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Deletion, insertion and duplication events giving rise to copy number variations (CNVs) have been found genome-wide in the humans and other species. Such genomic aberrations were identified already more than a decade ago using array-based comparative hybridization. They can also be detected using data from SNP genotyping arrays, typically by combining the intensities of the two probes for a given SNP and comparing to the same SNP from other arrays (thus deriving a copy number ratio). Significant shift from the baseline (unit ratio or zero log ratio) reflects copy number changes. Such changes can be identified in many ways, for example, one can use segmentation algorithms to partition the signal then try to classify such segments into gain, copy neutral and loss status. Yet, for large datasets, one can take advantage of the signal distribution at each SNP, and cluster each individual from the distribution into a component that would reflect a given copy number change.

We developped a Gaussian Mixture Model, which detect copy number variation from the distribution of copy number ratios. From the data, it will fit one component for each of the following copy number states: deletion, copy-neutral, 1 and 2 additional copy; with a constraint on the difference between the mixture means. Then for a given individual, it will determine the probabilities for each copy number state and compute the expected copy number (dosage).



The GMM algorithm is licensed under the GNU General Public License, version 2 or later. For details, see


The GMM can be applied to identify CNVs from any rectangular matrix of copy number ratio.


If you have the MATLAB software, you can directly use the source code.

Otherwise, you will need to download the Matlab Component Runtime to use the executables (see Download section).


Description File Name Size md5sum
MCR for 64-bit Linux[1] 224M 451c54a811b3e01402b6a46a1b814c4d
Linux Executables[2] 556k bd579f39c340a50de2bb80a649643be3
Source code[3] 16k 3cb7799bf3e180b33a6742ef382b105e
Example output files[4] 460k 6b621a6a8e279697f610db35810777ce