cGWAS.emmax: Genomewide Association Study - EMMAX
In cpgen: Parallelized Genomic Prediction and GWAS

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

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).

1 2	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)

`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`

...

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`

Claas Heuer

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.

cGWAS

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

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

## End(Not run)

Loading required package: Matrix
Loading required package: pedigreemm
Loading required package: lme4

Computing Eigen Decomposition and Estimating V

Variance Components:
Marker: 0.362
Residual: 0.789

Running GWAS
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