Description Usage Arguments Details Value See Also
Perform an informed Genome-Wide Association Study (iGWAS). This is based on a current study and a prior study. The goal is to discover significant SNPs in the current study using hypothesis testing. The prior study is used to improve power. P-values and sample sizes are used for both the current and the prior studies.
1 2 3 4 |
P_current |
P-values in the current study, a numeric vector of length J, with entries between 0 and 1 |
N_current |
sample size in the current study, a positive integer (or vector of length J) |
P_prior |
P-values in the prior study, a numeric vector of length J, with entries between 0 and 1 |
N_prior |
sample size in the current study, a positive integer (or vector of length J) |
q |
(optional) uncorrected level at which tests should be performed. Default |
weighting_method |
(optional) weighting method used. Available methods: |
p_adjust_method |
(optional) adjustment method for multiple testing used. The available methods are
|
sides |
(optional) The prior p-values must be one or two-sided: sides = 1 or 2. Default |
phi |
(optional) dispersion factor used to multiply all standard errors. Default |
beta |
(optional) weights are proportional to |
UB_exp |
(optional) upper bound on the weights (default |
figure |
(optional) |
GWAS_data_frame |
(optional) when |
This method computes the p-value weights based on the prior p-values, and uses them in multiple testing (p-value weighting) for the current p-values. The p-value weighting method (e.g. Unweighted, Bayes) and the multiple testing adjustment (e.g. Bonferroni, Benjamini-Hochberg) can be specified independently.
For more details, see the paper "Optimal Multiple Testing Under a Gaussian Prior on the Effect Sizes", by Dobriban, Fortney, Kim and Owen, http://arxiv.org/abs/1504.02935
A list containing:
sig_ind
: A vector of 0-1s indicating the significant tests (1-s)
num_sig
: The number of significant tests. Equals sum(sig_ind)
w
: The computed p-value weights
P_w
: The weighted p-values. These equal P_current/w
Other p.value.weighting: bayes_weights
;
exp_weights
;
spjotvoll_weights
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