DOI QR코드

DOI QR Code

Evaluation of Optimum Genetic Contribution Theory to Control Inbreeding While Maximizing Genetic Response

  • Oh, S.H.
  • Received : 2011.09.05
  • Accepted : 2012.01.09
  • Published : 2012.03.01

Abstract

Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.

Keywords

Genetic Contribution Theory;Genetic Response;Breeding Value

References

  1. Ameli, H., D. K. Flock and P. Glodek. 1991. Cumulative inbreeding in commercial White Leghorn lines under long-term reciprocal recurrent selection. Br. Poult. Sci. 32:439-449. https://doi.org/10.1080/00071669108417369
  2. Falconer, D. S. 1960. Introduction to Quantitative Genetics.
  3. Gulisija, D., D. Gianola and K. A. Weigel. 2007. Nonparametric analysis of the impact of inbreeding on production in Jersey cows. J. Dairy Sci. 90:493-500. https://doi.org/10.3168/jds.S0022-0302(07)72651-6
  4. Hinrichs, D., M. Wetten and T. H. E. Meuwissen. 2006. An algorithm to compute optimal genetic contributions in selection programs with large numbers of candidates. J. Anim. Sci. 84:3212-3218. https://doi.org/10.2527/jas.2006-145
  5. Johnson, R. K. 1990. Inbreeding effects on reproduction, growth and carcass traits. In: Genetics of Swine (Ed. L. R. Young).
  6. Konig, S., F. Tsehay, F. Sitzenstock, U. U. von Borstel, M. Schmutz, R. Preisinger and H. Simianer. 2010. Evaluation of inbreeding in laying hens by applying optimum genetic contribution and gene flow theory. Poult. Sci. 89:658-667. https://doi.org/10.3382/ps.2009-00543
  7. Meuwissen, T. H. E and A. K. Sonesson. 1998. Maximizing the response of selection with a predefined rate of inbreeding: overlapping generations. J. Anim. Sci. 76:2575-2583.
  8. Parland, S. Mc., J. F. Kearney, M. Rath and D. P. Berry. 2007. Inbreeding effects on milk production, calving performance, fertility, and conformation in irish Holstein-friesians. J. Dairy Sci. 90:4411-4419. https://doi.org/10.3168/jds.2007-0227
  9. Quinton, M. and C. Smith. 1995. Comparison of evaluation-selection systems for maximizing genetic response at the same level of inbreeding. J. Anim. Sci. 73:2208-2212.
  10. Rutten, M. J. M., P. Bijma, J. A. Woolliams and J. A. M. van Arendonk. 2002. SelAction: Software to predict selection response and rate of inbreeding in livestock breeding programs. J. Hered. 93(6):456-458. https://doi.org/10.1093/jhered/93.6.456
  11. Sanchez, L., P. Bijma and J. A. Woolliams. 2003. Minimizing inbreeding by managing genetic contributions across generations. Genetics 164:1589-1595.
  12. Sewalem, A., K. Johannson, M. Wilhelmson and K. Lippers. 1999. Inbreeding and inbreeding depression on reproduction and production traits of White Leghorn lines selected for egg production traits. Br. Poult. Sci. 40:203-208. https://doi.org/10.1080/00071669987601
  13. Smith, L. A., B. G. Cassell and R. E. Pearson. 1998. The effects of inbreeding on the lifetime performance of dairy cattle. J. Dairy Sci. 81:2729-2737. https://doi.org/10.3168/jds.S0022-0302(98)75830-8
  14. Weigel, K. A. 2001. Controlling inbreeding in modern breeding programs. J. Dairy Sci. 84(E. Suppl.):E177-E184. https://doi.org/10.3168/jds.S0022-0302(01)70213-5
  15. Weigel, K. A. and S. W. Lin. 2002. Controlling inbreeding by constraining the average relationship between parents of young bulls entering AI progeny test programs. J. Dairy Sci. 85:2376-2383. https://doi.org/10.3168/jds.S0022-0302(02)74318-X
  16. Wray, N. R. and M. E. Goddard. 1994. Increasing long-term response to selection. Genet. Sel. Evol. 26:431-451. https://doi.org/10.1186/1297-9686-26-5-431

Cited by

  1. Contribuição genética ótima aplicada à seleção de ovinos Santa Inês vol.51, pp.6, 2016, https://doi.org/10.1590/S0100-204X2016000600006