cGWAS.emmax: Genomewide Association Study - EMMAX

In cgenpp: Parallel genomic evaluations

Description

This is a convenience function that uses the function cGWAS but estimates the variance-covariance matrix of the phenotype vector in advance using clmm. This method was termed EMMAX (Kang et al., 2010).

Usage

cGWAS.emmax(y,M,A=NULL,X=NULL,dom=FALSE,verbose=FALSE,scale_a = 0, df_a = -2,
		   scale_e = 0, df_e = -2,niter=15000,burnin=7500,seed=NULL)

Arguments

y	vector of phenotypes
M	Marker matrix
A	Relationship matrix that is being used to estimate V - if omitted, A will be constructed using M and cgrm
X	Optional Design Matrix for additional fixed effects. If omitted a column-vector of ones will be assigned
dom	Defines whether to include an additional dominance coefficient for every marker. Note: only useful if the genotype-coding in M follows {-1,0,1} The dominance coefficient is computed as: 1-abs(M)
verbose	Prints progress to the screen
scale_a	prior scale parameter for a
df_a	prior degrees of freedom for a
scale_e	prior scale parameter for e
df_e	prior degrees of freedom for e
niter	Number of iterations used by clmm
burnin	Burnin for clmm
seed	Seed used by clmm

Details

...

Value

List of 3 vectors or matrices. If dom=TRUE every element of the list will be a matrix with two columns. First column additive, second dominance:

p-value	Vector of p-values for every marker
beta	GLS solution for fixed marker effects
se	Standard Errors for values in beta
marker_variance	Estimate of the marker variance reported by clmm
residual_variance	Estimate of the residual variance reported by clmm

Author(s)

Claas Heuer

References

Kang, H. M., N. A. Zaitlen, C. M. Wade, A. Kirby, D. Heckerman, M. J. Daly, and E. Eskin. "Efficient Control of Population Structure in Model Organism Association Mapping." Genetics 178, no. 3 (February 1, 2008): 1709-23. doi:10.1534/genetics.107.080101.

Kang, Hyun Min, Jae Hoon Sul, Susan K Service, Noah A Zaitlen, Sit-yee Kong, Nelson B Freimer, Chiara Sabatti, and Eleazar Eskin. "Variance Component Model to Account for Sample Structure in Genome-Wide Association Studies." Nature Genetics 42, no. 4 (April 2010): 348-54. doi:10.1038/ng.548.

See Also

cGWAS

Examples

## Not run: 
# generate random data
rand_data(500,5000)

# run EMMAX
res <- cGWAS.emmax(y,M,verbose=TRUE)

## End(Not run)

cgenpp documentation built on May 2, 2019, 5:56 p.m.