Asian-Australasian Journal of Animal Sciences

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Genomic Heritability of Bovine Growth Using a Mixed Model

  • Ryu, Jihye (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Lee, Chaeyoung (Department of Bioinformatics and Life Science, Soongsil University)
  • Received : 2014.04.17
  • Accepted : 2014.07.08
  • Published : 2014.11.01
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Abstract

This study investigated heritability for bovine growth estimated with genomewide single nucleotide polymorphism (SNP) information obtained from a DNA microarray chip. Three hundred sixty seven Korean cattle were genotyped with the Illumina BovineSNP50 BeadChip, and 39,112 SNPs of 364 animals filtered by quality assurance were analyzed to estimate heritability of body weights at 6, 9, 12, 15, 18, 21, and 24 months of age. Restricted maximum likelihood estimate of heritability was obtained using covariance structure of genomic relationships among animals in a mixed model framework. Heritability estimates ranged from 0.58 to 0.76 for body weights at different ages. The heritability estimates using genomic information in this study were larger than those which had been estimated previously using pedigree information. The results revealed a trend that the heritability for body weight increased at a younger age (6 months). This suggests an early genetic evaluation for bovine growth using genomic information to increase genetic merits of animals.

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References

  1. Davis, M. E. and R. C. M. Simmen. 2006. Genetic parameter estimates for serum insulin-like growth factor I concentrations, and body weight and weight gains in Angus beef cattle divergently selected for serum insulin-like growth factor I concentration. J. Anim. Sci. 84:2299-2308. https://doi.org/10.2527/jas.2005-567
  2. Kim, J. B. and C. Lee. 2000. Historical look at the genetic improvement in Korean cattle. Asian Australas. J. Anim. Sci. 13:1467-1481. https://doi.org/10.5713/ajas.2000.1467
  3. Kim, Y., J. Ryu, J. Woo, J. B. Kim, C. Y. Kim, and C. Lee. 2011. Genome-wide association study reveals five nucleotide sequence variants for carcass traits in beef cattle. Anim. Genet. 42:361-365. https://doi.org/10.1111/j.1365-2052.2010.02156.x
  4. Kim, Y., J. Ryu, and C. Lee. 2014. Replicated association of single-nucleotide marker on chromosome 6 with bovine yearling weight using a mixed model analysis. Anim. Genet. 45:151-153. https://doi.org/10.1111/age.12110
  5. La, B., D. Oh, Y. Lee, S. Shin, C. Lee, E. Chung, and J. Yeo. 2013. Association of bovine fatty acid composition with novel missense nucleotide polymorphism in the thyroid hormone-responsive (THRSP) gene. Anim. Genet. 44:118.
  6. Lee, C. and E. J. Pollak. 1997. Influence of sire misidentification on sire${\times}$year interaction variance and direct-maternal genetic covariance for weaning weight in beef cattle. J. Anim. Sci. 75:2858-2863.
  7. Lee, C., C. P. Van Tassell, and E. J. Pollak. 1997. Estimation of genetic variance and covariance components for weaning weight in Simmental cattle. J. Anim. Sci. 75:325-330.
  8. Lee, C. and E. J. Pollak. 2002. Genetic antagonism between body weight and milk production in beef cattle. J. Anim. Sci. 80:316-321.
  9. Lu, D., S. Miller, M. Sargolzaei, M. Kelly, G. Vander Voort, T. Caldwell, Z. Wang, G. Plastow, and S. Moore. 2013. Genomewide association analyses for growth and feed efficiency traits in beef cattle. J. Anim. Sci. 91:3612-3633. https://doi.org/10.2527/jas.2012-5716
  10. Manolio, T. A., F. S. Collins, N. J. Cox, D. B. Goldstein, L. A. Hindorff, D. J. Hunter, M. I. McCarthy, E. M. Ramos, L. R. Cardon, A. Chakravarti, J. H. Cho, A. E. Guttmacher, A. Kong, L. Kruglyak, E. Mardis, C. N. Rotimi, M. Slatkin, D. Valle, A. S. Whittemore, M. Boehnke, A. G. Clark, E. E. Eichler, G. Gibson, J. L. Haines, T. F. Mackay, S. A. McCarroll, and P. M. Visscher. 2009. Finding the missing heritability of complex diseases. Nature 461:747-753. https://doi.org/10.1038/nature08494
  11. Oh, D., Y. Lee, B. La, J. Yeo, E. Chung, Y. Kim, and C. Lee. 2012a. Fatty acid composition of beef is associated with exonic nucleotide variants of the gene encoding FASN. Mol. Biol. Rep. 39:4083-4090. https://doi.org/10.1007/s11033-011-1190-7
  12. Oh, D., Y. Lee, C. Lee, E. Chung, and J. Yeo. 2012b. Association of bovine fatty acid composition with missense nucleotide polymorphism in exon7 of peroxisome proliferator-activated receptor gamma gene. Anim. Genet. 43:474.
  13. Rolfe, K. M., W. M. Snelling, M. K. Nielsen, H. C. Freetly, C. L. Ferrell, and T. G. Jenkins. 2011. Genetic and phenotypic parameter estimates for feed intake and other traits in growing beef cattle, and opportunities for selection. J. Anim. Sci. 89:3452-3429. https://doi.org/10.2527/jas.2011-3961
  14. Ryoo, H. and C. Lee. 2014. Underestimation of heritability using a mixed model with a polygenic covariance structure in a genome-wide association study for complex traits. Eur. J. Hum. Genet. 22:851-854. https://doi.org/10.1038/ejhg.2013.236
  15. Ryu, J., Y. Kim, C. Kim, J. Kim, and C. Lee. 2012. Association of bovine carcass phenotypes with genes in an adaptive thermogenesis pathway. Mol. Biol. Rep. 39:1441-1445. https://doi.org/10.1007/s11033-011-0880-5
  16. Shin, S., J. Heo, J. Yeo, C. Lee, and E. Chung. 2012. Genetic association of Phosphodiesterase 1B (PDE1B) with carcass traits in Korean cattle. Mol. Biol. Rep. 39:4869-4874. https://doi.org/10.1007/s11033-011-1280-6
  17. Snelling, W. M., M. F. Allan, J. W. Keele, L. A. Kuehn, T. McDaneld, T. P. L. Smith, T. S. Sonstegard, R. M. Thallman, and G. L. Bennett. 2010. Genome-wide association study of growth in crossbred beef cattle. J. Anim. Sci. 88:837-848. https://doi.org/10.2527/jas.2009-2257
  18. VanRaden, P. M. 2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414-4423. https://doi.org/10.3168/jds.2007-0980
  19. Van Tassell, C. P., T. P. L. Smith, L. K. Matukumalli, J. F. Taylor, R. D. Schnabel, C. T. Lawley, C. D. Haudenschild, S. S. Moore, W. C. Warren, and T. S. Sonstegard. 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat. Methods 5:247-252. https://doi.org/10.1038/nmeth.1185
  20. Yang, J., S. H. Lee, M. E. Goddard, and P. M. Visscher. 2011. GCTA: A tool for genome-wide complex trait analysis. The Am. J. Hum. Genet. 88:76-82. https://doi.org/10.1016/j.ajhg.2010.11.011
  21. Zuk, O., E. Hechter, S. R. Sunyaev, and E. S. Lander. 2012. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc. Natl. Acad. Sci. 109:1193-1198. https://doi.org/10.1073/pnas.1119675109
  22. Zhang, Z., X. Ding, J. Liu, Q. Zhang, and D. J. de Koning. 2011. Accuracy of genomic prediction using low-density marker panels. J. Dairy Sci. 94:3642-3650. https://doi.org/10.3168/jds.2010-3917

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