Table 1.
Parameters and the associated definitions for equation 1 and 2.
Table 2.
For each allele the composition of the datasets.
Table 3.
Comparison of the analysis methods used for identifying phenodeviants.
Figure 1.
An overview of the mixed method methodology implemented.
The process can be summarised as a top down methodology involving six steps to build a mixed model to query the phenotyping data.
Figure 2.
Examining control data to assess batch to batch variation.
Representative time course plot showing the batch to batch variation in control data for male mice from a B6Brd;B6N-Tyrc-Brd genetic background. Example shown is the variation seen in the fat mass variable measured in grams. For each day, data was collected a box plot is drawn as a five point summary indicating the minimum, 1st quartile, median, 3rd quartile and maximum. The global median fat mass value is shown with a black solid line.
Figure 3.
Assessing distribution of batch for a variable.
Representative Normal Q-Q plots of the distribution of the mean for a batch in control data for male mice from a B6Brd;B6N-Tyrc-Brd genetic background. A: weight, B: bone mineral density and C: lean mass.
Figure 4.
Examining control data to assess variation in standard deviation with batch.
Representative time course plot showing the variation in standard deviation with batch in control data for mice from a B6Brd;B6N-Tyrc-Brd genetic background. Example shown is the variation seen in the lean mass variable for male mice measured in grams. The global median is shown with a black solid line, and the 95% confidence interval is shown with dotted lines.
Table 4.
Assumptions associated with the mixed model.
Table 5.
A comparison of phenotypic calls.