論文

査読有り 筆頭著者 責任著者
2017年10月

Genomic evaluation using SNP- and haplotype-based genomic relationship matrices in a closed line of Duroc pigs

ANIMAL SCIENCE JOURNAL
  • Yoshinobu Uemoto
  • ,
  • Shuji Sato
  • ,
  • Takashi Kikuchi
  • ,
  • Sachiko Egawa
  • ,
  • Kimiko Kohira
  • ,
  • Hironori Sakuma
  • ,
  • Satoshi Miyashita
  • ,
  • Shinji Arata
  • ,
  • Takatoshi Kojima
  • ,
  • Keiichi Suzuki

88
10
開始ページ
1465
終了ページ
1474
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1111/asj.12805
出版者・発行元
WILEY

A simulation analysis and real phenotype analysis were performed to evaluate the impact of three different relationship matrices on heritability estimation and prediction accuracy in a closed-line breeding of Duroc pigs. The numerator relationship matrix (NRM), single nucleotide polymorphism (SNP)-based genomic relationship matrix (GRM) (G(S)), and haplotype-based GRM (GH) were applied in this study. We used PorcineSNP60 genotype array data (38 114 SNPs) of 831 Duroc pigs with four selection traits. In both heritability estimation and prediction accuracy, the accuracy depended on the number of animals with records. For heritability estimation, a large difference in the results among three relationship matrices was not shown, but the trend of the estimated heritabilities between GRMs, that is G(S) < G(H), was shown in this population. For the accuracy of prediction values in test animals, the accuracies of prediction values obtained by two GRMs were higher than that by the NRM in this population. The accuracies obtained by GRMs using animals with no records were lower than that by the NRM using animals with their performance records, but were close to that by the NRM using animals with full-sib testing records.

リンク情報
DOI
https://doi.org/10.1111/asj.12805
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/28557153
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000412089900002&DestApp=WOS_CPL
ID情報
  • DOI : 10.1111/asj.12805
  • ISSN : 1344-3941
  • eISSN : 1740-0929
  • PubMed ID : 28557153
  • Web of Science ID : WOS:000412089900002

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