期刊
JOURNAL OF ANIMAL SCIENCE
卷 96, 期 5, 页码 2016-2026出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/jas/sky071
关键词
animal breeding; dominance and additive effects; genomic selection; genetic markers; sparse neural networks
资金
- National Natural Science Foundation of China [31772844, U1706203, 11771014]
- Fundamental Research Funds for the Central Universities [201762001, 201564009]
Neural networks (NNs) have emerged as a new tool for genomic selection (GS) in animal breeding. However, the properties of NN used in GS for the prediction of phenotypic outcomes are not well characterized due to the problem of over-parameterization of NN and difficulties in using whole-genome marker sets as high-dimensional NN input. In this note, we have developed an R package called snnR that finds an optimal sparse structure of a NN by minimizing the square error subject to a penalty on the L-1 -norm of the parameters (weights and biases), therefore solving the problem of over-parameterization in NN. We have also tested some models fitted in the snnR package to demonstrate their feasibility and effectiveness to be used in several cases as examples. In comparison of snnR to the R package brnn (the Bayesian regularized single layer NNs), with both using the entries of a genotype matrix or a genomic relationship matrix as inputs, snnR has greatly improved the computational efficiency and the prediction ability for the GS in animal breeding because snnR implements a sparse NN with many hidden layers.
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