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Multiple trait models were fit to estimate genetic covariances among traits.
By trend, multiple trait model results (based on created discrete herd classes) confirmed the random regression estimates.
Further, these viewers made multiple trait judgments to a single photo, which may increase overlap in these judgments (Rhodes, 2006).
The scheme can be applied to any analysis estimating multiple parameters for multiple species, and can be extended to include multiple trait axes (see Appendix S3).
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.
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For the joint QTL analysis, a multiple trait-multiple interval mapping (MT-MIM) model was evaluated as previously described [ 35].
We identified multiple trait-associated SNPs (diabetes, cholesterol, SLE) that affect multiple trans-genes, and identified novel insights into these associations (PMID: 24798236).
Covariance components were obtained using AIREMLF90, and a multiple-trait animal model.
(Co variance components and related parameters were estimated using REML multiple-trait procedures and animal models with unequal design for different traits.
The modular structures of genotype-phenotype map has also been observed in some classical multiple-trait association studies [17], [18].
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