Description Usage Arguments Details Value Author(s) References See Also Examples
Association mapping.
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object |
GPA model fit. |
FDR |
FDR level. |
fdrControl |
Method to control FDR. Possible values are "global" (global FDR control) and "local" (local FDR control). Default is "global". |
pattern |
Pattern for association mapping.
By default (i.e., |
... |
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.
Users can specify the pattern using 1 and * in pattern
argument,
where 1 and * indicate phenotypes of interest and phenotypes that are not of interest, respectively.
For example, when there are three phenotypes,
pattern="111"
means a SNP associated with all of three phenotypes,
while pattern="11*"
means a SNP associated with the first two phenotypes
(i.e., association with the third phenotype is ignored (averaged out)).
If pattern=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 GPA
.
Otherwise, returns a binary vector indicating association of SNPs for the phenotype combination of interest.
Dongjun Chung
Chung D*, Yang C*, Li C, Gelernter J, and Zhao H (2014), "GPA: A statistical approach to prioritizing GWAS results by integrating pleiotropy information and annotation data," PLoS Genetics, 10: e1004787. (* joint first authors)
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.
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