Back

Genetic diversity landscapes in outcrossing and selfing Caenorhabditis nematodes

Teterina, A. A.; Willis, J. H.; Lukac, M.; Jovelin, R.; Cutter, A. D.; Phillips, P. C.

2022-12-14 evolutionary biology
10.1101/2022.12.13.520296 bioRxiv
Show abstract

Caenorhabditis nematodes form an excellent model for studying how the mode of reproduction affects genetic diversity, as some species reproduce via outcrossing whereas others can self-fertilize. Currently, chromosome-level patterns of diversity and recombination are only available for self-reproducing Caenorhabditis, making the generality of genomic patterns across the genus unclear given the profound potential influence of reproductive mode. Here we present a whole-genome diversity landscape, coupled with a new genetic map, for the outcrossing nematode C. remanei. We demonstrate that the genomic distribution of recombination in C. remanei, like the model nematode C. elegans, shows high recombination rates on chromosome arms and low rates toward the central regions. Patterns of genetic variation across the genome are also similar between these species, but differ dramatically in scale, being 10-fold greater for C. remanei. Historical reconstructions of variation in effective population size over the past million generations echo this difference in polymorphism. Evolutionary simulations demonstrate how selection, recombination, mutation, and selfing shape variation along the genome, and that multiple drivers can produce patterns similar to those observed in natural populations. Convolutional neural networks demonstrate the potential for classifying distinct evolutionary scenarios in simulated populations. Distinguishing these forces confidently with the empirical data, however, will benefit from larger population genomic samples from multiple populations and consideration of an even more extensive training set of simulations.

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.