All response variables with the exception of the life-cycle completion and temporal variation analyses had to be log-transformed in order to meet the assumption of homogeneity of variance.
This leads to estimate and to reduce the individual variability as the main variation source; therefore, statistical analyses have more power.
Associations within population subgroups, such as the elderly or children, and disease specific analyses have suggested variation in effects and susceptibilities (Halonen et al. 2008).
Until recently, the high cost of testing for genetic variation has meant that most analyses have concentrated on the study of a limited number of functional genetic variants, primarily SNPs, in specific genes.
Nonsense and missense mutations in the SYN1 gene encoding for Synapsin I have been identified in families segregating for idiopathic epilepsy and ASD and genetic mapping analyses have identified variations in the SYN2 gene as significantly contributing to epilepsy predisposition.
Two variations of gene expression signature analyses have thus far been implemented, distinguished by, primarily, whether the position of the genes in a rank ordered dataset of gene expression differences is taken into account or not.
Transcriptome analyses have detected substantial variation among populations in numerous species, including yeast [ 18], Drosophila[ 19], fish [ 17, 20- 24], and humans [ 25, 26].
These latter analyses have shown how variations in GC and TA skews along chromosomes can arise from differences in the mutation or the repair rates of the leading and the lagging strands.
Phylogenetic and PAQ analyses have revealed a highly dynamic variation of intrapopulation diversity of FMDV quasispecies.
Analyses have been made to determine the variation of live loads with building height.
