The migration model is linked to a genetics model calculating gene frequency changes based on propagule deposition for potential duck landing sites along the European migration pathways.
Population geneticists pursue their goals by developing abstract mathematical models of gene frequency dynamics, trying to extract conclusions from those models about the likely patterns of genetic variation in actual populations, and testing the conclusions against empirical data.
By studying formal models of gene frequency change, population geneticists therefore hope to shed light on the evolutionary process, and to permit the consequences of different evolutionary hypotheses to be explored in a quantitatively precise way.
Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL.
Thus in this case, the primary difference between the two models is that in the gene frequency model residual variances are heterogeneous (see equation 18), whereas in the regression model residual variances are assumed homogeneous.
This model predicts that gene frequency distributions possess a characteristic U-shape even in the absence of selective forces driving genome and population structure.
The parameters in this two-allele model include the gene frequency at the linked QTL for each marker haplotype in the founders and the additive effect of the QTL [ 22, 26].
We use models of gene frequencies, typical of population genetics, to investigate long-term outcomes of coevolution over time-scales of hundreds or thousands of generations; this means that epidemiological and life-cycle parameters are not specified explicitly but absorbed in the four fitness costs.
Another difference between these two models is the assumption of biallelic QTL in the gene frequency model.
Precision to map a QTL using the gene frequency model (BGF) and the least squares regression model (LSR) with one marker (BGF1, LSR1) or two flanking markers (BGF2, LSR2) for different variances explained by the QTL (% of phenotypic variance), marker spacing, and sample size.
