R-Gada: a fast and flexible pipeline for copy number analysis in association studies

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.

Author: Roger Pique-RegiAlejandro CaceresJuan Gonzalez
Credits/Source: BMC Bioinformatics 2010, 11:380

Published on: 2010-07-16

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