  With novel developments in sequencing technologies, time-sampled data are
becoming more available and accessible. Naturally, there have been efforts in
parallel to infer population genetic parameters from these datasets. Here, we
compare and analyze four recent approaches based on the Wright-Fisher model for
inferring selection coefficients (s) given effective population size (Ne), with
simulated temporal datasets. Furthermore, we demonstrate the advantage of a
recently proposed ABC-based method that is able to correctly infer genome-wide
average Ne from time-serial data, which is then set as a prior for inferring
per-site selection coefficients accurately and precisely. We implement this ABC
method in a new software and apply it to a classical time-serial dataset of the
medionigra genotype in the moth Panaxia dominula. We show that a recessive
lethal model is the best explanation for the observed variation in allele
frequency by implementing an estimator of the dominance ratio (h).
