post_summaries {CPBayes}  R Documentation 
Run the post_summaries
function to summarize the MCMC data produced by
cpbayes_uncor
or cpbayes_cor
and obtain meaningful insights
into an observed pleiotropic signal.
post_summaries(mcmc_output, level = 0.05)
mcmc_output 
A list returned by either

level 
A numeric value. (1level)% credible interval (Bayesian analog of the confidence interval) of the unknown true genetic effect (beta/odds ratio) on each trait is computed. Default choice is 0.05. 
The output produced by this function is a list that consists of various components.
variantName 
It is the name of the genetic variant provided by the user. If not specified by the user, default name is ‘Variant’. 
log10_BF 
It provides the log10(Bayes factor) produced by CPBayes that measures the evidence of the overall pleiotropic association. 
locFDR 
It provides the local false discovery rate (posterior probability of null association) produced by CPBayes (a Bayesian analog of the pvalue) which is a measure of the evidence of aggregatelevel pleiotropic association. Bayes factor is adjusted for prior odds, but locFDR is solely a function of posterior odds. locFDR can sometimes be significantly small indicating an association, but log10_BF may not. Hence, always check both log10_BF and locFDR. 
subset 
A data frame providing the optimal subset of associated/nonnull traits along with their traitspecific posterior probability of association (PPAj) and direction of associations. It is NULL if no phenotype is selected by CPBayes. 
important_traits 
It provides the traits which yield a traitspecific posterior probability of association (PPAj) > 20%. Even if a phenotype is not selected in the optimal subset of nonnull traits, it can produce a nonnegligible value of traitspecific posterior probability of association. We note that ‘important_traits’ is expected to include the traits already contained in ‘subset’. It provides the name of the important traits and their traitspecific posterior probability of association (PPAj) and the direction of associations. Always check 'important_traits' even if 'subset' contains a single trait. It helps to better explain an observed pleiotropic signal. 
traitNames 
It returns the name of all the phenotypes specified by the user. Default is trait1, trait2, ... , traitK. 
PPAj 
Data frame providing the traitspecific posterior probability of association for all the phenotypes. 
poste_summary_beta 
Data frame providing the posterior summary of the unknown true genetic effect (beta) on each trait. It gives posterior mean, median, standard error, credible interval (lower and upper limits) of the true beta corresponding to each trait. 
poste_summary_OR 
Data frame providing the posterior summary of the unknown true genetic effect (odds ratio) on each trait. It gives posterior mean, median, standard error, credible interval (lower and upper limits) of the true odds ratio corresponding to each trait. 
Majumdar A, Haldar T, Bhattacharya S, Witte JS (2018) An efficient Bayesian meta analysis approach for studying crossphenotype genetic associations. PLoS Genet 14(2): e1007139.
data(ExampleDataUncor) BetaHat < ExampleDataUncor$BetaHat BetaHat SE < ExampleDataUncor$SE SE traitNames < paste("Disease", 1:10, sep = "") SNP1 < "rs1234" result < cpbayes_uncor(BetaHat, SE, Phenotypes = traitNames, Variant = SNP1) PleioSumm < post_summaries(result, level = 0.05) str(PleioSumm)