sandwrm
In most natural populations, individuals in close proximity are more related on average than those at greater distances; this pattern gives rise to geographic population genetic structure. Despite extensive theoretical work on spatial population genetics, few empirical methods exist to estimate the components of theoretical models of genetic relatedness in continuous space. One classic model of relatedness in continuous space is the Wright-Malécot model, which predicts that the probability of identity-by-descent decays as a function of geographic distances. The shape of this decay curve is dictated by the dynamics of local dispersal and mating, as well as population density. This model can be reformulated to describe the probability of identity-by-state, in which case it decays to an asymptote, the value of which is determined by the historical demography of the population. Collectively, these features can be modeled in a likelihood-based framework to estimate neighborhood size and long-term diversity from pairwise genetic and geographic distance.
My postdoc Zach Hancock and I developed an implementation of this model in the R package sandwrm. You can read more about the population genetic model in our paper here, and more about the implementation here. The package can be found at Zach's github, which includes installation instructions and tutorial walk-throughs.
In most natural populations, individuals in close proximity are more related on average than those at greater distances; this pattern gives rise to geographic population genetic structure. Despite extensive theoretical work on spatial population genetics, few empirical methods exist to estimate the components of theoretical models of genetic relatedness in continuous space. One classic model of relatedness in continuous space is the Wright-Malécot model, which predicts that the probability of identity-by-descent decays as a function of geographic distances. The shape of this decay curve is dictated by the dynamics of local dispersal and mating, as well as population density. This model can be reformulated to describe the probability of identity-by-state, in which case it decays to an asymptote, the value of which is determined by the historical demography of the population. Collectively, these features can be modeled in a likelihood-based framework to estimate neighborhood size and long-term diversity from pairwise genetic and geographic distance.
My postdoc Zach Hancock and I developed an implementation of this model in the R package sandwrm. You can read more about the population genetic model in our paper here, and more about the implementation here. The package can be found at Zach's github, which includes installation instructions and tutorial walk-throughs.