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Genetica

, Volume 143, Issue 3, pp 299–304 | Cite as

A novel genomic selection method combining GBLUP and LASSO

  • Hengde Li
  • Jingwei Wang
  • Zhenmin Bao
Article

Abstract

Genetic prediction of quantitative traits is a critical task in plant and animal breeding. Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNP). In the framework of linear mixed models, we extended genomic best linear unbiased prediction (GBLUP) by including additional quantitative trait locus (QTL) information that was extracted from high-throughput SNPs by using least absolute shrinkage selection operator (LASSO). GBLUP was combined with three LASSO methods—standard LASSO (SLGBLUP), adaptive LASSO (ALGBLUP), and elastic net (ENGBLUP)—that were used for detecting QTLs, and these QTLs were fitted as fixed effects; the remaining SNPs were fitted using a realized genetic relationship matrix. Simulations performed under distinct scenarios revealed that (1) the prediction accuracy of SLGBLUP was the lowest; (2) the prediction accuracies of ALGBLUP and ENGBLUP were equivalent to or higher than that of GBLUP, except under scenarios in which the number of QTLs was large; and (3) the persistence of prediction accuracy over generations was strongest in the case of ENGBLUP. Building on the favorable computational characteristics of GBLUP, ENGBLUP enables robust modeling and efficient computation to be performed for genomic selection.

Keywords

Genomic selection Genomic best linear unbiased prediction Least absolute shrinkage selection operator Quantitative trait loci 

Notes

Acknowledgments

This project was supported financially by the National High-Tech R&D Program of China (863 program) (Grant No. 2012AA10A402).

Conflict of interest

None.

Supplementary material

10709_2015_9826_MOESM1_ESM.docx (443 kb)
Supplementary material 1 (DOCX 442 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Centre for Applied Aquatic GenomicsChinese Academy of Fishery SciencesBeijingChina
  2. 2.College of Marine LifeOcean University of ChinaQingdaoChina
  3. 3.College of Animal ScienceFujian Agriculture and Forestry UniversityFuzhouChina

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