CAVIARBF statistics

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Citations per year

Number of citations per year for the bioinformatics software tool CAVIARBF

Tool usage distribution map

This map represents all the scientific publications referring to CAVIARBF per scientific context
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CAVIARBF specifications


Unique identifier OMICS_11040
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
Stability Stable
Rcpp, RcppEigen, glmnet
Maintained Yes




No version available



  • person_outline Daniel Schaid

Publication for CAVIAR Bayes Factor

CAVIARBF citations


Constraints on eQTL Fine Mapping in the Presence of Multisite Local Regulation of Gene Expression

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

[…] lly 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 l […]


JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects

Genet Epidemiol
PMCID: 4817278
PMID: 27027514
DOI: 10.1002/gepi.21953

[…] We compare the performance of JAM to the fine‐mapping frameworks CAVIARBF and FINEMAP, which also facilitate multivariate sparse Bayesian model selection using summary statistics, as well as Yang et al.'s stepwise selection framework (COJO within the GCTA software […]


Strategies for fine mapping complex traits

Hum Mol Genet
PMCID: 4572002
PMID: 26157023
DOI: 10.1093/hmg/ddv260
call_split See protocol

[…] ate 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 in each asso […]

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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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