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Automating sequence-based detection and genotyping of SNPs from diploid samples

Nature Genetics volume 38, pages 375–381 (2006)Cite this article

Abstract

The detection of sequence variation, for which DNA sequencing has emerged as the most sensitive and automated approach, forms the basis of all genetic analysis. Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47–90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. This algorithm is implemented in a software package, PolyPhred version 5.0, which is freely available for academic use.

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Figure 1: Sequence traces (chromatograms) for four individuals.
Figure 2: Removal of systematic variation in peak height improves discrimination between heterozygotes and homozygotes.
Figure 3: Missed SNP rate versus false discovery rate for different data sets.
Figure 4: Dependence of performance on sequence quality.

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Acknowledgements

The authors thank past and present members of the Nickerson lab for compiling the databases that were used to develop, train and test our algorithm. This work was supported by US National Institutes of Health (NIH) grants (1RO1HG/LM-02585 to M.S., and ES-15478 and HL-66682 to D.A.N.). P.S. was supported by an NIH training grant (T32 HG00035-06).

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Authors and Affiliations

  1. Department of Statistics, University of Washington, Seattle, 98195, Washington, USA

    Matthew Stephens & Paul Scheet

  2. Department of Genome Sciences, University of Washington, Seattle, 98195, Washington, USA

    Matthew Stephens, James S Sloan, P D Robertson & Deborah A Nickerson

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  1. Matthew Stephens
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  2. James S Sloan
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  3. P D Robertson
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  4. Paul Scheet
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  5. Deborah A Nickerson
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Correspondence to Matthew Stephens.

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Competing interests

POLYPHRED is freely available for academic purposes, but a licensing fee is charged for commercial use, which predominantly funds further software and methods development.

Supplementary information

Supplementary Fig. 1

Illustration of how our method removes systematic variation in secondary peak height to improve discrimination between heterozygotes and homozygotes. (PDF 95 kb)

Supplementary Fig. 2

Relationship between score assigned by our method to each genotype and genotyping agreement rate. (PDF 20 kb)

Supplementary Fig. 3

Illustration of tiled and double-coverage sequencing designs. (PDF 23 kb)

Supplementary Fig. 4

Relationship between rates of agreements between genotypes and the percentage of uncalled genotypes, as threshold for calling genotypes varies. (PDF 18 kb)

Supplementary Methods (PDF 83 kb)

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Stephens, M., Sloan, J., Robertson, P. et al. Automating sequence-based detection and genotyping of SNPs from diploid samples. Nat Genet 38, 375–381 (2006). https://doi.org/10.1038/ng1746

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