MLNe: Simulating and estimating effective size and migration rate from temporal changes in allele frequencies

Abstract

In studies of molecular ecology, conservation biology and evolutionary biology, the current or recent effective size (Ne) of a population is frequently estimated from the marker genotype data of two or more temporally spaced samples of individuals taken from the population. Despite the developments of numerous Bayesian, likelihood and moment estimators, only a couple of them can use both temporally and spatially spaced samples of individuals to estimate jointly the effective size (Ne) of and the migration rate (m) into a population. In this note I describe new implementations of these joint estimators of Ne and m in software MLNe which runs on multiple platforms (Windows, Mac, Linux) with or without a graphical user interface (GUI), has an integrated simulation module to simulate genotype data for investigating the impacts of various factors (such as sample size and sampling interval) on estimation precision and accuracy, exploits both Message Passing Interface (MPI) and openMP for parallel computations using multiple cores and nodes to speed up analysis. The program does not require data pre-processing and accepts multiple formats of a file of original genotype data and a file of parameters as input. The GUI facilitates data and parameter inputs and produces publication-quality output graphs, while the non-GUI version of software is convenient for batch analysis of multiple datasets as in simulations. MLNe will help advance the analysis of temporal genetic marker data for estimating Ne of and m between populations, which are important parameters that will help biologists for the conservation management of natural and managed populations. MLNe can be downloaded free from the website http://www.zsl.org/science/research/software/.

Time-Varying Effective Population Sizes of Group-Living Small Mammals

“…Genetic data such as microsatellite and SNP data have been widely used in the studies of effective population size and population genetic structure, often as the sole source of data [12,38,43]. Studies of effective population size with genetic data often assume constant population size, an assumption of the Wright-Fisher idealized population.…”

“…The Wright-Fisher model is still the main theoretic framework for the estimation of effective population size and the gene dynamics of populations [5,38]. The Chesser theory concerning the roles of social structure and coancestry has been developed to explain the outbreeding and excess heterozygosity of social mammals.…”

“…We used the temporal method implemented in computer software MLNe v. 2.1 to estimate the effective population size N e of Daurian pikas [38]. Changes in the allele frequencies of neural loci (i.e., no selection) over time are likely to be caused by genetic drift and inbreeding, which determines effective population size [39].…”

“…Therefore, N e can be estimated using temporal changes in the allelic frequencies with more than two samples [39]. The temporal method is robust to the violations of the assumptions of population spatial structure and age structure and can generate a good estimate of N e with 10-20 microsatellite markers (i.e., neutral loci) [38,39]. We used the maximum likelihood (ML) estimator of N e with three annual samples of Daurian pikas genotyped with 11 microsatellite markers [18].…”