A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

ABSTRACT: One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "

SUBMITTER: Jacquin L 

PROVIDER: S-EPMC4977290 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature