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The data for growth traits were analyzed using a univariate mixed model to estimate breeding values, variance components and response to selection.
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With BLUPs of each tocochromanol trait, we conducted a GWAS with 294,092 genome-wide SNPs using a univariate unified mixed linear model (Yu et al. 2006) that eliminated the need to recompute variance components (i.e., population parameters previously determined, or P3D) (Zhang et al. 2010) in the Genome Association and Prediction Integrated Tool package (Lipka et al. 2012).
Interactions between NRT threshold and demographical, clinical, and technical (of the CI) parameters were analyzed using a univariate linear mixed model to account for correlation between the repeated measurements of each subject (measurements of 22 different electrodes in the same subject, causing within-subject SD).
Using a univariate approach these parameters were optimized.
First, the PLE conditions were optimized using a univariate approach.
Experimental parameters were optimized using a univariate experimental design.
Initially, we examined these variables using a univariate analysis.
We propose the Intersection method that is simple to implement, using a univariate hotspot detection method.
Factors associated with ESBL acquisition were picked up using a univariate analysis.
All variables regarding clinical and imaging CVT characteristics were compared using a univariate analysis (χ2 test).
This is usually made using a univariate approach, which demands a large number of experiments, being time and money consuming.
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