assoc {GGPA} | R Documentation |
Association mapping.
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
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.
# 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 ))