Accuracy of high-density genotype imputation in Japanese Black cattle

Y. Uemoto, S. Sasaki, Y. Sugimoto, T. Watanabe

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Summary Genotype imputation facilitates the identification of missing genotypes on a high-density array using low-density arrays and has great potential for reducing genotyping costs for cattle populations. However, the imputation quality varies across breeds, which have different effective population sizes. Therefore, the accuracy of genotype imputation must be evaluated in each breed. The Japanese Black cattle population has a unique genetic background, and this study aimed to investigate different factors affecting imputation quality in this population. A total of 1368 animals were genotyped using the Illumina BovineHD BeadChip, and the accuracy of imputation was evaluated using information from four lower density arrays. The extent of linkage disequilibrium for this population was relatively higher than that in other beef breeds but lower than that in dairy breeds. The accuracy of arrays with more than 20 000 single nucleotide polymorphisms (SNPs) was similar to or higher than that of lower density arrays. In addition, the minor allele frequency of SNPs in the reference population affected the accuracy. The accuracy increased as the size of the reference population increased, up to 400 animals, beyond which there was little increase. A higher genetic relationship between the reference and test populations increased imputation accuracy. These results indicate that high imputation accuracy can be achieved using high-density arrays, having enough reference animals and including relatives in the reference population.

Original languageEnglish
Pages (from-to)388-394
Number of pages7
JournalAnimal Genetics
Issue number4
Publication statusPublished - 2015 Aug 1
Externally publishedYes


  • bovineHD
  • minor allele frequency
  • proportion and correlation

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Genetics


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