DOI QR코드

DOI QR Code

Comparison of the estimated breeding value and accuracy by imputation reference Beadchip platform and scaling factor of the genomic relationship matrix in Hanwoo cattle

  • Soo Hyun, Lee (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Chang Gwon, Dang (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Mina, Park (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Seung Soo, Lee (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Young Chang, Lee (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Jae Gu, Lee (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Hyuk Kee, Chang (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Ho Baek, Yoon (Animal Genetics & Breeding Division, National Institute of Animal Science) ;
  • Chung-il, Cho (Hanwoo Genetic Improvement Center, NongHyup Agribusiness Group Inc.) ;
  • Sang Hong, Lee (South Australian Health and Medical Research Institute, University of South Australia) ;
  • Tae Jeong, Choi (Animal Genetics & Breeding Division, National Institute of Animal Science)
  • 투고 : 2022.05.04
  • 심사 : 2022.06.17
  • 발행 : 2022.09.01

초록

Hanwoo cattle are a unique and historical breed in Korea that have been genetically improved and maintained by the national evaluation and selection system. The aim of this study was to provide information that can help improve the accuracy of the estimated breeding values in Hanwoo cattle by showing the difference between the imputation reference chip platforms of genomic data and the scaling factor of the genetic relationship matrix (GRM). In this study, nine sets of data were compared that consisted of 3 reference platforms each with 3 different scaling factors (-0.5, 0 and 0.5). The evaluation was performed using MTG2.0 with nine different GRMs for the same number of genotyped animals, pedigree, and phenotype data. A five multi-trait model was used for the evaluation in this study which is the same model used in the national evaluation system. Our results show that the Hanwoo custom v1 platform is the best option for all traits, providing a mean accuracy improvement by 0.1 - 0.3%. In the case of the scaling factor, regardless of the imputation chip platform, a setting of -1 resulted in a better accuracy increased by 0.5 to 1.6% compared to the other scaling factors. In conclusion, this study revealed that Hanwoo custom v1 used as the imputation reference chip platform and a scaling factor of -0.5 can improve the accuracy of the estimated breeding value in the Hanwoo population. This information could help to improve the current evaluation system.

키워드

과제정보

본 연구는 농촌진흥청 바이오그린 연계 농생명혁신기술개발사업 "국가단위 가축 유전능력평가용 범용 통계분석 패키지 개발(PJ01609901)" 연구과제의 지원 하에 수행되었음.

참고문헌

  1. Falconer DS, Mackay F. 1996. Introdutction to quantitative genetics. Benjamin-Cummings Pub Co., San Francisco, USA.
  2. Fragomeni BO, Lourenco DA, Masuda Y, Legarra A, Misztal I. 2017. Incorporation of causative quantitative trait nucleotides in single-step GBLUP. Genetics Selection Evolution 49:59. https://doi.org/10.1186/s12711-017-0335-0
  3. Jang SB, Kim SY, Lee SH, Shin MG, Kang JM, Lee DH, Kim SD, Noh SH, Lee SH, Choi TJ. 2019. The effect of progeny numbers and pedigree depth on the accuracy of the EBV with the BLUP method. Korean Journal of Agricultural Science 46:579-589. [in Korean] https://doi.org/10.7744/KJOAS.20190043
  4. Lee SH, Werf JVD. 2006. An efficient variance component approach implementing an average information REML suitable for combined LD and linkage mapping with a general complex pedigree. Genetics Selection Evolution 38:25. https://doi.org/10.1186/1297-9686-38-1-25
  5. Legarra A, Christensen OF, Aguilar I, Misztal I. 2014. Single step, a general approach for genomic selection. Livestock Science 166:54-65.
  6. Melka HD, Jeon EK, Kim SW, Han JB, Yoon DH, Kim KS. 2011. Identification of genomic differences between Hanwoo and Holstein breeds using the Illumina Bovine SNP50 BeadChip. Genomics Informatics 9:69-73. https://doi.org/10.5808/GI.2011.9.2.69
  7. Momin MM, Shin JS, Lee S, Truong B, Benyamin B, Lee SH. 2021. A novel method for an unbiased estimate of crossancestry genetic correlation using individual-level data. bioRxiv 2021.
  8. Purcell SB, Neale K, Todd-Brown L, Thomas MA, Ferreira D, Bender J, Maller P, Sklar PI, De Bakker. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81:559-575.
  9. Sargolzaei M, Chesnais JP, Schenkel FS. 2014. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15:478. https://doi.org/10.1186/1471-2164-15-478
  10. Speed D, Cai N, Johnson MR, Nejentsev S, Balding DJ. 2017. Reevaluation of SNP heritability in complex human traits. Nature Genetics 49:986-992. https://doi.org/10.1038/ng.3865
  11. Speed D, Hemani G, Johnson MR, Balding DJ. 2012. Improved heritability estimation from genome-wide SNPs. The American Journal of Human Genetics 91:1011-1021. https://doi.org/10.1016/j.ajhg.2012.10.010
  12. Teo YY, Inouye M, Small KS, Gwilliam R, Deloukas P, Kwiatkowski DP, Clark TG. 2007. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics 23:2741-2746. https://doi.org/10.1093/bioinformatics/btm443
  13. VanRaden PM. 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science 91:4414-4423. https://doi.org/10.3168/jds.2007-0980
  14. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath PA, Martin NG, Montgomery GW. 2010. Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42:565-569. https://doi.org/10.1038/ng.608