Implementation of genetic association tests that incorporate the rank-based inverse normal transformation (INT). These tests are broadly indicated for traits with continuous residual distributions. In the presence of non-normal residuals, INT-based tests robustly control the type I error, whereas standard linear regression may not. Moreover, INT-based tests dominate standard linear regression in terms of power. There are two main strategies for incorporating the INT in association analysis. In direct INT (D-INT), the trait is directly transformed. In indirect INT (I-INT), residuals are formed prior to transformation. Neither D-INT nor I-INT is uniformly most powerful. The INT omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach.
Version: | 0.6.0 |
Depends: | R (≥ 3.2.2) |
Imports: | abind, foreach, plyr, Rcpp |
LinkingTo: | Rcpp, RcppEigen |
Suggests: | cowplot, ggplot2, knitr, reshape2, rmarkdown |
Published: | 2019-01-12 |
Author: | Zachary McCaw [aut, cre] |
Maintainer: | Zachary McCaw <zmccaw at g.harvard.edu> |
License: | GPL-3 |
NeedsCompilation: | yes |
CRAN checks: | RNOmni results |
Reference manual: | RNOmni.pdf |
Vignettes: |
Rank Normal Omnibus Association Test |
Package source: | RNOmni_0.6.0.tar.gz |
Windows binaries: | r-devel: RNOmni_0.6.0.zip, r-release: RNOmni_0.6.0.zip, r-oldrel: RNOmni_0.6.0.zip |
OS X binaries: | r-release: RNOmni_0.6.0.tgz, r-oldrel: RNOmni_0.5.0.tgz |
Old sources: | RNOmni archive |
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