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Updated: Jun 10, 2026
Detection of Copy Number Alterations Using Single Cell Sequencing
Published on: February 17, 2017
R-Gada: a fast and flexible pipeline for copy number analysis in association studies
Roger Pique-Regi1, Alejandro Cáceres, Juan R González
1Signal and Image Processing Institute, Viterbi School of Engineering, University of Southern California, EEB 400, 3740 McClintock Ave, Los Angeles, CA 90089-2564, USA. rpique@ieee.org
Background:
Genome-wide association studies (GWAS) using Copy Number Variation (CNV) are becoming a central focus of genetic research. CNVs have successfully provided target genome regions for some disease conditions where simple genetic variation (i.e., SNPs) has previously failed to provide a clear association.
Results:
Here we present a new R package, that integrates: (i) data import from most common formats of Affymetrix, Illumina and aCGH arrays; (ii) a fast and accurate segmentation algorithm to call CNVs based on Genome Alteration Detection Analysis (GADA); and (iii) functions for displaying and exporting the Copy Number calls, identification of recurrent CNVs, multivariate analysis of population structure, and tools for performing association studies. Using a large dataset containing 270 HapMap individuals (Affymetrix Human SNP Array 6.0 Sample Dataset) we demonstrate a flexible pipeline implemented with the package. It requires less than one minute per sample (3 million probe arrays) on a single core computer, and provides a flexible parallelization for very large datasets. Case-control data were generated from the HapMap dataset to demonstrate a GWAS analysis.
Conclusions:
The package provides the tools for creating a complete integrated pipeline from data normalization to statistical association. It can efficiently handle a massive volume of data consisting of millions of genetic markers and hundreds or thousands of samples with very accurate results.
