Back

Population genomics of Marchantia polymorpha subspecies ruderalis reveals evidence of climate adaptation

Dolan, L.; Wu, S.; Jandrasits, K.; Swarts, K.; Roetzer, J.; Akimcheva, S.; Shimamura, M.; Hisanaga, T.; Berger, F.

2024-11-21 plant biology
10.1101/2024.11.19.624281 bioRxiv
Show abstract

Sexual reproduction results in the development of haploid and diploid cell states during the life cycle. In bryophytes the dominant multicellular haploid phase produces motile sperm that swim through water to the egg to effect fertilization from which a relatively small diploid phase develops. In angiosperms, the reduced multicellular haploid phase produces non-motile sperm that is delivered to the egg through a pollen tube to effect fertilization from which the dominant diploid phase develops. These different life cycle characteristics are likely to impact the distribution of genetic variation among populations. However, little is known about the distribution of genetic variation among populations of bryophytes. To understand how genetic variation is distributed among populations of a bryophyte and to establish the foundation for population genetics research in bryophytes, we described the genetic diversity of collections of Marchantia polymorpha subspecies ruderalis, a cosmopolitan ruderal liverwort. We explored genetic diversity of this species using 78 genetically unique (non-clonal) accessions from a total of 209 collected from 37 sites in Europe and Japan. There was no detectable population structure among European populations but significant genetic differentiation between Japanese and European populations. By associating genetic variation across the genome with global climate data, we identified summer temperature and precipitation as climate factors influencing the frequency of adaptative alleles. We speculate that the requirement for water through which motile sperm swim imposes a constraint on the life cycle to which the plant genetically adapts.

Matching journals

The top 10 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.