The variance of motherID will represent var(GCA).
In the previous equation motherID and(fatherID) overlays the design matrices for males and females so you get only one prediction for each parent, in spite of some parents acting as both male and female (which is typical in crossing programs in trees). # Uses an individual tree model # where var(tree) = additive variance and # var(family) = 1/4 dominance varianceĭbh ~ mu rep !r rep.iblock plot tree familyĭbh ~ mu rep !r rep.iblock plot motherID,
Incomplete block design using CP material …or in the case of controlled pollinated material: # Uses a family model # where var(motherID) = 1/4 additive variance # (if there is no selfing, etc) # Uses an individual tree model # where var(tree) = additive variance Incomplete block design using OP material when you are only interested in predicting breeding values for the parents for backwards selection, you may prefer to use models that are equivalent and computationally less demanding (e.g. # while this part works on basic density (bd) If you wanted to have interaction rather than nesting you would use, for example: factor1 factor2 factor1.factor2 or, even shorter, factor1*factor2, which would be expanded to the previous notation. Note that the nested effect is created defining rep as an effect in the model equation and then rep.iblock, In the case of incomplete block designs we treat replicates as fixed, and incomplete blocks (iblock) within each rep as random. # Data file and option to run # differentparts of the program You can run the different parts of the program using something like: > asreml partnumber (e.g. Lets start with the simplest design normally used in tree breeding programs: randomized complete blocks.