Data from: Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix

Given the drawbacks of implementing multivariate analysis for mapping
multiple traits in genome-wide association study (GWAS), principal
component analysis (PCA) has been widely used to generate independent
‘super traits’ from the original multivariate phenotypic traits for the
univariate analysis. However, parameter estimates in this framework may
not be the same as those from the joint analysis of all traits, leading to
spurious linkage results. In this paper, we propose to perform the PCA for
residual covariance matrix instead of the phenotypical covariance matrix,
based on which multiple traits are transformed to a group of pseudo
principal components. The PCA for residual covariance matrix allows
analyzing each pseudo principal component separately. In addition, all
parameter estimates are equivalent to those obtained from the joint
multivariate analysis under a linear transformation. However, a fast least
absolute shrinkage and selection operator (LASSO) for estimating the
sparse oversaturated genetic model greatly reduces the computational costs
of this procedure. Extensive simulations show statistical and
computational efficiencies of the proposed method. We illustrate this
method in a GWAS for 20 slaughtering traits and meat quality traits in
beef cattle.