title: Multiple-trait genome-wide association study based on principal component analysis for residual covariance matrix.
abstract: 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.
authors.affiliation: Institute of Animal Sciences, Chinese Academy of Agricultural Science, Beijing, People's Republic of China.
authors.forename: H
authors.initials: H
authors.lastname: Gao
authors.affiliation: Institute of Animal Sciences, Chinese Academy of Agricultural Science, Beijing, People's Republic of China.
authors.forename: Y
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
authors.forename: T
authors.initials: T
authors.lastname: Zhang
authors.affiliation: School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, People's Republic of China.
authors.forename: Y
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, People's Republic of China.
authors.forename: L
authors.initials: L
authors.lastname: Jiang
authors.affiliation: Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, People's Republic of China.
authors.forename: J
authors.initials: J
authors.lastname: Zhan
authors.affiliation: Institute of Animal Sciences, Chinese Academy of Agricultural Science, Beijing, People's Republic of China.
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.affiliation: Research Centre for Aquatic Biotechnology, Chinese Academy of Fishery Sciences, Beijing, People's Republic of China.
authors.forename: R
authors.initials: R
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000818:Animals; D002417:Cattle / Q000235:genetics*; D056726:Genetic Association Studies; D008460:Meat; D008957:Models, Genetic; D015999:Multivariate Analysis; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D019655:Quantitative Trait, Heritable