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Nevin Manimala Statistics

Genomic landscape of introgression from the ghost lineage in a gobiid fish uncovers the generality of forces shaping hybrid genomes

Mol Ecol. 2023 Dec 4. doi: 10.1111/mec.17216. Online ahead of print.

ABSTRACT

Extinct lineages can leave legacies in the genomes of extant lineages through ancient introgressive hybridization. The patterns of genomic survival of these extinct lineages provide insight into the role of extinct lineages in current biodiversity. However, our understanding on the genomic landscape of introgression from extinct lineages remains limited due to challenges associated with locating the traces of unsampled ‘ghost’ extinct lineages without ancient genomes. Herein, we conducted population genomic analyses on the East China Sea (ECS) lineage of Chaenogobius annularis, which was suspected to have originated from ghost introgression, with the aim of elucidating its genomic origins and characterizing its landscape of introgression. By combining phylogeographic analysis and demographic modelling, we demonstrated that the ECS lineage originated from ancient hybridization with an extinct ghost lineage. Forward simulations based on the estimated demography indicated that the statistic γ of the HyDe analysis can be used to distinguish the differences in local introgression rates in our data. Consistent with introgression between extant organisms, we found reduced introgression from extinct lineage in regions with low recombination rates and with functional importance, thereby suggesting a role of linked selection that has eliminated the extinct lineage in shaping the hybrid genome. Moreover, we identified enrichment of repetitive elements in regions associated with ghost introgression, which was hitherto little known but was also observed in the re-analysis of published data on introgression between extant organisms. Overall, our findings underscore the unexpected similarities in the characteristics of introgression landscapes across different taxa, even in cases of ghost introgression.

PMID:38047388 | DOI:10.1111/mec.17216

By Nevin Manimala

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