Validation of selection accuracy for the total number of piglets born in Landrace pigs using genomic selection

  • Oh, Jae-Don (Department of Animal Biotechnology, Chonbuk National University) ;
  • Na, Chong-Sam (Department of Animal Biotechnology, Chonbuk National University) ;
  • Park, Kyung-Do (Department of Animal Biotechnology, Chonbuk National University)
  • Received : 2016.05.19
  • Accepted : 2016.08.10
  • Published : 2017.02.01


Objective: This study was to determine the relationship between estimated breeding value and phenotype information after farrowing when juvenile selection was made in candidate pigs without phenotype information. Methods: After collecting phenotypic and genomic information for the total number of piglets born by Landrace pigs, selection accuracy between genomic breeding value estimates using genomic information and breeding value estimates of best linear unbiased prediction (BLUP) using conventional pedigree information were compared. Results: Genetic standard deviation (${\sigma}_a$) for the total number of piglets born was 0.91. Since the total number of piglets born for candidate pigs was unknown, the accuracy of the breeding value estimated from pedigree information was 0.080. When genomic information was used, the accuracy of the breeding value was 0.216. Assuming that the replacement rate of sows per year is 100% and generation interval is 1 year, genetic gain per year is 0.346 head when genomic information is used. It is 0.128 when BLUP is used. Conclusion: Genetic gain estimated from single step best linear unbiased prediction (ssBLUP) method is by 2.7 times higher than that the one estimated from BLUP method, i.e., 270% more improvement in efficiency.


Supported by : Rural Development Administration


  1. VanRaden PM, Van Tassell CP, Wiggans GR, et al. Invited review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci 2009;92:16-24.
  2. Christensen OF, Madsen P, Nielsen B, Ostersen T, Su G. Single-step methods for genomic evaluation in pigs. Animal 2012;6:1565-71.
  3. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001;157: 1819-29.
  4. Gengler, N, Abras S, Verkenne C, et al. Accuracy of prediction of gene content in large animal populations and its use for candidate gene detection and genetic evaluation. J Dairy Sci 2008;91:1652-59.
  5. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci 2008;91:4414-23.
  6. Misztal I, Legarra A, Aguilar I. A relationship matrix including full pedigree and genomic information. J Dairy Sci 2009;92:4656-63.
  7. Liu M, Goddard ME, Reinhardt F, Reent R. A single-step genomic model with direct estimation of marker effects. J Dairy Sci 2014; 97:5833-50.
  8. Christensen OF, Lund MS. Genomic prediction when some animals are not genotyped. Genet Sel Evol 2010;42:1.
  9. Su G, Lund MS, Sorensen D. Selection for litter size at day five to improve litter size at weaning and piglet survival rate. J Anim Sci 2007;85:1385-92.
  10. Chen P, Baas TJ, Mabry JW, Koehler KJ, Dekkers JC. Genetic parameters and trends for litter traits in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs. J Anim Sci 2003;81:46-53.
  11. Lee JH, Song KD, Lee HK, et al. Genetic parameters of reproductive and meat quality traits in Korean Berkshire pigs. Asian- Australas J Anim Sci 2015;28:1388-93.
  12. Arango J, Misztal I, Tsuruta S, Culbertson M, Herring W. Threshold- linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of Large White sows. J Anim Sci 2005;83:499-506.
  13. Forni S, Aguilar I, Misztal I. Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Sel Evol 2011;43:1.