scan.glm.2D: Scans regional data allowing for gene-gene interaction using glm

Description

Scans regional data allowing for gene-gene interaction using glm

Usage

scan.glm.2D(formula, family = gaussian(), data, snpsubset, idsubset, 
		bcast = 50)

Arguments

formula

character string containing formula to be used in glm. You should put CRSNP argument in the formula, to arrange how the SNP from the list would be treated. This allows to put in an interaction term.

family

family to be passed to glm

snpsubset

Index, character or logical vector with subset of SNPs to run analysis on. If missing, all SNPs from data are used for analysis.

idsubset

Index, character or logical vector with subset of IDs to run analysis on. If missing, all people from data/cc are used for analysis.

data

object of class "gwaa.data"

bcast

show progress every bcast SNPs

Value

Object of class scan.gwaa.2D-class

Details

For each pair of SNPs, say snp1 and snp2, scan.glm.2D estimates 5 models. Let us denote snp1 when it is coded as allele dose (0,1, 2) and thus results in additive model as snp1dose and when it is coded as 'factor' (genotypic model) as snp1factor

m00: y ~ mu [1 regression coefficient to estimate]

m10: y ~ mu + snp1dose + snp2dose [3 coefficients]

m11: y ~ mu + snp1dose + snp2dose + snp1dose * snp2dose [4 coefficients]

m20: y ~ mu + snp1factor + snp2factor [5 coefficients]

m21: y ~ mu + snp1factor + snp2factor + snp1factor * snp2factor [9 coefficients]

In the output, "P1df" refers to the test of m00 vs m10 (this is actually 2 df test); "P2df" refers to the test of m00 vs m20 (4 df test); "Pint1df" refers to the test of m10 vs m11 (1 df test); "Pint2df" refers to the test of m20 vs m21 (4 df test). The output is in matrix format as these P-values are generated for each pair of SNPs in turn.

Examples

Run this code

## Not run: 
# require(GenABEL.data)
# data(srdta)
# a <- scan.glm.2D("bt~sex+age+CRSNP",family=binomial(),data=srdta,snps=(1:10),bcast=2)
# plot(a)
# ## End(Not run)