Similar, perhaps even more striking, cases of collinearity are found in mosses (spanning true mosses) and liverworts (spanning all liverworts; Liu et al. 2011).
High collinearity hinders the selection of markers closest to true QTL positions in linear regression and may result in the selection of pairs of tightly linked loci, with biased effect estimates.
As described by Fitch et al. [ 3], this approach is inherently based on the assumption of collinearity within the target genomes, which may not hold true for bacterial genomes.
To calculate sample size, we made the following assumptions: incidence at the mean for all factors in the model = 0.15, odds ratio determinant = 2.0, odds ratio confounder = 1.6, agreement between measured exposure and true score (validity) = 0.70, correlation between confounder and exposure (multi-collinearity) = 0.30, statistical power = 0.80 and alpha = 0.05.
Our results show that previous studies of segmental collinearity between A. thaliana, Brassica and AK genomes, although very useful, represent over-simplifications of the true inter-relationships of the genomes.
This is not true for CMB-LGO, which only includes sources in a model for which a known source profile exists, though collinearity and exclusion of sources might cause "misplacement" of emissions between the various sources examined.
Our results show that previous studies of segmental collinearity between the A. thaliana, Brassica and ancestral karyotype genomes, although very useful, represent over-simplifications of their true relationships.
This is particularly true in the measurement of physician and medical site levels for the same chronic condition scope, as there may be a collinearity problem in constructing a linear model.
Fox, J. & Monette, G. Generalized collinearity diagnostics.
Synteny and collinearity in plant genomes.
Thus, collinearity is another invariant property.
