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.