Wichman, H. A. | Search bioRxiv

bioRxiv2018

Tracing the History of LINE and SINE Extinction in Sigmodontine Rodents

BackgroundL1 retrotransposons have co-evolved with their mammalian hosts for the entire history of mammals and currently make up to 20% of a typical mammalian genome. B1 retrotransposons are dependent on L1 for retrotransposition and span the evolutionary history of rodents since their radiation. L1s were found to have lost their activity in a group of South American rodents, the Sigmodontinae, and B1 inactivation preceded the extinction of L1 in the same group. Consequently, a basal group of sigmodontines have active L1s but inactive B1s and a derived clade have both inactive L1s and B1s. It has been suggested that B1s became extinct during a long period of L1 quiescence and that L1s subsequently reemerged in the basal group.\n\nResultsHere we investigate the evolutionary histories of L1 and B1 in the sigmodontine rodents and show that L1 activity continued until after the split of the L1-extinct clade and the basal group. After the split, L1s had a small burst of activity in the former group, followed by extinction. In the basal group, activity was initially low but was followed by a dramatic increase in L1 activity. We found the last wave of B1s retrotransposition was large and probably preceded the split between the two rodent clades.\n\nConclusionsGiven that L1s had been steadily retrotransposing during the time corresponding to B1 extinction and that the burst of B1 activity preceding B1 extinction was large, we conclude that B1 extinction was not a result of L1 quiescence. Rather, the burst of B1 activity may have contributed to L1 extinction both by competition with L1 and by putting strong selective pressure on the host to control retrotransposition.

evolutionary biology↗

bioRxiv2017

Selecting among three basic fitness landscape models: additive, multiplicative and stickbreaking

Fitness landscapes map genotypes to organismal fitness. Their topography depends on how mutational effects interact-epistasis-and is important for understanding evolutionary processes such as speciation, the rate of adaptation, the advantage of recombination, and predictability versus stochasticity of evolution. The growing amount of empirical data has made it possible to better test landscape models empirically. We argue that this endeavor will benefit from the development and use of meaningful null models against which to compare more complex models. Here we develop statistical and computational methods for fitting fitness data from mutation combinatorial networks to three simple models: additive, multiplicative and stickbreaking. We employ a Bayesian framework for doing model selection. Using simulations, we demonstrate that our methods work and we explore their statistical performance: bias, error, and the power to discriminate among models. We then illustrate our approach and its flexibility by analyzing several previously published datasets. An R-package that implements our methods is available in the CRAN repository under the name Stickbreaker.

evolutionary biology↗