Use SPLS for selecting significant variants and testing the variants associated with disease traits.
Usage
SPLS(x, y, scale = TRUE, ncomp, eta.grid, size.max, a = 2,
npermutation = 0, npermutation.max, min.nonsignificant.counts)
Arguments
x
Genotype matrix, each row as an individual and each column as a snp
y
Phenotype vector
scale
see spls
ncomp
Number of components
eta.grid
see spls
size.max
Maximum number of variants included
a
Penalty parameter for information criterion, a=2 for AIC.
npermutation
Number of permutation, if less than 1, the permutation will not be run.
npermutation.max
Maximum permutation
min.nonsignificant.counts
Minimum nonsignificant counts
Value
nonsignificant.counts
Counts of permuted data that have a higher score than unpermuted data.
pvalue.empirical
Empirical pvalue via permutation
pvalue.nominal
Not availabe
vs
The selected variants
total.permutation
Total permutation
Details
Use spls package to implement SPLS and an information criterion (AIC, BIC, GIC) to select a set of variants.
References
Xu C, Ladouceur M, Dastani Z, Richards JB, Ciampi A, Greenwood CMT. (2012) Multiple Regression Methods Show Great Potential for Rare Variant Association Tests. PLoS ONE 7(8): e41694. doi:10.1371/journal.pone.0041694