Abstract
BackgroundSNP 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.ResultsHere 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.ConclusionsMethods 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).
Highlights
Single-nucleotide polymorphism (SNP) genotyping microarrays have revolutionized the study of complex disease
We analyze datasets from a number of platforms and highlight the benefit of careful signal adjustment between samples to optimize calling accuracy. We compare this approach to four existing algorithms (GenCall, GenoSNP, Illuminus, and OptiCall) by analyzing data sets with increasing coverage of low frequency/rare variants, and compare the performance of these methods in terms of accuracy at varying minor allele frequency
For performance comparison, concordance between the genotype calls made by each method and the independent calls obtained from the HapMap database were used to calculate accuracy
Summary
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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
