summaryBVS: Calculates Posterior Summaries for BVS Methods

In BVS: Bayesian Variant Selection: Bayesian Model Uncertainty Techniques for Genetic Association Studies

Description

This function calculates the global and marginal Bayes Factors that give the strength of evidence of there being an association in the overall set of variants of interest, the individual genes of interest (if specified) and the individual variants of interest.

Usage

summaryBVS(BVS.out,data=data,forced=NULL,cov=NULL,burnin=1000,regions=NULL,
           rare=FALSE,mult.regions=FALSE,inform=FALSE)

Arguments

BVS.out	Output from sampleBVS or enumerateBVS
data	an (n x (p+1)) dimensional data frame where the first column corresponds to the response variable that is presented as a factor variable corresponding to an individuals disease status (0|1),and the final p columns are the SNPs of interest each coded as a numeric variable that corresponds to the number of copies of minor alleles (0|1|2)
forced	an optional (n x c) matrix of c confounding variables that one wishes to adjust the analysis for and that will be forced into every model.
burnin	an integer indicating the length of the burnin.
regions	an optional p dimensional vector of character strings giving the names of the regions (example can be gene names or pathway names) for each of the variants in data set. If a region vector is given, the function will report regional BF.
inform	if inform=TRUE corresponds to iBMU algorithm of Quintana and Conti (Submitted) that incorporates user specified external predictor-level covariates into the variant selection algorithm.
cov	an optional (p x q) dimensional matrix of q predictor-level covariates (needed when inform=TRUE) that the user wishes to incorporate into the estimation of the marginal inclusion probabilities using the iBMU algorithm
rare	if rare=TRUE corresponds to the Bayesian Risk index (BRI) algorithm of Quintana and Conti (2011) that constructs a risk index based on the multiple rare variants within each model. The marginal likelihood of each model is then calculated based on the corresponding risk index.
mult.regions	when rare=TRUE if mult.regions=TRUE then we include multiple region specific risk indices in each model. If mult.regions=FALSE a single risk index is computed for all variants in the model.

Details

Global and marginal Bayes factors (BF) are computed based on calculating the posterior probabilities of each of the unique models that were visited in sampleBVS or all models that were enumerated in enumerateBVS. The global BF tests the hypothesis that there is an association in the overall set of variants. BF's are also calculated at the regional (if regions are specified) and the variant level. At the regional level, BF are computed for the overall evidence of at least one of the variants within the region of interest being associated. Posterior estimates for the coefficients are also reported. Finally, if inform=TRUE posterior estimates of the effects of the posterior-level covariates on the marginal inclusion probabilities are reported.

Value

This function outputs a list of the following values:

Global	Global Bayes Factor giving the strength of evidence that at least one variant within the analysis is associated with the outcome of interest
MargBF	Marginal variant specific Bayes Factors giving the strength of evidence that each one of the variants are associated with the outcome of interest
Marg.RBF	Regional level Bayes Factors giving the strength of evidence that at least one variant within the region is associated with the outcome of interest
PostAlpha	If inform=TRUE gives that posterior estimates of the effects of the posterior-level covariates on the marginal inclusion probabilities.
PostCoef	Posterior estimates for the coefficients of each variant if rare=FALSE and of the risk index if rare=TRUE
Which	Matrix of the unique models as well as their prior probability and posterior probability
Which.r	Matrix indicating which regions are included in each of the unique models given in Which
Coef	Matrix indicating the coefficients of the variants (or risk index) included in each unique model

Author(s)

Melanie Quintana <[e-mail]>

References

Quintana M, Conti D (2011). Incorporating Model Uncertainty in Detecting Rare Variants: The Bayesian Risk Index. Genetic Epidemiology 35:638-649.

Quintana M, Conti D (Submitted). Integrative Variable Selection via Bayesian Model Uncertainty.

Examples

## RARE VARIANT BRI EXAMPLE
  ## Load the data for Rare variant example
  data(RareData)

  ## Load the results from running sampleBVS on rare variant data for 100K iterations
  data(RareBVS.out)

  ## Summarize output with a burn in of 1000 iterations
  ## Results from summary found in data(RareResults)
  RareResults = summaryBVS(RareBVS.out,data=RareData,burnin=1000,rare=TRUE)

## INFORMATIVE iBMU EXAMPLE
  ##Load the data for the informative example
  data(InformData)

  ## Load the results from running sampleBVS with inform=FALSE for 100K iterations
  data(InformBVS.NI.out)

  ## Summarize output
  ## Results from summary found in data(Informresults.NI)
  
  Informresults.NI = summaryBVS(InformBVS.NI.out,data=InformData$data,burnin=1000,
                                regions=InformData$genes,inform=FALSE)

  ## Load the results from running sampleBVS with inform=TRUE for 100K iterations
  data(InformBVS.I.out)

  ## Summarize output
  ## Results from summary found in data(Informresults.I)
 
  Informresults.I = summaryBVS(InformBVS.I.out,data=InformData$data,
                               cov=as.matrix(InformData$cov),burnin=1000,
                               regions=InformData$genes,inform=TRUE)

BVS documentation built on May 1, 2019, 10:16 p.m.