24,443 research outputs found
Genealogies of rapidly adapting populations
The genetic diversity of a species is shaped by its recent evolutionary
history and can be used to infer demographic events or selective sweeps. Most
inference methods are based on the null hypothesis that natural selection is a
weak or infrequent evolutionary force. However, many species, particularly
pathogens, are under continuous pressure to adapt in response to changing
environments. A statistical framework for inference from diversity data of such
populations is currently lacking. Toward this goal, we explore the properties
of genealogies in a model of continual adaptation in asexual populations. We
show that lineages trace back to a small pool of highly fit ancestors, in which
almost simultaneous coalescence of more than two lineages frequently occurs.
While such multiple mergers are unlikely under the neutral coalescent, they
create a unique genetic footprint in adapting populations. The site frequency
spectrum of derived neutral alleles, for example, is non-monotonic and has a
peak at high frequencies, whereas Tajima's D becomes more and more negative
with increasing sample size. Since multiple merger coalescents emerge in many
models of rapid adaptation, we argue that they should be considered as a
null-model for adapting populations.Comment: to appear in PNA
Robust forward simulations of recurrent hitchhiking
Evolutionary forces shape patterns of genetic diversity within populations
and contribute to phenotypic variation. In particular, recurrent positive
selection has attracted significant interest in both theoretical and empirical
studies. However, most existing theoretical models of recurrent positive
selection cannot easily incorporate realistic confounding effects such as
interference between selected sites, arbitrary selection schemes, and
complicated demographic processes. It is possible to quantify the effects of
arbitrarily complex evolutionary models by performing forward population
genetic simulations, but forward simulations can be computationally prohibitive
for large population sizes (). A common approach for overcoming these
computational limitations is rescaling of the most computationally expensive
parameters, especially population size. Here, we show that ad hoc approaches to
parameter rescaling under the recurrent hitchhiking model do not always provide
sufficiently accurate dynamics, potentially skewing patterns of diversity in
simulated DNA sequences. We derive an extension of the recurrent hitchhiking
model that is appropriate for strong selection in small population sizes, and
use it to develop a method for parameter rescaling that provides the best
possible computational performance for a given error tolerance. We perform a
detailed theoretical analysis of the robustness of rescaling across the
parameter space. Finally, we apply our rescaling algorithms to parameters that
were previously inferred for Drosophila, and discuss practical considerations
such as interference between selected sites
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