Abstract
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.
Highlights
Over the last years, Genome-Wide Association Studies (GWAS) using microarray-based technology have played an important role in the identification of common genetic variations and their relationship with disease susceptibility [1,2,3,4]
Our new method achieves a superior accuracy in both Single Nucleotide Polymorphisms (SNPs) and Copy Number Variants (CNVs) genotyping compared to well-established methods
Performance assessment of SNP genotyping For each available Illumina platform, the golden standard genotype calls were compared with the calls generated by GStream, GenoSNP, GenCall and M3 software tools
Summary
Genome-Wide Association Studies (GWAS) using microarray-based technology have played an important role in the identification of common genetic variations and their relationship with disease susceptibility [1,2,3,4]. Recent studies based either on specific CGH arrays or genotyping microarrays have demonstrated the importance of CNVs due to their global contribution to the human genome variation, their functional impact and their role in human disease [7,9,10,11,12,13,14]. Some of these reference studies have contributed to elaborate a map of regions containing highly polymorphic CNVs called Copy Number Polymorphisms (CNPs) [9,10,15]. These common variations have appeared as a significant area of interest, since they segregate in the population at an appreciable frequency and their analysis over big sample collections could potentially lead to significant disease risk associations
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