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Improving genomic prediction for two Yorkshire populations with a limited size using the single‐step method
Author(s) -
Zhou L.,
Wang Y.,
Yu J.,
Yang H.,
Kang H.,
Zhang S.,
Wang C.,
Liu J.
Publication year - 2019
Publication title -
animal genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 81
eISSN - 1365-2052
pISSN - 0268-9146
DOI - 10.1111/age.12806
Subject(s) - purebred , best linear unbiased prediction , population , biology , breed , genomic selection , statistics , population size , trait , selection (genetic algorithm) , genetics , demography , mathematics , genotype , computer science , single nucleotide polymorphism , artificial intelligence , sociology , gene , programming language
Summary In this study, we conducted genomic prediction for two Yorkshire purebred populations (Yichun and Chifeng) from two different provinces of China that both had a limited population size. Two growth traits (age adjusted to 100 kg weight, AGE ; back‐fat thickness adjusted to 100 kg weight, BF ) and one reproduction trait (total number of piglets born, TNB ) were analyzed with four prediction strategies: one‐population BLUP , joint two‐population BLUP , one‐population single‐step BLUP ( SSBLUP ) and joint two‐population SSBLUP . Our results illustrate that accuracies of genomic estimated breeding values were improved for BF and TNB for the Yichun population and for BF for the Chifeng population by genomic prediction (one‐population SSBLUP and joint two‐population SSBLUP ). The accuracy of TNB for the Yichun population was increased two fold when comparing the one‐population SSBLUP to the one‐population BLUP prediction. Meanwhile, prediction biases were dramatically reduced for AGE for the Yichun population and for TNB for the Chifeng population. The conclusions of this study are as follows: first, genomic prediction is useful for improving prediction accuracy for purebred pig breeding farms with a limited population size; second, joint genomic prediction for different populations of the same breed with certain genetic links has the trend to further improve prediction accuracy.