DOI QR코드

DOI QR Code

Genetic Parameter Estimation in Seedstock Swine Population for Growth Performances

  • Choi, Jae Gwan (Animal Breeding and Genetics Division, National Institute of Animal Science) ;
  • Cho, Chung Il (Animal Breeding and Genetics Division, National Institute of Animal Science) ;
  • Choi, Im Soo (Korea Animal Improvement Association) ;
  • Lee, Seung Soo (Animal Breeding and Genetics Division, National Institute of Animal Science) ;
  • Choi, Tae Jeong (Animal Breeding and Genetics Division, National Institute of Animal Science) ;
  • Cho, Kwang Hyun (Animal Breeding and Genetics Division, National Institute of Animal Science) ;
  • Park, Byoung Ho (Animal Breeding and Genetics Division, National Institute of Animal Science) ;
  • Choy, Yun Ho (Animal Breeding and Genetics Division, National Institute of Animal Science)
  • Received : 2012.08.22
  • Accepted : 2012.10.22
  • Published : 2013.04.01

Abstract

The objective of this study was to estimate genetic parameters that are to be used for across-herd genetic evaluations of seed stock pigs at GGP level. Performance data with pedigree information collected from swine breeder farms in Korea were provided by Korea Animal Improvement Association (AIAK). Performance data were composed of final body weights at test days and ultrasound measures of back fat thickness (BF), rib eye area (EMA) and retail cut percentage (RCP). Breeds of swine tested were Landrace, Yorkshire and Duroc. Days to 90 kg body weight (DAYS90) were estimated with linear function of age and ADG calculated from body weights at test days. Ultrasound measures were taken with A-mode ultrasound scanners by trained technicians. Number of performance records after censoring outliers and keeping records pigs only born from year 2000 were of 78,068 Duroc pigs, 101,821 Landrace pigs and 281,421 Yorkshire pigs. Models included contemporary groups defined by the same herd and the same seasons of births of the same year, which was regarded as fixed along with the effect of sex for all traits and body weight at test day as a linear covariate for ultrasound measures. REML estimation was processed with REMLF90 program. Heritability estimates were 0.40, 0.32, 0.21 0.39 for DAYS90, ADG, BF, EMA, RCP, respectively for Duroc population. Respective heritability estimates for Landrace population were 0.43, 0.41, 0.22, and 0.43 and for Yorkshire population were 0.36, 0.38, 0.22, and 0.42. Genetic correlation coefficients of DAYS90 with BF, EMA, or RCP were estimated to be 0.00 to 0.09, -0.15 to -0.25, 0.22 to 0.28, respectively for three breeds populations. Genetic correlation coefficients estimated between BF and EMA was -0.33 to -0.39. Genetic correlation coefficient estimated between BF and RCP was high and negative (-0.78 to -0.85) but the environmental correlation coefficients between these two traits was medium and negative (near -0.35), which describes a highly correlated genetic response to selection on one or the other of these traits. Genetic Trends of all three breeds tend to be towards bigger EMA or greater RCP and shorter DAYS90 especially from generations born after year 2000.

Keywords

References

  1. Arrango, J., I. Misztal, S. Tsuruta, M. Culbertson and W. Herring. 2005. Threshold-linear estimation of genetic parameters for farrowing mortality, litter size, and test performance of large white sows. J. Anim. Sci. 83:499-506.
  2. Chen, P., T. J. Baas, J. W. Mabry, J. C. Dekkers and K. J. Koehler. 2002. Genetic parameters and trends for lean growth rate and its components in U.S. Yorkshire, Duroc, Hampshire, and Landrace pigs.J. Anim. Sci. 80:2062-2070.
  3. Estany, J., D. Villalba, M. Tor, D. Cubilo and J. L. Noguera. 2002. Correlated response to selection for litter size in pigs: II. Carcass, meat, and fat quality traits. J. Anim. Sci. 80:2566-2573.
  4. Fischer, T. M., A. R. Gilmour and J. H. van der Werf. 2004. Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML. Genet. Sel. Evol. 36:363-369. https://doi.org/10.1186/1297-9686-36-3-363
  5. Hicks, C., M. Satoh, K. Ishii, S. Kuroki, T. Fujiwara and T. Furukawa. 1999. Effect of sex on estimates of genetic parameters for daily gain and ultrasonic backfat thickness in swine. Asian-Aust. J. Anim. Sci. 12:677-681. https://doi.org/10.5713/ajas.1999.677
  6. Kim, J. I., Y. G. Sohn, J. H. Jung and Y. I. Park. 2004. Genetic parameter estimates for backfat thickness at three different sites and growth rates in swine. Asian-Aust. J. Anim. Sci. 17:305-308. https://doi.org/10.5713/ajas.2004.305
  7. Li, X. and B. W. Kennedy. 1994. Genetic parameters for growth rate and backfat in Canadian Yorkshire, Landrace, Duroc, and Hampshire pigs. J. Anim. Sci. 72:1450-1454.
  8. Mizstal, I. 2002. REMLF90 Mannual. http://nce.ads.uga.edu/ -ignacy.
  9. Noguera, J. L., L. Varona, D. Babot and J. Estany. 2002. Multivariate analysis of litter size for multiple parities with production traits in pigs: I. Bayesian variance component estimation. J. Anim. Sci. 80:2540-2547.
  10. Petry, D. B., J. W. Holl and R. K. Johnson. 2004. Responses to 19 generations of litter size selection in the NE Index line. II. Growth and carcass responses estimated in pure line and crossbred litters. J. Anim. Sci. 82:1895-1902.
  11. Suzuki, K., M. Ishida, H. Kadowaki, T. Shibata, H. Uchida and A. Nishida. 2006. Genetic correlations among fatty acid compositions in different sites of fat tissues, meat production, and meat quality traits in Duroc pigs. J. Anim. Sci. 84:2026-2034. https://doi.org/10.2527/jas.2005-660

Cited by

  1. Genetic Parameters of Pre-adjusted Body Weight Growth and Ultrasound Measures of Body Tissue Development in Three Seedstock Pig Breed Populations in Korea vol.28, pp.12, 2015, https://doi.org/10.5713/ajas.14.0971
  2. Unravelling the genetic loci for growth and carcass traits in Chinese Bamaxiang pigs based on a 1.4 million SNP array pp.09312668, 2018, https://doi.org/10.1111/jbg.12365
  3. Genetic parameters and trends for production traits and their relationship with litter traits in Landrace and Yorkshire pigs vol.89, pp.10, 2018, https://doi.org/10.1111/asj.13090
  4. Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited revie vol.9, pp.7, 2015, https://doi.org/10.1017/s1751731115000336
  5. Bayes Factor-Based Regulatory Gene Network Analysis of Genome-Wide Association Study of Economic Traits in a Purebred Swine Population vol.10, pp.4, 2013, https://doi.org/10.3390/genes10040293
  6. Analysis of Genetic Parameters of Carcass Traits and Daily Gain of Native Breed Pigs Raised in Poland vol.19, pp.3, 2019, https://doi.org/10.2478/aoas-2019-0018
  7. Estimates of variance components and heritability using different animal models for growth, backfat, litter size, and healthy birth ratio in Large White pigs vol.100, pp.2, 2013, https://doi.org/10.1139/cjas-2019-0136
  8. Genetic Analysis of Major Production and Reproduction Traits of Korean Duroc, Landrace and Yorkshire Pigs vol.11, pp.5, 2013, https://doi.org/10.3390/ani11051321
  9. Genetic relationship between purebred and synthetic pigs for growth performance using single step method vol.34, pp.6, 2013, https://doi.org/10.5713/ajas.20.0261