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

BMC Bioinformatics

Software

Alejandro Cáceres¹  Juan R González²  Roger Pique-Regi³ 

[1] Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader, 88, 08003, Barcelona, Spain;Institut Municipal d'Investigació Mèdica (IMIM), Doctor Aiguader, 88, 08003, Barcelona, Spain;Center for Research in Environmental Epidemiology (CREAL), Doctor Aiguader, 88, 08003, Barcelona, Spain;Institut Municipal d'Investigació Mèdica (IMIM), Doctor Aiguader, 88, 08003, Barcelona, Spain;CIBER Epidemiología y Salud Pública (CIBERESP), Doctor Aiguader, 88, 08003, Barcelona, Spain;Signal and Image Processing Institute, Viterbi School of Engineering, University of Southern California, EEB 400, 3740 McClintock Ave, 90089-2564, Los Angeles, CA, USA;Division of Hematology - Oncology, Department of Pediatrics, Childrens Hospital Los Angeles, 4650 Sunset Boulevard Los Angeles, 90027, CA, USA;

关键词: Copy Number Variation;    Copy Number Change;    Multiple Correspondence Analysis;    HapMap Sample;    Circular Binary Segmentation;   

DOI  :  10.1186/1471-2105-11-380

 received in 2009-12-17, accepted in 2010-07-16,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundGenome-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.ResultsHere 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.ConclusionsThe package provides the tools for creating a complete integrated pipeline from data normalization to statistical association. It can effciently handle a massive volume of data consisting of millions of genetic markers and hundreds or thousands of samples with very accurate results.

【 授权许可】

CC BY   
© Pique-Regi et al; licensee BioMed Central Ltd. 2010