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Olson et al. [ 26] found a general increase of 2% from pooling U.S. and Canadian H populations and 5% for the Brown Swiss from European countries when using multiple trait methodology.
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Direct and correlated responses to 11 years of selection were estimated using BLUP methodology applied to a multiple trait animal model.
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.
Multiple trait CIM takes into account the correlated structure of phenotypic traits to map QTL affecting multiple traits (Jiang and Zeng 1995).
Applying SEM-related methodologies to QTL analysis and gene mapping with multiple traits not only allows inference regarding causal relationships among phenotypes, but it also enhances detection power and precision of estimates, with the additional advantage of a distinction between direct and indirect genetic effects of QTL on each trait [ 12].
Although some of these QTL studies considered multiple traits, they were analysed separately thus neglecting possible genetic correlations between traits.
Jointly modeling multiple traits' genetic profiles has provided insights into the shared genetic basis of many complex 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.
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