the genomic regions contributing to reproductive isolation. In order to quantify patterns of differentiation within A. gambiae, we hybridized population samples of genomic DNA from each form to Affymetrix GeneChip microarrays. We found that three regions, together encompassing less than 2.8 Mb, are the only locations where the M and S forms are significantly differentiated. Two of these regions are adjacent to centromeres, on Chromosomes 2L and X, and contain 50 and 12 predicted genes, respectively. Sequenced loci in these regions contain fixed differences between forms and no shared polymorphisms, while no fixed differences were found at nearby control loci. The third region, on Chromosome 2R, contains only five predicted genes; fixed differences in this region were also verified by direct sequencing. These "speciation islands" remain differentiated despite considerable gene flow, and are therefore expected to contain the genes responsible for reproductive isolation. Much effort has recently been applied to locating the genes and genetic changes responsible for reproductive isolation between species. Though much can be inferred about speciation by studying taxa that have diverged for millions of years, studying differentiation between taxa that are in the early stages of isolation will lead to a clearer view of the number and size of regions involved in the genetics of speciation. Despite appreciable levels of gene flow between the M and S forms of A. gambiae, we were able to isolate three small regions of differentiation where genes responsible for ecological and behavioral isolation are likely to be located. We expect reproductive isolation to be due to changes at a small number of loci, as these regions together contain only 67 predicted genes. Concentrating future mapping experiments on these regions should reveal the genes responsible for reproductive isolation between forms.

Pubmed ID: 16076241 RIS Download

Mesh terms: Animals | Anopheles gambiae | Base Sequence | Genetic Variation | Genome | Molecular Sequence Data | Oligonucleotide Array Sequence Analysis | Reproduction | Species Specificity

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Associated grants

  • Agency: NIGMS NIH HHS, Id: R01 GM061773
  • Agency: NIGMS NIH HHS, Id: R01 GM61773-01

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GenBank

NIH genetic sequence database that provides an annotated collection of all publicly available DNA sequences for almost 280 000 formally described species. (Jan 2014) These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and GenBank staff assigns accession numbers upon data receipt. It is part of the International Nucleotide Sequence Database Collaboration and daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP.

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A catalog of tools and software packages for the analysis and comprehension of high-throughput genomic data that uses the R statistical programming language. Bioconductor has a development version to which new features and packages are added prior to incorporation in the release. A large number of meta-data packages provide pathway, organism, microarray and other annotations. The broad goals of the Bioconductor project are: to provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data; to facilitate the inclusion of biological metadata in the analysis of genomic data; to provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software; and to train researchers on computational and statistical methods for the analysis of genomic data.

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