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In the multiple trait scenario with a null residual correlation, the mean of the approximated CD of contrast for the lowly heritable traits increased from 0.31 in the single-trait evaluation to 0.35 in the multi-trait evaluation with a high genetic correlation and highly heritable trait, increasing the percentage of connected herds from 25.54% to 34.03% (Table 5).
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Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal.
As discussed by [ 16], in the classical genetic evaluation scenario, breeding values of candidate individuals are predicted by fitting multiple trait models (MTM), which neglect the causal network that influences phenotypic traits.
Multiple trait models were fit to estimate genetic covariances among traits.
Chromosomal regions affecting multiple traits (multiple trait quantitative trait regions or MQR) in dairy cattle were detected using a method based on results from single trait analyses to detect quantitative trait loci (QTL).
Multiple trait strategies were generally more powerful and accurate than the single trait technique.
Multiple trait associations ranged from two to five traits per DArT locus.
In a more general scenario, we may have multiple traits recorded in each environment.
Although some of these QTL studies considered multiple traits, they were analysed separately thus neglecting possible genetic correlations between traits.
In order to unravel the genetic architecture of multiple traits of Agaricus bisporus and the genetic correlations among traits, we have investigated a total of six agronomic and quality traits through multi-trait QTL analyses in a mixed-model.
Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex traits.
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