Coupled with rapid developments of efficient genetic markers, powerful population genetics methods were proposed to estimate migration rates (m) in natural populations in much broader spatial and temporal scales than the traditional mark-release-recapture (MRR) methods. Highly polymorphic (e.g. microsatellites) and genomic wide (e.g. SNPs) markers provide sufficient information to assign individuals to their populations or parents of origin, and thereby to estimate directly m in a way similar to MRR. Such direct estimates of current migration rates are particularly useful in understanding the ecology and microevolution of wild populations and in managing the populations in the future. In this study I proposed and implemented, in the software MigEst, a likelihood method to use marker based parentage assignments in jointly estimating m and candidate parent sampling proportions (x) in a subset of populations, investigated its power and accuracy using data simulated in various scenarios of population properties (e.g. the actual m, number, size and differentiation of populations) and sampling properties (e.g. the numbers of sampled parent candidates, offspring, and markers), compared it with the population assignment approach implemented in the software BayesAss, and demonstrated its usefulness by analysing a microsatellite dataset from three natural populations of Brazilian bats. Simulations showed that MigEst provides unbiased and accurate estimates of m, and performs better than BayesAss except when populations are highly differentiated with very small and ecologically insignificant migration rates. A valuable property of MigEst is that in the presence of unsampled populations, it gives good estimates of the rate of migration among sampled populations as well as of the rate of migration into each sampled population from the pooled unsampled populations.