Linear multiple regression and correlation analyses reveal several important findings regarding the predictive power of brain volume changes in lesioned animals.
The genetic correlation analyses between several behavioral traits showed an 'L'-type distribution pattern (Fig. 5).
In order to understand the relationship between marker types and densities using KASP or GBS markers for estimating genetic purity and identity, we conducted correlation analyses by creating several subsets of data (Fig. 6).
Bivariate correlation analyses of each quantitative measurement showed several significant correlations with age and baseline mass; therefore, associations between each SNP and quantitative phenotype were assessed using analysis of covariance (ANCOVA) methods.
We identified several quantitative trait loci (QTL) associated with variation in colonization by correlation analyses.
The moving windows consisting of several mapping units were shifted in space and meanwhile used as target areas for correlation analyses.
The possible influence of environmental factors was studied by correlation analyses with external exposure to air pollutants as well as with several lifestyle factors.
Bivariate correlation analyses of microarray expression data showed a strong and consistent positive correlation between LCN2 gene expression and several S100A-genes, with significant correlations in both series with S100A2, S100A3, S100A6, S100A8, S100A9, S100A11, S100A14 and S100A16 (Table 4).
