Back

Population divergence manifested by genomic rearrangements in a keystone Arctic species with high gene flow

Hoff, S. N. K.; Maurstad, M. F.; Le Moan, A.; Ravinet, M.; Pampoulie, C.; Vieweg, I.; Collard, F.; Moiseev, D.; Bradbury, I. R.; Torresen, O. K.; Godiksen, J. A.; Hop, H.; Renaud, P. E.; Nahrgang, J.; Jakobsen, K. S.; Praebel, K.; Durant, J. M.; Jentoft, S.

2024-06-28 genomics
10.1101/2024.06.28.597535 bioRxiv
Show abstract

Genomic rearrangements have in recent years gained attention due to their evolutionary role in processes related to adaptation to local environmental conditions as well as diversification and speciation. In this study, we report on genomic rearrangements in the cold-water adapted polar cod (Boreogadus saida), a keystone Arctic fish species. By taking advantage of a new chromosome-level genome assembly in combination with whole-genome population sequencing data from specimens across the northern Barents Sea and adjacent regions, we identified a substantial number of larger chromosomal inversions (n=20) and characterized the previously identified chromosomal fusions (n=5). These genomic features -- encompassing over 20% of the genome -- exhibited genetic divergence, strong internal linkage disequilibrium, and signals of selection. Two of the identified inversions were associated with the two previously described hemoglobin clusters, while a third chromosomal region was found to differentiate between males and females. Moreover, clustering analyses on genotype frequencies of inversions revealed sub- structuring according to five geographic sub-groups suggesting sub-populations and/or the existence of cryptic ecotypes. These results provide novel insights into the impact of genomic rearrangements in population divergence and thus, potentially local adaptation, especially in species with high gene flow.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.