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Open Access Highly Accessed Methodology article

KRLMM: an adaptive genotype calling method for common and low frequency variants

Ruijie Liu1, Zhiyin Dai1, Meredith Yeager2, Rafael A Irizarry3* and Matthew E Ritchie145*

Author Affiliations

1 Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia

2 Cancer Genomics Research Laboratory, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 20877, USA

3 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, CLSB 11007, 450 Brookline Ave, Boston, Massachusetts 02215, USA

4 Department of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia

5 Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia

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BMC Bioinformatics 2014, 15:158  doi:10.1186/1471-2105-15-158

Published: 23 May 2014

Abstract

Background

SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering.

Results

Here we develop KRLMM, a new method for converting raw intensities into genotype calls that aims to overcome this issue. Our method is unique in that it applies careful between sample normalization and allows a variable number of clusters k (1, 2 or 3) for each SNP, where k is predicted using the available data. We compare our method to four genotyping algorithms (GenCall, GenoSNP, Illuminus and OptiCall) on several Illumina data sets that include samples from the HapMap project where the true genotypes are known in advance. All methods were found to have high overall accuracy (> 98%), with KRLMM consistently amongst the best. At low minor allele frequency, the KRLMM, OptiCall and GenoSNP algorithms were observed to be consistently more accurate than GenCall and Illuminus on our test data.

Conclusions

Methods that tailor their approach to calling low frequency variants by either varying the number of clusters (KRLMM) or using information from other SNPs (OptiCall and GenoSNP) offer improved accuracy over methods that do not (GenCall and Illuminus). The KRLMM algorithm is implemented in the open-source crlmm package distributed via the Bioconductor project (http://www.bioconductor.org webcite).

Keywords:
Genotyping; Clustering; Microarray data analysis