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

Citation

Ritchie, ME and Liu, R and Carvalho, BS and Bahlo, M and Booth, DR and Broadley, SA and Brown, MA and Foote, SJ and Griffiths, LR and Kilpatrick, TJ and Lechner-Scott, J and Moscato, P and Perreau, VM and Rubio, JP and Scott, RJ and Stankovich, J and Stewart, GJ and Taylor, BV and Wiley, J and Clarke, G and Cox, MB and Csurhes, PA and Danoy, P and Dickinson, JL and Drysdale, K and Field, J and Greer, JM and Guru, P and Hadler, J and Hoban, E and McMorran, BJ and Jensen, CJ and Johnson, LJ and McCallum, R and Merriman, M and Merriman, T and Polanowski, A and Pryce, K and Tajouri, L and Whittock, L and Wilkins, EJ and Browning, BL and Browning, SR and Perera, D and Butzkueven, H and Carroll, WM and Chapman, C and Kermode, AG and Marriott, M and Mason, D and Heard, RN and Pender, MP and Slee, M and Tubridy, N and Willoughby, E and Irizarry, RA, Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips, BMC Bioinformatics, 12 Article 68. ISSN 1471-2105 (2011) [Refereed Article]


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Licensed under Creative Commons Attribution 2.0 Generic (CC BY 2.0) http://creativecommons.org/licenses/by/2.0/

DOI: doi:10.1186/1471-2105-12-68

Abstract

Background: Illumina’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. Results: In 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. Conclusions: CRLMM, 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.

Item Details

Item Type:Refereed Article
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Probability theory
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in economics
UTAS Author:Foote, SJ (Professor Simon Foote)
UTAS Author:Stankovich, J (Dr Jim Stankovich)
UTAS Author:Taylor, BV (Professor Bruce Taylor)
UTAS Author:Dickinson, JL (Professor Joanne Dickinson)
UTAS Author:Drysdale, K (Ms Karen Drysdale)
UTAS Author:Guru, P (Mrs Preethi Mayura Guru)
UTAS Author:Hoban, E (Ms Ella Hoban)
UTAS Author:McMorran, BJ (Associate Professor Brendan McMorran)
UTAS Author:Polanowski, A (Ms Andrea Polanowski)
UTAS Author:Whittock, L (Dr Lucy Whittock)
UTAS Author:Perera, D (Miss Devindri Perera)
ID Code:76019
Year Published:2011
Web of Science® Times Cited:39
Deposited By:Menzies Institute for Medical Research
Deposited On:2012-02-22
Last Modified:2017-10-06
Downloads:329 View Download Statistics

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