Inbreeding in stochastic subdivided mating systems: the genetic consequences of host spatial structure, aggregated transmission dynamics and life history characteristics in parasite populations
Inbreeding in parasite populations can have important epidemiological and evolutionary implications. However, theoretical models have predominantly focussed on the evolution of parasite populations under strong selection or in epidemic situations, and our understanding of neutral gene dynamics in parasite populations at equilibrium has been limited to verbal arguments or conceptual models. This study focusses on how host–parasite population dynamics affects observed levels of inbreeding in a random sample of parasites from an infinite population of hosts by bridging traditional genetic and parasitological processes utilizing a backward–forward branching Markov process embedded within a flexible statistical framework, the logarithmic-poisson mixture model. My results indicate that levels of inbreeding in parasites are impacted by demographic and/or transmission dynamics (subdivided mating, aggregated transmission dynamics and host spatial structure), and that this inbreeding is poorly estimated by ‘equilibrium’ levels of inbreeding calculated assuming regular systems of mating. Specifically, the model reveals that at low levels of inbreeding (𝐹 ≤ 0.1), equilibrium levels of inbreeding are lower than those observed, while at high levels of inbreeding the opposite pattern occurs. The model also indicates that inbreeding could have important epidemiological implications (e.g., the spread of recessive drug resistance genes) by directly impacting the observed frequency of rare homozygotes in parasite populations. My results indicate that frequencies of rare homozygotes are affected by aggregated transmission dynamics and host spatial structure, and also that an increase in the frequency of rare homozygotes can be caused by a decrease in effective population size solely due to the presence of a subdivided breeding system.
Volume 100, 2021
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