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The dominant factor (76%) is the polygenic background, estimated as a random effect, using the pairwise kinship matrix.
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We tested this using the pairwise phase consistency (PPC) as a measure of spike-field coherence24.
As shown in Figure 4, about 50% of the pairwise kinship estimates were close to 0, indicating that the lines were unrelated.
The majority of the pairwise kinships (82,852; 83.5%) fell below 0.7, while 18,019 (91.8%) of the pairwise kinship coefficients between controls fell below 0.7.
A total of 58.6 % of the pairwise kinship coefficients were zero, and about 24.4%% of the kinship values ranged from 0 (excluding 0) to 0.1.
In contrast, although the mean of the pairwise kinship of SKM was somewhat high value, the mean of pairwise kinship of the putative natural populations and mixed background population, SKM and CCJ, is not significantly different from zero.
Then SPAGeDi software [ 59] was applied to evaluating relative pairwise kinship (K) by 90 SSR markers, and then the pairwise kinship matrix (100 × 100) was produced by the Loiselle coefficient [ 60].
Meanwhile, 53.1% of the pairwise kinship estimates were from 0.8 to 1, indicating that the lines were closely related.
Because four of these clusters did not contain enough individuals, we estimated the pairwise kinship values for only three clusters.
Yu et al. [ 43] developed a mixed linear model (MLM) which incorporates the pairwise kinship (K matrix) to correct for relatedness.
For this analysis, we calculated the pairwise kinship coefficient between all individuals (F ij ) separated at different distance classes following [ 88] up to 10 km.
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