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Inflexible linkage between multiple traits was detected in artificial positive control datasets but not in the C3 C4 dataset.
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Notably, overall correlations between multiple traits are not necessary for multivariate analysis.
SNPs which are associated for multiple traits are defined as hot spots for these traits.
The analysis speed per trait increased significantly when multiple traits were analyzed in one go.
There are also more opportunities for examining correlations between multiple traits in the laboratory versus in the field (e.g., Walsh et al. 2006).
Genome-wide association between markers and traits was decided based on a significance level of 0.05 corrected for multiple tests using the Li and Ji method [ 54].
Multiple correlations between traits were evident in the investigated population.
Natural selection on multiple traits simultaneously is expected to produce genetic correlations between them through linkage disequilibrium (reviewed by McKinnon and Pierotti 2010).
Although some of these QTL studies considered multiple traits, they were analysed separately thus neglecting possible genetic correlations between traits.
An integrated analysis by combining multiple traits can be more powerful than a collection of single-trait analyses and allows a more realistic analysis of the data since genetic correlations between traits can be directly modelled (Malosetti et al. 2008).
Associations between markers and traits were analysed by multiple marker regression.
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