Designing complex research projects to estimate genetic parameters plus treatment and other effects - Optimising the experimental design

Abstract

There is an increasing trend towards integrated research, in which several individuals or institutions pool their expertise and make use of common resources, collaborating towards a common set of scientific goals. Integrated research enables a larger number of factors to be investigated, and the most influential or important ones identified, providing information on how the different factors interact or fit together. Good experimental design is, however, required to ensure the aims can be achieved and resources spent wisely. Issues involved in the experimental design of the Australian Beef Cattle Cooperative Research Centre for Meat Quality are discussed. Theoretical results and simulation studies were used to determine optimal numbers of progeny per sire for estimating genetic parameters. For heritabilities of 0.2 and 0.5, the optima are respectively 21 and 9 progeny with recorded measurements. The curves surrounding the optima are quite flat, so aiming for 10-15 progeny with measurements per trait should provide reasonable accuracy in many situations. Estimates of heritabilities, genetic correlations and phenotypic variances have lower sampling correlations than genetic variances and covariances, suggesting that when results are pooled over different breeds or trials, it is better to pool estimates of heritabilities and genetic correlations than (co)variances. Using sires in more than one year increases the robustness of estimated sire effects and increases the accuracy of genetic parameter estimates for hard-to-measure traits (e.g. feed efficiency) that are not recorded on all animals. Unless sires can be chosen as a true random sample of the population, arrangements of link sires (and other effects such as treatments) should be chosen to provide accurate estimates when all terms in the model are fitted as fixed.