Genetic assignment methods for the direct, real‐time estimation of migration rate: a simulation‐based exploration of accuracy and power

Abstract

Genetic assignment methods use genotype likelihoods to draw inference about where individuals were or were not born, potentially allowing direct, real-time estimates of dispersal. We used simulated data sets to test the power and accuracy of Monte Carlo resampling methods in generating statistical thresholds for identifying F0 immigrants in populations with ongoing gene flow, and hence for providing direct, real-time estimates of migration rates. The identification of accurate critical values required that resampling methods preserved the linkage disequilibrium deriving from recent generations of immigrants and reflected the sampling variance present in the data set being analysed. A novel Monte Carlo resampling method taking into account these aspects was proposed and its efficiency was evaluated. Power and error were relatively insensitive to the frequency assumed for missing alleles. Power to identify F0 immigrants was improved by using large sample size (up to about 50 individuals) and by sampling all populations from which migrants may have originated. A combination of plotting genotype likelihoods and calculating mean genotype likelihood ratios (DLR) appeared to be an effective way to predict whether F0 immigrants could be identified for a particular pair of populations using a given set of markers.

Genetic diversity and admixture among Canadian, Mountain and Moorland and Nordic pony populations

“…Individuals were assigned to predefined populations using the 'leave-one-out option' and the maximum likelihood method (Paetkau et al, 1995;Paetkau et al, 2004) as implemented in GENALEX version 6. Structure version 2.1 (Pritchard et al, 2000), with the parameter settings: K10 to 30, 50 000 burn-in, 100 000 Markov chain Monte Carlo (reaching equilibrium) with 10 replicates, was used to assign individuals to inferred clusters.…”

Genetic relationships between Spanish Assaf (Assaf.E) and Spanish native dairy sheep breeds

“…This method shows better assignment performance than frequentist methods or distance-based methods both in simulated or real populations (Cornuet et al, 1999;Arranz et al, 2001). Moverover, using the program GENECLASS 2.0 (Piry et al, 2004), detection of first generation migrants from the native Spanish dairy sheep breeds into the Assaf.E breeds was carried out employing the methodology proposed by Paetkau et al (2004) which seeks the maximization of the ratio of the likelihood computed from the population where the individual was sampled over the highest likelihood value among all population samples including the population where the individual was sampled.…”

“…Two of the individuals incorrectly assigned were considered most likely to be Manchega individuals, and the other 2 were assigned to the Castellana and Awassi breeds, respectively. Additionally, the same 4 incorrectly assigned individuals were detected as first generation migrants from the same sheep breeds into the Assaf.E breed using the methodology described by Paetkau et al (2004).…”

Allometric trajectories of body and head morphology in three sympatric Arctic charr (Salvelinus alpinus (L.)) morphs

“…The genetic assignment was performed with GeneClass2 (Piry et al., 2004), using Bayesian computation (Rannala & Mountain, 1997). Monte–Carlo resampling (Paetkau, Slade, Burden, & Estoup, 2004) using 10,000 simulated individuals and α = .01 was also employed to obtain probabilities of the assignments. The assignment was confirmed by an additional STRUCTURE analysis, using similar settings as above, where all ( N  = 275) individuals were included.…”