Article ; Online: Detection of F1 Hybrids from Single-genome Data Reveals Frequent Hybridization in Hymenoptera and Particularly Ants.
Molecular biology and evolution
2022 Volume 39, Issue 4
Abstract: Hybridization occupies a central role in many fundamental evolutionary processes, such as speciation or adaptation. Yet, despite its pivotal importance in evolution, little is known about the actual prevalence and distribution of current hybridization ... ...
Abstract | Hybridization occupies a central role in many fundamental evolutionary processes, such as speciation or adaptation. Yet, despite its pivotal importance in evolution, little is known about the actual prevalence and distribution of current hybridization across the tree of life. Here we develop and implement a new statistical method enabling the detection of F1 hybrids from single-individual genome sequencing data. Using simulations and sequencing data from known hybrid systems, we first demonstrate the specificity of the method, and identify its statistical limits. Next, we showcase the method by applying it to available sequencing data from more than 1,500 species of Arthropods, including Hymenoptera, Hemiptera, Coleoptera, Diptera, and Archnida. Among these taxa, we find Hymenoptera, and especially ants, to display the highest number of candidate F1 hybrids, suggesting higher rates of recent hybridization between previously isolated gene pools in these groups. The prevalence of F1 hybrids was heterogeneously distributed across ants, with taxa including many candidates tending to harbor specific ecological and life-history traits. This work shows how large-scale genomic comparative studies of recent hybridization can be implemented, uncovering the determinants of first-generation hybridization across whole taxa. |
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MeSH term(s) | Animals ; Ants/genetics ; Gene Pool ; Genome ; Genomics ; Hybridization, Genetic |
Language | English |
Publishing date | 2022-05-06 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 998579-7 |
ISSN | 1537-1719 ; 0737-4038 |
ISSN (online) | 1537-1719 |
ISSN | 0737-4038 |
DOI | 10.1093/molbev/msac071 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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