Processes | Genetics | Progeny Simulation

Progeny Simulation
Progeny Simulation simulates the progeny from a specified set of crosses scoring code. Scoring code is a predictive model saved into a SAS or JSL file. This process enables you to test various models to estimate the resulting outcomes.
Caution : specifying a large number of crosses and/or progeny can consume considerable CPU and disk resources.
What do I need?
Two files are required by this process.
The Input Data Set must contain columns identifying the genotypes for each of the markers. Markers must be biallelic and defined using a numeric format. Genotypes must be coded as 0, 1, 2, or dot (.) (for missing value) before this data set can be input into this process.
The sampledata_numgeno.sas7bdat data set shown above is included in the Sample Data folder. The trait to be assessed and the biallelic markers ( 0 and 2 represent individuals homozygous for one of two possible alleles , 1 represents the heterozygote) are indicated.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .
A second, required, file is one or more Scoring Code Files . These files are SAS program files that contain models that use measured attributes to either characterize or predict the value of an event. These models are developed on historical data where an event has been measured or inferred and are generated using one or more predictive modeling processes, such as Ridge Regression , for example. The models are then applied to new data for which the attributes are known, but the event has not yet occurred.
The file is shown below:
This file consists of one line of SAS code (spread over multiple lines in the file) that can be incorporated into a SAS DATA step in which the generated coefficients are multiplied by the variables to produce predictors for each cross.
A third file is a SAS data set indicating the crosses that are to be simulated. This data set is optional. If not specified, progeny are simulated from all possible crosses.
The output generated by this process is summarized in a Tabbed report. Refer to the Cross Evaluation output documentation for detailed descriptions and guides to interpreting your results.