Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-17T17:09:05.818Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of factors affecting superovulatory responses in ruminants

Published online by Cambridge University Press:  27 March 2009

J. A. Woolliams ,
Z. W. Luo ,
B. Villanueva ,
D. Waddington ,
P. J. Broadbent ,
W. A. C. McKelvey  and
J. J. Robinson
J. A. Woolliams
Affiliation:
Roslin Institute(Edinburgh), Roslin, Midlothian EH25 9PS, UK
Z. W. Luo
Affiliation:
Roslin Institute(Edinburgh), Roslin, Midlothian EH25 9PS, UK
B. Villanueva
Affiliation:
Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JQ, UK
D. Waddington
Affiliation:
Roslin Institute(Edinburgh), Roslin, Midlothian EH25 9PS, UK
P. J. Broadbent
Affiliation:
Scottish Agricultural College, 581 King Street, Aberdeen AB9 1UD, UK
W. A. C. McKelvey
Affiliation:
Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JQ, UK
J. J. Robinson
Affiliation:
Rowett Research Institute, Greenburn Road, Bucksburn, Aberdeen AB2 9SB, UK
Get access Rights & Permissions [Opens in a new window]

Summary

Data on ovulation rate and numbers of ova and transferable embryos recovered from superovulated cattle and sheep were analysed using generalized linear models, quasi-likelihood, restricted maximum likelihood (REML) and generalized linear mixed models (GLMMS). The data pertained to the operation of nucleus breeding schemes in cattle and the commercial application of embryo transfer in sheep.

Results of the analyses showed that generalized linear models involving Poisson and Binomial distributions were inappropriate because of over-dispersion, and that analyses using quasi-likelihood to model negative binomial and β-binomial distributions were more suitable. Factors identified as important in determining the results in cattle were the number of previous superovulations (a higher proportion of transferable embryos were obtained in the initial flush compared to subsequent recoveries in two out of three sets of data), the donor (significant in all analyses with repeated recoveries) and its mate (significant in some analyses). In sheep, the use of pFSH or hMG for superovulation increased embryo yields above those obtained with PMSG + GnRH. Analyses of a further data set for sheep showed the effect of breed was ambiguous.

The effects of donors and their mates were treated as random effects in analyses involving REML and GLMMS. Results showed that the repeatability of the number of transferable embryos produced per donor ranged between 0·13 and 0·23 in three sets of data and was significant in all cases. In these analyses the variance among mates was not significantly different from zero.

The results of analyses were used to develop a random generator to simulate the numbers of ova and embryos recovered from a cow following superovulation. By sampling from negative binomial distributions where the scale factor used for each cow was a normally distributed deviate, distributions were obtained which had the same mean, variance and repeatability as those observed.

Type
Animals
Information
The Journal of Agricultural Science , Volume 124 , Issue 1 , February 1995 , pp. 61 - 70
Copyright
Copyright © Cambridge University Press 1995

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Breslow, N. E. (1984). Extra-Poisson variation in log-linear models. Applied Statistics 33, 38–44.CrossRefGoogle Scholar
Breslow, N. E. & Clayton, D. G. (1993). Approximate inference in Generalized Linear Mixed Models. Journal of the American Statistical Association 88, 9–25.Google Scholar
Genstat 5 (1993). Release 3, Reference Manual. Oxford: Clarendon Press.Google Scholar
Mauléon, P., Mariana, J.-C., Benoit, M., Solari, A. & Chupin, D. (1970). Influence de différentes doses de PMSG et HCG injectées en phase folliculaire de cycle oestrien sur le nombre et le rendement d'ovulations de vaches de race Française Frisome Pie Noire. Annales de Biologie Animate, Biochimie et Biophysique 10, Supplement 1, 31–46.CrossRefGoogle Scholar
Nelder, J. A. (1985). Quasi-likelihood and GLIM. In Generalized Linear Models: Proceedings of the GLIM85 Conference. Lancaster, UK.Google Scholar
Nelder, J. A. & Wedderburn, R. W. M. (1972). Generalized Linear Models. Journal of the Royal Statistical SocietyA 135, 370–384.CrossRefGoogle Scholar
Patterson, H. D. & Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika 58, 545–554.CrossRefGoogle Scholar
Ruane, J. & Thompson, R. (1991). Comparison of simulated and theoretical results in adult MOET nucleus schemes for dairy cattle. Livestock Production Science 28, 1–20.CrossRefGoogle Scholar
Schrooten, C. & Van Arendonk, J. A. M. (1992). Stochastic simulation of dairy cattle breeding schemes: genetic evaluation of nucleus size and type. Journal of Animal Breeding and Genetics 109, 1–15.CrossRefGoogle Scholar
Scudamore, C. L., Robinson, J. J., Aitken, R. P., Kennedy, D. J., Ireland, S. & Robertson, I. S. (1991). Laparoscopy for intrauterine insemination and embryo recovery in superovulated ewes at a commercial embryo transfer unit. Theriogenology 35, 329–337.CrossRefGoogle Scholar
Waddington, D., Welham, S. J., Gilmour, A. R. & Thompson, R. (1993). GENSTAT Newsletter 30, 13–24.Google Scholar
Wedderburn, R. W. M. (1974). Quasi-likelihood functions, generalized linear models and the Gauss-Newton method, Biomethka 61, 439–447.Google Scholar
Williams, D. A. (1982). Extra-binomial variation in logistic linear models. Applied Statistics 31, 144–148.CrossRefGoogle Scholar