GStream: improving SNP and CNV coverage on genome-wide association studies

• Published in: PloS one Vol. 8; no. 7; p. e68822
• Main Authors: Alonso, Arnald, Marsal, Sara, Tortosa, Raül, Canela-Xandri, Oriol, Julià, Antonio
• Format: Journal Article Publication
• Language: English
• Published: United States Public Library of Science 03.07.2013; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Aplicacions de la informàtica; Arrays; Bioinformàtica; Biological effects; Biology; Comparative analysis; Computational Biology - methods; Computer applications; Computer Science; Computing time; Cytogenetics; Datasets; DNA Copy Number Variations; Genes; Genetic aspects; Genetics; Genome-wide association studies; Genome-Wide Association Study - methods; Genomes; Genomics; Genotyping; Genòmica; Health risks; Hybridization; Informàtica; Linkage disequilibrium; Methods; Polymorphism, Single Nucleotide; Reproducibility of Results; Rheumatology; Sensitivity and Specificity; Single-nucleotide polymorphism; SNP genotyping software; Software; Studies; Àrees temàtiques de la UPC; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0068822

We present GStream, a method that combines genome-wide SNP and CNV genotyping in the Illumina microarray platform with unprecedented accuracy. This new method outperforms previous well-established SNP genotyping software. More importantly, the CNV calling algorithm of GStream dramatically improves the results obtained by previous state-of-the-art methods and yields an accuracy that is close to that obtained by purely CNV-oriented technologies like Comparative Genomic Hybridization (CGH). We demonstrate the superior performance of GStream using microarray data generated from HapMap samples. Using the reference CNV calls generated by the 1000 Genomes Project (1KGP) and well-known studies on whole genome CNV characterization based either on CGH or genotyping microarray technologies, we show that GStream can increase the number of reliably detected variants up to 25% compared to previously developed methods. Furthermore, the increased genome coverage provided by GStream allows the discovery of CNVs in close linkage disequilibrium with SNPs, previously associated with disease risk in published Genome-Wide Association Studies (GWAS). These results could provide important insights into the biological mechanism underlying the detected disease risk association. With GStream, large-scale GWAS will not only benefit from the combined genotyping of SNPs and CNVs at an unprecedented accuracy, but will also take advantage of the computational efficiency of the method.