Efficient Score Statistics for Genome-Wide SNP Set Analysis
An implementation of the use of efficient score statistics
in genome-wide SNP set analysis with complex traits. Three standard score statistics
(Cox, binomial, and Gaussian) are provided, but the package is easily extensible to
include others. Code implementing the inferential procedure is primarily written in C++ and
utilizes parallelization of the analysis to reduce runtime. A supporting function offers
simple computation of observed, permutation, and FWER and FDR adjusted p-values.
RSNPset v0.5.3 (Release date: 2017-12-11)
- Minor bug fix for example code in function documentation
RSNPset v0.5.2 (Release date: 2017-02-20)
- Minor bug fix for note about registering native routines under r-devel
RSNPset v0.5.1 (Release date: 2017-02-16)
- Minor bug fix for warnings in vingette under r-devel
- Changed names of two returned columns in rsnpset.pvalue() (and associated documentation) for consistency
RSNPset v0.5 (Release date: 2015-11-05)
- Authors now recommend pruning SNPs when using the Cox score statistic (method=”cox”)
- Documentation updates and corrections
RSNPset v0.4 (Release date: 2015-02-11)
- Addition of NEWS file
- Implementation of argument X (covariates) in rsnpset() for score="gaussian"
- New option/default for rsnpset() argument r.method="monte carlo"
- New default for rsnpset() argument v.permute="FALSE"
- New attribute for RSNPset S3 objects: "r.method", now expected by summary.RSNPset()
- Elements of results list for RSNPset S3 objects renamed to "Observed", "Replication.1", "Replication.2", ...
- Argument pval.transform=TRUE now allowed in rspset.pvalue() for rsnpset() results with return.rank=FALSE
RSNPset v0.3 (Release date: 2014-12-16)