By comparison, bagging uses the original marker set to build prediction models on bootstrapped samples of lines, and random forests uses randomly sampled linkage markers and bootstrapped samples of lines.
The likely cause of this is random sampling of the very large number of low MAF variants in a relatively small sample of lines.
Figure 1 highlights shape variation along two of these descriptors, sPC1 and sPC2, that together explain >70% of lobe variation in our sample of lines.
Subsequently, phenotyping a representative sample of lines from each group would be sufficient to determine differences in the performance level of the different groups.
Consequently, the associations reported here should be considered indicative and validated in a larger sample of lines before applied in, e.g., breeding programs.
In addition, it is indicated that useful variation in these genes can be identified even within elite breeding lines of maize, although alleles with larger effects on phenotype might be mined from a broader and larger sample of lines.
Subsample data sets were generated in 10 replications in each replication the complete data set was partitioned into five folds, each fold containing an approximately equally sized random sample of lines.
This is comparable to the situation at the PAL locus, at which all informative polymorphisms were in complete LD across ~2.5 kb within an overlapping sample of lines [ 18].
Splenic samples of line 127 mice did not show this reduction in total eIF2α levels (not shown) and thus further characterisation of this observation is required.
Differences between the two established lines became obvious, with tissue samples of line 2 always showing a stronger transcriptional induction of the five target genes, compared with line 1.
A weak signal was evident in the water-treated WT, especially in the interfascicular fibers, but it was missing in the water-treated samples of line 4 and line 5.
