Abstract #766
Section: Breeding and Genetics
Session: Breeding and Genetics: Poultry and swine
Format: Oral
Day/Time: Wednesday 3:30 PM–3:45 PM
Location: Panzacola F-3
Session: Breeding and Genetics: Poultry and swine
Format: Oral
Day/Time: Wednesday 3:30 PM–3:45 PM
Location: Panzacola F-3
# 766
Genetic and economic effects of incorporating genomic predictions on health in swine breeding schemes.
Chandraratne M. B. Dematawewa*1, Anna Grosse Holthaus2, Henner Simianer2, Jack C. M. Dekkers1, 1Iowa State University, Ames, IA, 2University of Göttingen, Göttingen, Germany.
Key Words: genomic selection, health, swine breeding
Genetic and economic effects of incorporating genomic predictions on health in swine breeding schemes.
Chandraratne M. B. Dematawewa*1, Anna Grosse Holthaus2, Henner Simianer2, Jack C. M. Dekkers1, 1Iowa State University, Ames, IA, 2University of Göttingen, Göttingen, Germany.
A study was conducted to determine the effect of inclusion of genomic evaluations for health (GE-health) on improvements in health, the overall breeding goal (ΔH) and discounted profit (ΔΩ) in commercial cross breeding schemes. A 3-way cross breeding scheme with 2 maternal lines [female: Yorkshire (YS); male: Landrace (LR)] and a terminal sire line (Duroc, DU) was deterministically simulated using the ZPLAN+ software. The YS nucleus consisted of 50 boars and 1000 sows, while LR and DU nuclei had 100 boars and 800 sows each. Productive life of nucleus and multiplier (n = 2400) animals was 1 year, while F1 crossbred sows were kept for 2 years to produce 532,400 commercial piglets/year. Days to market (DY), backfat thickness (BF), and litter weight at 21 d (LW; for YS and LR only) were considered with heritabilities (h2) of 0.4, 0.5, and 0.09. The traits were standardized to genetic SD = 1. A health trait (HL) was simulated with h2 = 0.05 and genetic SD = 1, with positive genetic (0.2) and phenotypic (0.3) correlations with DY and BF, and zero correlations with LW. HL was recorded from 80 commercial halfsibs/animal. Economic weights ($) for DY, BF, LW and HL were −1.86, −1.82, 5.35, and 1.86 per genetic SD, respectively. Planning horizon was 10 years (discount rate = 0.05). Costs for high and low density genotyping and health recording were $100, $40 and $10 per animal, respectively. Accuracy of GE-health (rMG-HEALTH) was varied from 0 to 1.0 and 0.7 for the other traits. Inclusion of GE-health for both sires and dams increased both ΔH and ΔΩ as rMG-HEALTH increased, mainly due to greater genetic gain in HEALTH. At rMG-HEALTH = 0.8, extra response in ΔH and ΔΩ for were 6.25% and $0.55 per animal in the breeding program, compared with having no GE-health. The corresponding values were higher (7.06% and $1.34, respectively) when HEALTH phenotypes were not recorded, partly due to lower initial response. The benefit of health recording diminished when rMG-HEALTH increased. These results show economic feasibility for implementing GE-health in commercial swine breeding. Funded by Genome Canada.
Key Words: genomic selection, health, swine breeding