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Open Access Methodology article

Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias

John P Didion123, Hyuna Yang4, Keith Sheppard5, Chen-Ping Fu6, Leonard McMillan6, Fernando Pardo-Manuel de Villena123* and Gary A Churchill5*

Author Affiliations

1 Department of Genetics, University of North Carolina at Chapel Hill, CB 7264, Chapel Hill, North Carolina, 27599-7264, USA

2 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, CB 7295, Chapel Hill, North Carolina, 27599-7295, USA

3 Carolina Center for Genome Science, University of North Carolina at Chapel Hill, CB 7264, Chapel Hill, North Carolina, 27599-7264, USA

4 Department of Biostatistics and Bioinformatics, Duke University Medical Center, Box 2721, Durham, NC, 27710, USA

5 Center for Genome Dynamics, The Jackson Laboratory, 600 Main Street, Bar Harbor, Maine, 04609, USA

6 Department of Computer Science, University of North Carolina at Chapel Hill, CB 3175, Chapel Hill, North Carolina, 27599-3175, USA

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BMC Genomics 2012, 13:34  doi:10.1186/1471-2164-13-34

Published: 19 January 2012

Abstract

Background

High-density genotyping arrays that measure hybridization of genomic DNA fragments to allele-specific oligonucleotide probes are widely used to genotype single nucleotide polymorphisms (SNPs) in genetic studies, including human genome-wide association studies. Hybridization intensities are converted to genotype calls by clustering algorithms that assign each sample to a genotype class at each SNP. Data for SNP probes that do not conform to the expected pattern of clustering are often discarded, contributing to ascertainment bias and resulting in lost information - as much as 50% in a recent genome-wide association study in dogs.

Results

We identified atypical patterns of hybridization intensities that were highly reproducible and demonstrated that these patterns represent genetic variants that were not accounted for in the design of the array platform. We characterized variable intensity oligonucleotide (VINO) probes that display such patterns and are found in all hybridization-based genotyping platforms, including those developed for human, dog, cattle, and mouse. When recognized and properly interpreted, VINOs recovered a substantial fraction of discarded probes and counteracted SNP ascertainment bias. We developed software (MouseDivGeno) that identifies VINOs and improves the accuracy of genotype calling. MouseDivGeno produced highly concordant genotype calls when compared with other methods but it uniquely identified more than 786000 VINOs in 351 mouse samples. We used whole-genome sequence from 14 mouse strains to confirm the presence of novel variants explaining 28000 VINOs in those strains. We also identified VINOs in human HapMap 3 samples, many of which were specific to an African population. Incorporating VINOs in phylogenetic analyses substantially improved the accuracy of a Mus species tree and local haplotype assignment in laboratory mouse strains.

Conclusion

The problems of ascertainment bias and missing information due to genotyping errors are widely recognized as limiting factors in genetic studies. We have conducted the first formal analysis of the effect of novel variants on genotyping arrays, and we have shown that these variants account for a large portion of miscalled and uncalled genotypes. Genetic studies will benefit from substantial improvements in the accuracy of their results by incorporating VINOs in their analyses.