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1000 Titel
  • Genomic Prediction Using LD-Based Haplotypes Inferred From High-Density Chip and Imputed Sequence Variants in Chinese Simmental Beef Cattle
1000 Autor/in
  1. Li, Hongwei |
  2. Zhu, Bo |
  3. Xu, Ling |
  4. Wang, Zezhao |
  5. Xu, Lei |
  6. Zhou, Peinuo |
  7. Gao, Han |
  8. Guo, Peng |
  9. Chen, Yan |
  10. Gao, Xue |
  11. Zhang, Lupei |
  12. Gao, Huijiang |
  13. Cai, Wentao |
  14. Xu, Lingyang |
  15. Li, Junya |
1000 Verlag
  • Frontiers Media S.A.
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-07-29
1000 Erschienen in
1000 Quellenangabe
  • 12:665382
1000 Copyrightjahr
  • 2021
1000 Embargo
  • 2022-01-31
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3389/fgene.2021.665382 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8358323/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Abstract/Summary
  • <jats:p>A haplotype is defined as a combination of alleles at adjacent loci belonging to the same chromosome that can be transmitted as a unit. In this study, we used both the Illumina BovineHD chip (HD chip) and imputed whole-genome sequence (WGS) data to explore haploblocks and assess haplotype effects, and the haploblocks were defined based on the different LD thresholds. The accuracies of genomic prediction (GP) for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW) based on haplotype were investigated and compared for both data sets in Chinese Simmental beef cattle. The accuracies of GP using the entire imputed WGS data were lower than those using the HD chip data in all cases. For DP and MP, the accuracy of GP using haploblock approaches outperformed the individual single nucleotide polymorphism (SNP) approach (GBLUP_In_Block) at specific LD levels. Hotelling’s test confirmed that GP using LD-based haplotypes from WGS data can significantly increase the accuracies of GP for RERW, compared with the individual SNP approach (∼1.4 and 1.9% for G<jats:sub>H</jats:sub>BLUP and G<jats:sub>H</jats:sub>BLUP+GBLUP, respectively). We found that the accuracies using haploblock approach varied with different LD thresholds. The LD thresholds (<jats:italic>r</jats:italic><jats:sup>2</jats:sup> ≥ 0.5) were optimal for most scenarios. Our results suggested that LD-based haploblock approach can improve accuracy of genomic prediction for carcass traits using both HD chip and imputed WGS data under the optimal LD thresholds in Chinese Simmental beef cattle.</jats:p>
1000 Sacherschließung
lokal LD
lokal Genetics
lokal haplotype
lokal prediction accuracy
lokal Chinese Simmental beef cattle
lokal genomic prediction
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/TGksIEhvbmd3ZWk=|https://frl.publisso.de/adhoc/uri/Wmh1LCBCbw==|https://frl.publisso.de/adhoc/uri/WHUsIExpbmc=|https://frl.publisso.de/adhoc/uri/V2FuZywgWmV6aGFv|https://frl.publisso.de/adhoc/uri/WHUsIExlaQ==|https://frl.publisso.de/adhoc/uri/WmhvdSwgUGVpbnVv|https://frl.publisso.de/adhoc/uri/R2FvLCBIYW4=|https://frl.publisso.de/adhoc/uri/R3VvLCBQZW5n|https://frl.publisso.de/adhoc/uri/Q2hlbiwgWWFu|https://frl.publisso.de/adhoc/uri/R2FvLCBYdWU=|https://frl.publisso.de/adhoc/uri/WmhhbmcsIEx1cGVp|https://frl.publisso.de/adhoc/uri/R2FvLCBIdWlqaWFuZw==|https://frl.publisso.de/adhoc/uri/Q2FpLCBXZW50YW8=|https://frl.publisso.de/adhoc/uri/WHUsIExpbmd5YW5n|https://frl.publisso.de/adhoc/uri/TGksIEp1bnlh
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1000 Label
1000 Förderer
  1. National Natural Science Foundation of China-Guangdong Joint Fund |
  2. Chinese Academy of Agricultural Sciences |
  3. Beijing Municipal Natural Science Foundation |
1000 Fördernummer
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1000 Förderprogramm
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1000 Dateien
1000 Förderung
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    1000 Förderer National Natural Science Foundation of China-Guangdong Joint Fund |
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    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer Chinese Academy of Agricultural Sciences |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer Beijing Municipal Natural Science Foundation |
    1000 Förderprogramm -
    1000 Fördernummer -
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1000 Erstellt am 2024-05-21T21:03:51.510+0200
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