The Genomic BLUP process computes
Best Linear Unbiased Predictions (BLUPs) of the response variables based on a mixed model that includes an additive variance component, and optionally a dominance variance. Additive and dominance genomic relationship matrices are computed based on the scores of molecular markers, and these matrices are then entered in the mixed model to be used in the estimation of their respective variance components, i.e., additive and dominance variances. Computations are performed using either SAS/STAT PROC MIXED or HPMIXED. Summary statistics and visual display of the results are provided and can help breeders select best performing samples (lines or individuals) to follow-up with the
Progeny Simulation process to produce offspring for the next breeding cycle.
One wide Input Data Set containing response variables and marker variables holding scores of molecular markers is required.
The sample data set used in the following example, the samplegmdata_numgeno data set, is partially shown below.
Note: Genotypes must be coded as 0, 1, 2, or dot (.) (for missing value) before this data set can be input into this process.
An optional Annotation Data Set containing a column with information on each marker and other relevant columns is also allowed, however, this data set is not used in the
Genomic BLUP process; but instead it is passed on the follow-up
Progeny Simulation process where it is used.
A portion of t
he annotation data set used in the following example, the samplemap_pos data set, is illustrated below. This data set is a
tall data set; each row corresponds to a different marker.