A novel genomic selection method combining GBLUP and LASSO

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• 作者：Hengde Li ; Jingwei Wang ; Zhenmin Bao
• 关键词：Genomic selection ; Genomic best linear unbiased prediction ; Least absolute shrinkage selection operator ; Quantitative trait loci
• 刊名：Genetica
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：143
• 期：3
• 页码：299-304
• 全文大小：365 KB
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• 作者单位：Hengde Li (1) (2)
  Jingwei Wang (1) (3)
  Zhenmin Bao (2)
  
  1. Centre for Applied Aquatic Genomics, Chinese Academy of Fishery Sciences, Beijing, 100141, China
  2. College of Marine Life, Ocean University of China, Qingdao, 266003, China
  3. College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, 350002, Fujian, China
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Life Sciences
  Animal Genetics and Genomics
  Plant Genetics and Genomics
  Human Genetics
  Microbial Genetics and Genomics
  
• 出版者：Springer Netherlands
• ISSN：1573-6857

文摘

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.