analytic_locFDR_BF_cor {CPBayes} | R Documentation |
Run the analytic_locFDR_BF_cor
function to analytically compute the local FDR & Bayes factor (BF)
that quantifies the evidence of aggregate-level pleiotropic association for correlated summary statistics.
Here a fixed value of slab variance is considred instead of a range of it in cpbayes_cor
.
analytic_locFDR_BF_cor(BetaHat, SE, Corln, SpikeVar = 1e-04, SlabVar = 0.8)
BetaHat |
A numeric vector of length K where K is the number of phenotypes. It contains the beta-hat values across studies/traits. No default. |
SE |
A numeric vector with the same dimension as BetaHat providing the standard errors corresponding to BetaHat. Every element of SE must be positive. No default. |
Corln |
A numeric square matrix of order K by K providing the correlation matrix of BetaHat.
The number of rows & columns of Corln must be the same as the length of BetaHat. No default
is specified. See |
SpikeVar |
Variance of spike (normal distribution with small variance) representing the null effect distribution. Default is 10^(-4). |
SlabVar |
Variance of slab normal distribution representing the non-null effect distribution. Default is 0.8. |
The output produced by the function is a list which consists of the local FDR and log10(Bayes factor).
locFDR |
It provides the analytically computed local false discovery rate (posterior probability of null association) under CPBayes model (a Bayesian analog of the p-value) which is a measure of the evidence of the aggregate-level pleiotropic association. Bayes factor is adjusted for prior odds, but locFDR is solely a function of the posterior odds. |
log10_BF |
It provides the analytically computed log10(Bayes factor) produced by CPBayes that measures the evidence of the overall pleiotropic association. |
Majumdar A, Haldar T, Bhattacharya S, Witte JS (2018) An efficient Bayesian meta analysis approach for studying cross-phenotype genetic associations. PLoS Genet 14(2): e1007139.
cpbayes_cor
, estimate_corln
, analytic_locFDR_BF_uncor
, cpbayes_uncor
, post_summaries
, forest_cpbayes
data(ExampleDataCor) BetaHat <- ExampleDataCor$BetaHat BetaHat SE <- ExampleDataCor$SE SE cor <- ExampleDataCor$cor cor result <- cpbayes_cor(BetaHat, SE, cor) str(result)