CAVIARBF statistics

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CAVIARBF specifications

Information


Unique identifier OMICS_11040
Name CAVIARBF
Alternative name CAVIAR Bayes Factor
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Some marginal test statistics for each SNP and the pairwise correlation among SNPs.
Operating system Unix/Linux, Mac OS
Programming languages C++, R
Computer skills Advanced
Version 0.1.4.1
Stability Stable
Requirements
Rcpp, RcppEigen, glmnet
Maintained Yes

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  • person_outline Daniel Schaid <>

Publication for CAVIAR Bayes Factor

CAVIARBF in publications

 (4)
PMCID: 5727399
PMID: 29235454
DOI: 10.1038/s41467-017-01913-6

[…] 47 lead snps using a single, randomly selected iop measurement for each individual., to produce the most thorough list of candidate genes within the 47 identified loci, we used a bayesian approach (caviarbf), publicly available at https://bitbucket.org/wenan/caviarbf. briefly, for each of the 47 signals, we computed each variant’s ability to explain the observed signal within a 2 mb window […]

PMCID: 5555460
PMID: 28600440
DOI: 10.1534/g3.117.043752

[…] not known, and there are no established methods for comprehensive screening transcriptome-wide for localization of multi-locus local eqtl effects. two exhaustive search algorithms, paintor () and caviarbf (), hold promise for detailed dissection of multisite models at individual loci, and a bayesian shotgun stochastic search algorithm, finemap (), has recently been proposed for rapid maximum […]

PMCID: 4663677
PMID: 26611117
DOI: 10.1038/ncomms9653

[…] and thus is likely conservative. for that reason, we also applied a recently developed fine-mapping method that uses marginal test statistics and correlations among snps in a bayesian framework (caviarbf) which may help to interpret the results for some genes. caviarbf estimates a posterior inclusion probability for each snp. when summed, this provides an estimate of the expected number […]

PMCID: 4572002
PMID: 26157023
DOI: 10.1093/hmg/ddv260

[…] data sets, where genotype level data may not be available. a recent analysis () compared bimbam (which can incorporate multiple causal variants but requires genotype level data) with two methods, caviarbf [a modified implementation of caviar ()] and paintor (), which require only the summary test statistics and a matrix of the pairwise correlation coefficients (r2) of the variants […]


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CAVIARBF institution(s)
Division of Biostatistics, Mayo Clinic, Rochester, MN, USA; Mayo Clinic Vaccine Research Group, Mayo Clinic, Rochester, MN, USA
CAVIARBF funding source(s)
Supported by the U.S. Public Health Service, National Institutes of Health, contract grant no. GM065450 and by federal funds from the National Institute of Allergies and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract no. HHSN272201000025C.

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