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Recessive, overdominant and codominant models of inheritance showed significant values, but the overdominant model was the best fitting our data.
Recessive, overdominant and codominant models showed significant values, but the overdominant model was the best fitting our data according to AIC and BIC.
(3) Finally, the model best fitting our data were used to compute the percent variance explained (PVE) associated with the QTL.
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Modeltest [24] identified the TrN+I+G model as that which best fitted our data.
We used the software Modeltest version 3.6 [90] to choose the substitution model that best fits our data using the Akaike Information Criterion [91].
Classifying SLC2A4 codons in two classes of purifying selection is a simplification, but we think this is a reasonable assignment that derives from the evolutionary model that best fit our data (Model 3 of Yang [37], supplementary File S1).
The model that best fitted our data involves a substantial bottleneck among non-Africans (Figure 3C, 2≤βOoA≤20 giving the highest ψ1 and the only significant comparison after correction for multiple testing when all values of ψ1 are compared with the ψ1 of the constant size model, P<0.01), rejecting significantly a constant population size model for these populations.
We generated 106 simulated datasets of 20 unlinked DNA sequences (∼1,400 bp each) in 118 sub-Saharan African, 47 European, and 48 East-Asian individuals using the model that best fit our data, i. e. the combination of ranges of parameters determined in the first step of this study.
We found that a Student's t distribution best fit our data.
The one that best fit our data was the gamma distribution.
The evolutionary model that best fitted our data, according to the Akaike Information Criterion, was calculated in jModelTest 0.1.1 [ 50].
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