The Red Queen and the persistence of linkage-disequilibrium oscillations in finite and infinite populations.
BACKGROUND
The Red Queen Hypothesis (RQH) suggests that the coevolutionary dynamics of host-parasite systems can generate selection for increased host recombination. Since host-parasite interactions often have a strong genetic basis, recombination between different hosts can increase the fraction of novel and potentially resistant offspring genotypes. A prerequisite for this mechanism is that host-parasite interactions generate persistent oscillations of linkage disequilibria (LD).


RESULTS
We use deterministic and stochastic models to investigate the persistence of LD oscillations and its impact on the RQH. The standard models of the Red Queen dynamics exhibit persistent LD oscillations under most circumstances. Here, we show that altering the standard model from discrete to continuous time or from simultaneous to sequential updating results in damped LD oscillations. This suggests that LD oscillations are structurally not robust. We then show that in a stochastic regime, drift can counteract this dampening and maintain the oscillations. In addition, we show that the amplitude of the oscillations and therefore the strength of the resulting selection for or against recombination are inversely proportional to the size of the (host) population.


CONCLUSIONS
We find that host parasite-interactions cannot generally maintain oscillations in the absence of drift. As a consequence, the RQH can strongly depend on population size and should therefore not be interpreted as a purely deterministic hypothesis.
Kouyos
Roger D
RD
Institute of Integrative Biology, ETH Zürich, ETH-Zentrum CHN, 8092 Zürich, Switzerland. roger.kouyos@env.ethz.ch
Salathé
Marcel
M
Bonhoeffer
Sebastian
S
D000818:Animals; D005828:Genetics, Population; D006790:Host-Parasite Interactions / Q000235:genetics*; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D011156:Population Density; D012098:Reproduction / Q000235:genetics; D012641:Selection, Genetic; D013269:Stochastic Processes