Dimension reduction and variable selection for genomic selection: application to predicting milk yield in Holsteins.

Abstract excerpt

Genome-assisted prediction of genetic merit of individuals for a quantitative trait requires building statistical models that can handle data sets consisting of a massive number of markers and many fewer observations. Numerous regression models have been proposed in which marker effects are treated as random variables. Alternatively, multivariate dimension reduction techniques [such as principal component...

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