title: Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese-Nordic reference population.
abstract: This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows genotyped with 54K chip, 510 Nordic Holstein bulls genotyped with HD chip, and 4398 Nordic Holstein bulls genotyped with 54K chip and with deregressed proofs for five milk production traits. Based on these data, the accuracy of imputation from 54K to HD marker data and the accuracy of genomic predictions in Chinese Holstein were assessed. The allele correct rate increased around 2.7 and 1.7% in imputation from the 54K to the HD marker data for Chinese Holstein bulls and cows, respectively, when the Nordic HD-genotyped bulls were included in the reference data for imputation. However, the prediction accuracy was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy of genomic predictions increased 6.5 percentage points together with a reduction of prediction bias. The HD markers did not outperform the 54K markers in genomic prediction based on the present data. The results indicate that for Chinese Holsteins, it is necessary to genotype more individuals with 54K chip to increase reference population rather than increasing marker density.
authors.affiliation: Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark; Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
authors.forename: P
authors.initials: P
authors.lastname: Ma
authors.forename: M S
authors.initials: MS
authors.lastname: Lund
authors.forename: X
authors.initials: X
authors.lastname: Ding
authors.forename: Q
authors.initials: Q
authors.lastname: Zhang
authors.forename: G
authors.initials: G
authors.lastname: Su
mesh_terms: D000818:Animals; D001823:Body Composition / Q000235:genetics; D001947:Breeding; D002417:Cattle / Q000033:anatomy & histology / Q000235:genetics* / Q000378:metabolism; D003612:Dairying; D005819:Genetic Markers; D005838:Genotype; D008892:Milk / Q000378:metabolism; D008957:Models, Genetic; D012984:Software
chemical_list: D005819:Genetic Markers
keywords: Genomic prediction; high-density markers; imputation; reference population