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Through cycles of inter-mating, multiple traits are recombined resulting in an accumulation of favourable genes in the same process.
Figure 2 shows the pattern for just one trait, but the limnetic, benthic, and solitary species are even more distinct when multiple traits are examined together (Schluter and McPhail 1992).
Notably, overall correlations between multiple traits are not necessary for multivariate analysis.
Compared with univariate models, multivariate models can be more powerful in cases where multiple traits are influenced by a common genetic variant.
Resistance alleles that pleiotropically affect multiple traits are probably very common.
SNPs which are associated for multiple traits are defined as hot spots for these traits.
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Genes associated with multiple traits were counted within each relevant category.
The simplest approach to analyse multiple traits is to perform a series of single-trait analyses and then combine the results.
Also, in some cases favorable alleles for multiple traits were detected in the same chromosome regions, thus CSSLs with these chromosome segments are potentially useful breeding materials for simultaneously improving multiple traits.
Many QTLs for multiple traits were identified, such as traits associated with agronomic characteristics (Huang et al. 2010; Zhao et al. 2011; Yang et al. 2014), and with responses to abiotic stresses (Famoso et al. 2011; Pan et al. 2015; Lv et al. 2016), and to biotic stresses (Jia et al. 2012; Wang et al. 2014; Kang et al. 2016; Wang et al. 2015).
When reigning evolutionary theories were formulated in the 19th and 20th Centuries, non-Mendelian events like these that simultaneously affect multiple traits were unknown or ignored.
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