Genomewide Association Study - EMMAX

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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

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cGWAS.emmax(y,M,A=NULL,X=NULL,dom=FALSE,verbose=TRUE,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

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

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

## End(Not run)