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Updated: Jun 3, 2026
13:33
Infinium Assay for Large-scale SNP Genotyping Applications
Published on: November 19, 2013
Comparing genotyping algorithms for Illumina's Infinium whole-genome SNP BeadChips
Matthew E Ritchie1, Ruijie Liu, Benilton S Carvalho
1Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia. mritchie@wehi.edu.au
BMC Bioinformatics
|March 10, 2011
Summary
Four genotype calling algorithms were compared for accuracy in genetic studies. CRLMM and GenoSNP generally outperformed GenCall, while Illuminus performance varied with sample size.
Area of Science:
- Genetics
- Bioinformatics
- Genomics
Background:
- Illumina's Infinium SNP BeadChips are widely used in genetic research.
- Accurate genotype calling from raw intensity data is crucial for genetic analysis.
- Several algorithms exist for processing SNP intensity data into genotype calls.
Purpose of the Study:
- To compare the accuracy of four genotype calling algorithms: GenCall, Illuminus, GenoSNP, and CRLMM.
- To evaluate algorithm performance on known true genotypes and a genome-wide association study dataset.
- To assess the impact of sample size and sex on algorithm performance.
Main Methods:
- Comparison of GenCall, Illuminus, GenoSNP, and CRLMM algorithms.
- Evaluation using datasets with known true genotypes.
- Analysis of performance on a genome-wide association study dataset.
- Assessment of performance for X chromosome SNPs and low minor allele frequency SNPs.
Main Results:
- CRLMM and GenoSNP consistently outperformed GenCall.
- Illuminus performance is sample-size dependent, improving with more samples.
- Sex-dependent models (Illuminus, CRLMM) are superior for X chromosome SNPs.
- CRLMM and GenoSNP showed higher accuracy for low minor allele frequency SNPs.
- Sample quality metrics from all methods showed high agreement.
Conclusions:
- CRLMM, GenoSNP, and GenCall are reliable across various study sizes.
- Illuminus requires a large sample size for optimal performance; not recommended for small studies (<50 individuals).
- Algorithm choice can impact accuracy, especially for specific SNP types and study designs.
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