vGWAS.variance: Calculating Variance Explained by A Single Marker
In vGWAS: Variance Heterogeneity Genome-Wide Association Study

Description Usage Arguments Details Value Author(s) References See Also Examples

The function calculates and reports the variance explained for a single marker by fitting a double generalized linear model. It gives both the variance explained by the mean and variance parts of model.

1	vGWAS.variance(phenotype, marker.genotype, print = TRUE)

`phenotype`	a `numeric` vector of the phenotyic values. See Examples.
`marker.genotype`	a `numeric` or `character` or `factor` vector of the genotypes of a single marker. See Examples.
`print`	a `logical` value. If `FALSE`, the heritability values will be returned for storage.

The Value will only be available if only.print = FALSE.

`variance.mean`	the variance explained by the mean part of model.
`variance.disp`	the variance explained by the variance part of model.

Xia Shen

Shen, X., Pettersson, M., Ronnegard, L. and Carlborg, O. (2011): Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana. Submitted.

vGWAS-package, vGWAS, plot.vGWAS

## Not run: 

# ----- load data ----- #

data(pheno)
data(geno)
data(chr)
data(map)

# ----- variance GWA scan ----- #

vgwa <- vGWAS(phenotype = pheno, geno.matrix = geno,
              marker.map = map, chr.index = chr)
              
# ----- visualize the scan ----- #

plot(vgwa)

# ----- calculate the variance explained by strongest the marker ----- #

vGWAS.variance(phenotype = pheno, 
                   marker.genotype = geno[,vgwa$p.value == min(vgwa$p.value)])
                   

## End(Not run)