The
SNP Interaction Discovery
process uses
penalized logistic regression
(PLR) to search for pairs of
SNPs
predictive of a
binary trait
beyond the predictive ability of single SNPs (Park and Hastie, 2008).
PLR incorporates SNPs stepwise, first adding terms up the maximum requested and then sequentially deleting them. The best
model
is selected from the stepwise sequence. A SNP interaction term competes for inclusion if one of the SNPs is already in the model. The PLR penalty equals
lambda
times the sum of squares of the coefficients. You can specify a single value of
lambda
to get the desired list of interactions, or a suitable
lambda
can be found by cross validating the process over a range of
lambda
s.
A Forest model is available to pre-select SNPs likely to be part of an interaction. Alternatively, a Forest of
trees
can create candidate SNP interaction indicators that can then compete with single SNPs for predictive ability in PLR.
Note
: This process is considered
experimental
.
One wide
Input Data Set
is needed to run the
SNP Interaction Discovery
process. This data set contains all of the numeric and other data to be analyzed. The
samplegmdata_numgeno.sas7bdat
data set, used in the following example, consists of 611 rows of individuals with 70 columns corresponding to data on these individuals. It was derived from the original, computer-generated,
samplegmdata.sas7bdat
data set described in
Data Sets Used in JMP Genomics Processes
, using the
Recode Genotypes
process. Marker data is presented in the one-column format. This data set is partially shown below.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see
Files and Data Sets
.
The output generated by this process is summarized in a Tabbed report. Refer to the
SNP Interaction Discovery
output documentation for detailed descriptions and guides to interpreting your results.