GWCNV is a genome-wide algorithm for detecting CNV associations with diseases. Instead of calling CNVs from individual data and performing the association tests based on estimated CNVs, this algorithm can detect the associations between CNVs and diseases directly from a transformation of intensity data. GWCNV was implemented by combining the existing hidden Markov model (HMM) defined in PennCNV and the logistic regression model. It is powerful and sensitive in detecting small CNV associations, and retains high power for large CNVs.

To run GWCNV, users need to perform a modified version of PennCNV algorithm on the data to generate the input files. GWCNV is a Python program which utilizes the R functions for statistical computation. Utility functions for handling the data and retrieve the results are provided in the software.

Theoretically, GWCNV can handle both Illumina and Affymetrix platforms, but currently only Illumina data were tested in the software. And currently, the programs only were tested in Unix/Linux environment.

Please contact Yaji Xu at for any questions about GWCNV.