It is unclear how mobile DNA sequences (transposable elements, hereafter TEs) invade eukaryotic genomes and reach stable copy numbers, as transposition can decrease host fitness. This challenge is particularly stark early in the invasion of a TE family at which point hosts may lack the specialized machinery to repress the spread of these TEs. One possibility (in addition to the evolution of host regulation of TEs), is that TE families may evolve to preferentially insert into chromosomal regions that are less likely to impact host fitness. This may allow the mean TE copy number to grow while minimizing the risk for host population extinction. To test this, we constructed simulations to explore how the transposition probability and insertion preference of a TE family influence the evolution of mean TE copy number and host population size, allowing for extinction. We find that the effect of a TE family’s insertion preference depends on a host’s ability to regulate this TE family. Without host repression, a neutral insertion preference increases the frequency of and decreases the time to population extinction. With host repression, a preference for neutral insertions minimizes the cumulative deleterious load, increases population fitness, and, ultimately, avoids triggering an extinction vortex.

We use a non-Wright-Fisher framework in SLiM 3 (Haller and Messer 2019) to explore how transposition probability and insertion preference influence the evolution of mean TE copy number and host population size . We consider a naïve diploid population that gains a single copy of a TE in the genome of a single individual (analogous to horizontal transfer). This TE belongs to a unique family with an assigned transposition probability and range of fitness effects for novel insertions that represent insertion preference. We generated 6 distinct models to explore this question that incorporate distinct biological features including host repression, non-autonomous TEs, and random excision of TEs. The table below highlights the differences between these models. Simulation results are stored in this data respository with a specific file for each model. The scripts for all simulations, job submissions, and figure visualizations can be found on GitHub: https://github.com/mam737/InsertionPreference_TEs.

All data files included with submission are text files and can be opened with any text editor. There is a compressed (.tar.gz) file for each distinct model that houses its respective simulation results. Once unzipped, each folder will contain a series of subdirectories named after the specific parameter combination used to generate them. There are at most 169 unique parameter combinations, and, for each parameter combination, at most 100 technical replicates. Due to computational constraints, some models have fewer parameter combinations and some parameter combinations have fewer technical replicates. Please refer to the manuscript for specific details.