For a set that contains k markers, we consider the cross tabulated Table 2 that contains frequency counts of all unique pairwise comparisons among the k markers.
To increase the information value of this index with respect to the resolution capacity of the respective molecular markers, we consider the nodes that appear in the trees inferred from BI and ML, where nodes below 50% were collapsed.
Since no traces of hidden population substructure were found in Poles on the level of autosomal markers, we consider our results reliable and representative of the general population of Poland.
The potential predictive marker we considered was gene amplification of ACTN4.
In setting the optimal significance threshold for each marker, we consider the rate of return, or power, which depends on the amount invested, or significance threshold.
Given the large standard deviations and confidence intervals, as well as the fact that estimates were based on a single marker, we consider our results sufficient to indicate that gene flow along the Wild Coast is not unidirectional, but insufficient to obtain any more accurate estimates.
Despite the notable differences between the DTHK and ITS results regarding the pairwise ΦST (see Additional file 2), which are easily observed in the MDS plot, one a posteriori grouping was consistently supported by both markers when we considered the hierarchical AMOVA outcomes (Table 2).
To investigate the effect of high, medium, and low marker density, we considered distances between two adjacent markers of 1 cM, 2 cM, or 5 cM (md = 1, 2, 5).
When investigating potential modifications for lung cancer risk by marker genotypes, we considered sex, age and smoking habits as covariates.
