Amount Of Genetic Drift Research Articles

On the limits of fitting complex models of population history to f-statistics.

Our understanding of population history in deep time has been assisted by fitting admixture graphs ('AGs') to data: models that specify the ordering of population splits and mixtures, which along with the amount of genetic drift on each lineage and the proportions of mixture, is the only information needed to predict the patterns of allele frequency correlation among populations. Not needing to specify population size changes, split times, or whether admixture events were sudden or drawn out simplifies the space of models that need to be searched. However, the space of possible AGs relating populations is vast and cannot be sampled fully, and thus most published studies have identified fitting AGs through a manual process driven by prior hypotheses, leaving the vast majority of alternative models unexplored. Here, we develop a method for systematically searching the space of all AGs that can incorporate non-genetic information in the form of topology constraints. We implement this findGraphs tool within a software package, ADMIXTOOLS 2, which is a reimplementation of the ADMIXTOOLS software with new features and large performance gains. We apply this methodology to identify alternative models to AGs that played key roles in eight published studies and find that graphs modeling more than six populations and two or three admixture events are often not unique, with many alternative models fitting nominally or significantly better than the published one. Our results suggest that strong claims about population history from AGs should only be made when all well-fitting and temporally plausible models share common topological features. Our re-evaluation of published data also provides insight into the population histories of humans, dogs, and horses, identifying features that are stable across the models we explored, as well as scenarios of populations relationships that differ in important ways from models that have been highlighted in the literature, that fit the allele frequency correlation data, and that are not obviously wrong.

Genetic Drift of HIV Populations in Culture

Populations of Human Immunodeficiency Virus type 1 (HIV-1) undergo a surprisingly large amount of genetic drift in infected patients despite very large population sizes, which are predicted to be mostly deterministic. Several models have been proposed to explain this phenomenon, but all of them implicitly assume that the process of virus replication itself does not contribute to genetic drift. We developed an assay to measure the amount of genetic drift for HIV populations replicating in cell culture. The assay relies on creation of HIV populations of known size and measurements of variation in frequency of a neutral allele. Using this assay, we show that HIV undergoes approximately ten times more genetic drift than would be expected from its population size, which we defined as the number of infected cells in the culture. We showed that a large portion of the increase in genetic drift is due to non-synchronous infection of target cells. When infections are synchronized, genetic drift for the virus is only 3-fold higher than expected from its population size. Thus, the stochastic nature of biological processes involved in viral replication contributes to increased genetic drift in HIV populations. We propose that appreciation of these effects will allow better understanding of the evolutionary forces acting on HIV in infected patients.

Developments in prediction theories of the effective size of populations under selection

ABSTRACTThe effective population size is a key parameter in the definition of selection programs, because the magnitude of this parameter determines both the rate of inbreeding and the amount of genetic drift in the population. Prediction of the effective size of selected populations is complicated by the fact that selection has a cumulative effect on the effective size. In the present article, two basic approaches to predict the effective size of populations under selection were summarized, and the interrelation among them was clarified. Several extensions to practical situations relevant to animal breeding, such as non‐random mating, index selection and marker‐assisted selection, were also reviewed.