Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips

BMC Bioinformatics

Methodology Article

Ruijie Liu¹  Matthew E Ritchie²  Rafael A Irizarry³  Benilton S Carvalho⁴ 

[1] Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, 3052, Parkville, Victoria, Australia;Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, 3052, Parkville, Victoria, Australia;Department of Medical Biology, The University of Melbourne, 3010, Parkville, Victoria, Australia;Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, North Wolfe Street E3035, 21205, Baltimore, MD, USA;Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, CB2 0RE, Cambridge, UK;

关键词: HapMap Project;    HapMap Data;    Chip Type;    Average Posterior Probability;    Component Mixture Model;   

DOI  :  10.1186/1471-2105-12-68

 received in 2010-11-08, accepted in 2011-03-08,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundIllumina's Infinium SNP BeadChips are extensively used in both small and large-scale genetic studies. A fundamental step in any analysis is the processing of raw allele A and allele B intensities from each SNP into genotype calls (AA, AB, BB). Various algorithms which make use of different statistical models are available for this task. We compare four methods (GenCall, Illuminus, GenoSNP and CRLMM) on data where the true genotypes are known in advance and data from a recently published genome-wide association study.ResultsIn general, differences in accuracy are relatively small between the methods evaluated, although CRLMM and GenoSNP were found to consistently outperform GenCall. The performance of Illuminus is heavily dependent on sample size, with lower no call rates and improved accuracy as the number of samples available increases. For X chromosome SNPs, methods with sex-dependent models (Illuminus, CRLMM) perform better than methods which ignore gender information (GenCall, GenoSNP). We observe that CRLMM and GenoSNP are more accurate at calling SNPs with low minor allele frequency than GenCall or Illuminus. The sample quality metrics from each of the four methods were found to have a high level of agreement at flagging samples with unusual signal characteristics.ConclusionsCRLMM, GenoSNP and GenCall can be applied with confidence in studies of any size, as their performance was shown to be invariant to the number of samples available. Illuminus on the other hand requires a larger number of samples to achieve comparable levels of accuracy and its use in smaller studies (50 or fewer individuals) is not recommended.

【 授权许可】

CC BY   
© Ritchie et al; licensee BioMed Central Ltd. 2011