assoc: Association mapping
In GGPA: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture

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

Association mapping.

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assoc( object, ... )
## S4 method for signature 'GGPA'
assoc( object, FDR=0.05, fdrControl="global", i=NULL, j=NULL )

`object`	A GGPA model fit as obtained by GGPA().
`FDR`	The desired FDR level.
`fdrControl`	Method to control FDR. Possible values are "global" (global FDR control) and "local" (local FDR control). Default is "global".
`i`	Index for the first phenotype used in association mapping. See the details about how users can specify the pattern.
`j`	Index for the second phenotype used in association mapping. See the details about how users can specify the pattern.
`...`	Other parameters to be passed through to generic `assoc`.

assoc uses the direct posterior probability approach of Newton et al. (2004) to control global FDR in association mapping.

By default (i.e., i=NULL, j=NULL), assoc implements association mapping for each phenotype. If users are interested in identifying SNPs associated with a pair of phenotypes, users can specify indices of phenotypes of interest using the arguments i and j. Note that both i and j should be either NULL or numeric.

If i=NULL, j=NULL, returns a binary matrix indicating association of SNPs for each phenotype, where its rows and columns match those of input p-value matrix for function GGPA. Otherwise, returns a binary vector indicating association of SNPs for i-th and j-th phenotype pair.

Hang J. Kim and Dongjun Chung

Chung D, Kim H, and Zhao H (2016), "graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture," 13(2): e1005388

Kim H, Yu Z, Lawson A, Zhao H, and Chung D (2017), "Improving SNP prioritization and pleiotropic architecture estimation by incorporating prior knowledge using graph-GPA."

Newton MA, Noueiry A, Sarkar D, and Ahlquist P (2004), "Detecting differential gene expression with a semiparametric hierarchical mixture method," Biostatistics, Vol. 5, pp. 155-176.

GGPA, GGPA.

# Load the included simulation data
data(simulation)

# fit GGPA model with 200 iterations and a burn-in of 200 iterations
# Note that we recommend more than 200 iterations in practice
fit <- GGPA( simulation$pmat, nMain = 200, nBurnin = 200)

# Association mapping with FDR of 0.1 and global control
head(assoc( fit, FDR=0.1, fdrControl="global" ))

# We may specift i = 1 and j = 2 if we are interested in that specific phenotype
head(assoc( fit, FDR=0.1, fdrControl="global", i=1, j=2 ))