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After removing extremely high outlier values (those that lie outside the 97.5% upper quartile), the estimated switching rates were found to be significantly negatively correlated with the input trait correlation values (for q1,0, F1,9748 = 135.7, P < 2.2 × 10 16; for q0,1, F1,9748 = 177.4, P < 2.2 × 10 16; see also Fig. S1B,C).
Under the assumption that genes with trait correlated expression levels belong to pathways or networks relevant for the control of the trait, correlation analysis of microarray expression data and records of WHC, measured as drip loss, reveals a list of functional candidate genes.
Genotypic correlations of traits with seed yield were greater than their respective phenotypic correlations across environments indicating the greater contribution of genotypic factors to the trait correlation.
Trait variance-covariance matrices were converted to trait correlation matrices by dividing covariances by respective variances.
These steps produce a 32×32 trait correlation matrix for each species.
Trait correlation matrices for each species were compared to that of every other species, using matrix correlation analysis.
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In general, trait correlations were weak yet stronger in H. rubicundus than in H. pluvialis.
We discuss implications of these findings for the evolutionary causes and consequences of trait correlations.
Further, many trait correlations have a polygenic basis, suggesting that both pleiotropy and linkage likely contribute.
Here, we review studies of the genetic basis of trait correlations to ask: What has this new information taught us?
Since this theory was initially developed, technology has enabled the identification of specific genetic variants that contribute to trait correlations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com