Abstracts (first author)

Talk 

Genomic tests of whether chromosomal rearrangements facilitated local adaptation in Anopheles gambiae based on coalescent expectations

Author(s): He Q, Knowles LL

Summary:

As the primary malaria vector that is widely distributed in sub-Sahara, Anopheles gambiae s. str. utilizes many different microhabitats where human reside. Its fast adaptation is partly attributed to polymorphic inversions within or between populations in the species. Theory predicts that large paracentric inversions, which do not directly affect individual fitness, can facilitate local adaptation by suppressing recombination among co-adapted genes captured by inversions. Clines and associations between inversions and specific environmental factors are suggestive of causal relationships, but do not demonstrate that the inversions facilitated the local adaptation. Here, by comparing the time to coalescence of genomic regions within inversions and among collinear regions, we can estimate the age of inversions, selective strength acting on inversions in local populations, and how selection varies depending on the connectivity among populations. Genomic regions inside inversions where co-adapted genes can be identified as well. In this study, wild mosquito samples were collected from five populations in transitional ecozones between forest and savanna in Cameroon. Karyotypes for two major inversions, 2La and 2Rb, were determined molecularly and genome-wide SNPs were identified for each individual from barcoded Illumina sequencing. Genetic data, together with spatial information on the sampled individuals, are then analyzed with a coalescent modeling framework to test whether inversions contributed to genomic divergence among ecologically dissimilar populations under gene flow. The approach provides new insights into genomic profiles when population divergence (or speciation) is promoted by structural genomic variants.



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XIV Congress of the European Society for Evolutionary Biology

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Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
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Portugal

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Computational Biology & Population Genomics Group 
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