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Detecting traits like confusion could be really important to educational and training apps, preventing important concepts from escaping users.
However, while these technologies are excellent in detecting traits in populations and revealing population structure [ 36], they are less informative in defining introgression borders and their content.
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By examining the likelihood of detecting trait-mediated effects under large variations in experimental design, our study demonstrates that trait-mediated effects are likely to have pervasive and powerful influences in nature.
With the great progresses in the methods of detecting trait differences and genetic variations, GWAS has emerged as a promising approach to inspect the relationships between complex genetic polymorphisms and distinctive phenotypes for most species (Slate et al. 2009).
Indeed, by correcting the structure effect, there is a risk of not being able to detect traits that would have a distribution superimposed on the population structure [ 39].
Several preliminary analyses considering the whole set of lipid-related phenotypes (including muscle content of individual fatty acids and several carcass fat measures) allowed us to detect traits with either low variability or displaying high correlations with other ones.
We identified a total of 32 new loci associated with flowering time and with ten grain-related traits, indicating that the larger sample increased the power to detect trait-associated variants using GWAS.
By utilizing the same SNP panel across a wide array of canine breeds it is possible to detect trait-associated SNPs in breeds not thought to carry those traits.
This type of approach would offer advantages over the standard approach looking at a single trait including the ability to look for shared genetic risks directly, the ability to detect trait-trait association, and overall lower cost.
We examine the ability of MANOVA to detect trait-associated genetic loci in the presence of gene-gene interactions.
MANOVA and the Bayesian nonparametric approach show good ability to detect trait-associated genetic variants under various possible genetic models.
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