畜牧兽医学报2017，Vol.48Issue(12)：2258-2267,10.DOI:10.11843/j.issn.0366-6964.2017.12.005
基于GBLUP与惩罚类回归方法的猪血液性状基因组选择研究
A Study of Genomic Selection on Porcine Hematological Traits Using GBLUP and Penalized Regression Methods
摘要
Abstract
This study aimed to explore the application of GBLUP and penalized regression methods in genomic selection of the hematological traits in pigs.We chose 13 hematological traits from the immune resource population collected by our laboratory as the analyzed objects.We used the genotyping data of Illumina PorcineSNP60 Genotyping Beadchip to conduct the genomic selection analysis,in which GBLUP and 3 penalized regression methods (ridge,lasso and elastic-net) were used based on additive model and additive-dominance model.The results showed that the accuracy of genomic selection was positively correlated with estimated values of chip heritabilities of traits.The results of cross-validation analysis showed that the MCV (mean corpuscular volume) had the highest prediction accuracy among 13 hematological traits.The prediction accuracy of additive model and additive-dominance model were different in different traits.In total trend,the prediction accuracy of the lasso and elastic-net regressions were lower than that of the ridge regression and GBLUP.But in a few traits,such as NE％,it was opposite.In conclusion,there is no optimal genomic prediction method that could be suitable for all traits,and we should consider the genetic characteristics of the target traits when choosing a genome evaluation method.This research provides important reference information for the practical application of genomic selection for immune traits in pigs.关键词
猪/血液性状/基因组选择/GBLUP/惩罚类回归Key words
porcine/hematological trait/genomic selection/GBLUP/penalized regression分类
农业科技引用本文复制引用
张巧霞,张玲妮,刘飞,刘向东,刘小磊,赵书红,朱猛进..基于GBLUP与惩罚类回归方法的猪血液性状基因组选择研究[J].畜牧兽医学报,2017,48(12):2258-2267,10.基金项目
国家自然科学基金面上项目(31372302 （）
31672392) （）
国家高技术研究发展计划(2013AA102502) （2013AA102502）
湖北省公益性科技研究项目(2012DBA25001) （2012DBA25001）
国家生猪产业技术体系项目(CARS-35) （CARS-35）
