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Frontiers of Agricultural Science and Engineering

Front. Agr. Sci. Eng.    2017, Vol. 4 Issue (3) : 268-278
Statistical considerations for genomic selection
Huimin KANG, Lei ZHOU, Jianfeng LIU()
National Engineering Laboratory for Animal Breeding/Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture/College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Genomic selection is becoming increasingly important in animal and plant breeding, and is attracting greater attention for human disease risk prediction. This review covers the most commonly used statistical methods and some extensions of them, i.e., ridge regression and genomic best linear unbiased prediction, Bayesian alphabet, and least absolute shrinkage and selection operator. Then it discusses the measurement of the performance of genomic selection and factors affecting the prediction of performance. Among the measurements of prediction performance, the most important and commonly used measurement is prediction accuracy. In simulation studies where true breeding values are available, accuracy of genomic estimated breeding value can be calculated directly. In real or industrial data studies, either training-testing approach or k-fold cross-validation is commonly employed to validate methods. Factors influencing the accuracy of genomic selection include linkage disequilibrium between markers and quantitative trait loci, genetic architecture of the trait, and size and composition of the training population. Genomic selection has been implemented in the breeding programs of dairy cattle, beef cattle, pigs and poultry. Genomic selection in other species has also been intensively researched, and is likely to be implemented in the near future.

Keywords genomic estimated breeding value      genomic selection      linkage disequilibrium      statistical methods     
Corresponding Authors: Jianfeng LIU   
Just Accepted Date: 30 June 2017   Online First Date: 24 July 2017    Issue Date: 12 September 2017
 Cite this article:   
Huimin KANG,Lei ZHOU,Jianfeng LIU. Statistical considerations for genomic selection[J]. Front. Agr. Sci. Eng. , 2017, 4(3): 268-278.
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Fig.1  Implementation of genomic selection. To implement genomic selection, a reference population should be constructed, in which individuals are genotyped and phenotyped. Based on the reference population, genomic estimated breeding values (GEBV) are obtained by using statistical methods for candidate individuals only having genotypic information. Individuals are selected according to the rank of their GEBV.
Fig.2  Annual number of citations of reference[3], the rate of genetic improvement in milk production[96], and the number of Holstein cows chip-genotyped by December of each year from the Council for Dairy Cattle Breeding database ( Adapted from reference[97].
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