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. 2013 Jun 18:14:195.
doi: 10.1186/1471-2105-14-195.

Quantifying single nucleotide variant detection sensitivity in exome sequencing

Alison M Meynert  1 , Louise S Bicknell, Matthew E Hurles, Andrew P Jackson, Martin S Taylor
Affiliations

Quantifying single nucleotide variant detection sensitivity in exome sequencing

Alison M Meynert et al. BMC Bioinformatics. .

Abstract

Background: The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.

Results: Using down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give "power estimates" for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5-15% of heterozygous and 1-4% of homozygous SNVs in the targeted regions will be missed.

Conclusions: Non-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the "missing heritability" of quantitative traits.

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Figures

Figure 1
Figure 1
SNV detection sensitivity as a function of depth at the polymorphic site. Sensitivity of at least 95% is reached at depths of 3X for homozygous SNVs and 13X for heterozygous SNVs. Raw data for these curves is available (Additional file 2).
Figure 2
Figure 2
SNV detection sensitivity across the example gene FERMT3 for the exome HW07. Grey vertical lines denote exon boundaries.
Figure 3
Figure 3
SNV detection sensitivity as a function of mean on-target read depth. Mean on-target read depth is calculated using bases aligned to the set of target regions covered by all exome capture platforms.
See this image and copyright information in PMC

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