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We applied to multivariate phenotype based GWAS to relations between plasma lipid levels for an in-depth study.
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Lange et al. [37] has proposed a natural extension of the FBAT statistics which are based on the general likelihood-score test approach [38] to multivariate phenotypes based on GEE scores.
Our work emphasize that multivariate phenotypes based GWAS can identify pleiotropic genes that share common etiology pathways.
The multivariate model we developed characterizes a "healthy aging" phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult 65-699 years of age).
This methodology applied to the GWAS for multivariate phenotype highLDLhighTG derived from the predicted patterns of the phenotypic associations.
CCA applied to test for association between individual SNPs and the multivariate phenotype performed badly.
The mORpS measures the strength of association of a multivariate phenotype against those of single traits.
The multivariate phenotype can be a conjunction of predicates of single traits, as expressed one or more association rules.
An association rule expresses association of single traits X and Y and can be derived to a multivariate phenotype.
Dang and Serfling [3] introduced nonparametric multivariate outlier identifiers based on multivariate depth functions.
Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals.
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