Jung-Ying Tzeng

CCRET: CNV Collapsing Random Effects Test

CCRET Overview

CCRET is a random effects approach applicable to variants measured on a multi-categorical scale, collectively modeling the effects of multiple CNV features, and is robust to etiological heterogeneity. CCRET has the following key features.
  1. CCRET converts the source CNV data to three input matrixes in order to store the different features of CNVs, i.e., dosage ("DS"), length ("Len"), and gene intersection ("GI"). For "DS" and "Len" matrixes, we use CNV regions as the "locus" unit. For "GI" matrix, we use genes as the "locus" units.
  2. CCRET models the covariates and background CNV features using fixed effects as did in Raychaudhuri et al. (2010), and models the CNV feature of interest using random effects in order to retain the locus-specific details and to account for both between-locus and within-locus etiological heterogeneity.
  3. CCRET quantifies the genetic similarity between any two individuals based on the CNV feature of interest, which is then used to depict the covariance among the CNV effects of different individuals (i.e, the more similar the genetic feature between two individuals is, the more correlated their CNV effects would be). When calculating genetic similarity, we factorize the multi-categorical allele values recorded in the input matrices. Consequently, alleles with opposite effects within a locus are not lumped together when computing similarity, which makes CCRET robust against within-locus heterogeneity.
  4. Under the mixed effects model framework, the aggregate CNV effect can be evaluated by examining the significance of the variance component.


CCRET contains several R functions. The R scripts as well as example datasets can be downloaded by clicking ccret.tar.gz (updated on 9/26/2015). If in unix, please type the following two commands after downloading to extract the files.

gunzip ccret.tar.gz

tar -xvf ccret.tar

Instruction and Reference

The instruction can be downloaded here. The ccret paper is

Tzeng J.Y., Magnusson, P.K.E., Sullivan, P.F., The Swedish Schizophrenia Consortium, Szatkiewicz, J. (2015) A new method for detecting associations with rare copy-number variants. PLoS Genetics, in press

Contant Information

For any questions for using ccret or any suggestions, please contact

Jung-Ying Tzeng (jytzeng@stat.ncsu.edu), Department of Statistics and Bioinformatics Research Center, Campus Box 7566, North Carolina State University, Raleigh 27695 NC