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Heterogeneity between the risk estimates was assessed using unadjusted logistic regression models in case-case analyses.
Statistical significance of Fst estimates was assessed using software Arlequin with 10,000 permutations.
Statistical significance of the differences in Kaplan-Meier estimates was assessed using the log-rank test.
The significance of parameter estimates was assessed using 95% Bayesian credibility intervals.
The significance of parameter estimates was assessed using the Wald test.
Goodness of fit of parameter estimates was assessed using 85%% confidence intervals and Ω02 for the global model [ 34, 35].
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Individual ancestry estimates were assessed using an unsupervised assignment of individuals to populations by means of STRUCTURE 2.2 [48].
p-Values for the heritability estimates were assessed using a Bonferroni-corrected p threshold (0.05/4,924 or 4,787 = 1.0 × 10 5).
The influence that this had on the effect estimate was assessed using the change in the mean squared error.
The accuracy of the forest height estimated is assessed using ground-based measurements.
The predictive power of the measured and estimated deviations was assessed using the receiver operating characteristic (ROC) curves that characterize the relative trade-off between the true positive rate (sensitivity) and the false positive rate (1 - specificity).
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