post_summaries: Post summary of the MCMC data generated by the uncorrelated...

Description Usage Arguments Value References See Also Examples

View source: R/summary_functions.R

Description

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.

Usage

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post_summaries(mcmc_output, level = 0.05)

Arguments

mcmc_output

A list returned by either cpbayes_uncor or cpbayes_cor. This list contains the primary results and MCMC data produced by cpbayes_uncor or cpbayes_cor. No default is specified. See the example below.

level

A numeric value. (1-level)% 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.

Value

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 p-value) which is a measure of the evidence of aggregate-level 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/non-null traits along with their trait-specific 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 trait-specific posterior probability of association (PPAj) > 20%. Even if a phenotype is not selected in the optimal subset of non-null traits, it can produce a non-negligible value of trait-specific 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 trait-specific 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 provaiding the trait-specific 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.

References

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.

See Also

cpbayes_uncor, cpbayes_cor

Examples

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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)

ArunabhaCodes/CPBayes documentation built on May 5, 2019, 8:12 a.m.