Explorer Quantifying single nucleotide variant detection sensitivity in exome sequencing

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. Background Targeted capture and sequencing of human exons (exomeseq) is an increasingly popular addition to genotyping microarrays and a lower cost alternative to whole genome sequencing for researchers investigating heritable traits [1]. The complete human protein-coding exome comprises only 1% of the human genome, and it is typically in what would be considered the easier fraction to sequence and align uniquely to a reference genome. Most of the causal disease variants identified to date have been in protein-coding exonic sequence [2], and there are well established experimental paradigms to explore the functional consequences of amino acid changing variants. As *Correspondence: alison.meynert@igmm.ed.ac.uk; martin.taylor@igmm.ed.ac.uk 1MRC Human Genetics Unit, MRC Institute for Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK Full list of author information is available at the end of the article the approach is sequence based, there is the potential to ascertain all simple sequence variants within the targeted regions in each member of a study cohort. Exome-seq has been successfully applied to identify causal variants in a number of Mendelian genetic disorders [3-5]. These study designs often require only tiny cohorts with little or no pedigree information, making many diseases tractable to genetic study for the first time. Exome-seq is also being used to study somatic mutations in cancers [6] and has been proposed as a method to study complex traits where the ability to detect rare variants along with new variant discovery make it an ideal complement to association studies [7,8]. Causal variants for complex diseases previously identified through microarray-based association studies have been shown to localize in or near exonic sequence [9], and human exomes contain an excess of rare non-synonymous coding variants that could explain some part of the missing heritability problem [10]. © 2013 Meynert et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Meynert et al. BMC Bioinformatics 2013, 14:195 Page 2 of 9 http://www.biomedcentral. com/1471-2105/14/195 For all re-sequencing projects (where there is a closely related reference sequence that can be used for assembly, rather than requiring de novo sequence assembly), the total amount of usable sequence is considerably less than the amount that comes off the sequencing machines. Two standard analysis steps reduce the amount. First, reads that cannot be uniquely mapped to the genome cannot be used. A read may be unmappable because a) it contains too many sequencing errors, b) it is relatively error free but contains too many non-reference bases, c) it maps to an insertion, deletion, or structural variant, or d) it maps to multiple positions in the genome equally well. Second, duplicate reads are removed. Duplication is generally defined as having identical position in the genome, but may or may not include identical read sequence. Removing duplicate reads minimises issues with PCR amplification and improves the specificity of variant calling [11]. Exome sequencing suffers from the additional problem of off-target reads, those generated from the genomic sample but outside the regions targeted for capture [12]. These reads can certainly be used for polymorphism discovery, but they are unlikely to map to protein-coding sequence or even regulatory features. After applying all three of these reductions, the percentage of usable ontarget reads can be as low as 35% [3,4]. Read mapping also demonstrates a bias towards the reference sequence: reads that contain too many alternate alleles align with lower scores to the reference genome than reads with only reference alleles [13]. As a consequence of heterogeneity in target capture efficiency, amplification and the mapping of reads to the reference genome, usable reads are unevenly distributed over the targeted regions. There has rightly been a great deal of focus on accurately measuring the specificity of single nucleotide variant (SNV) calls in next-generation sequence data (e.g. [11]). But it is also hugely important for study design and the interpretation of results to understand the sensitivity attained. How many SNVs are likely to have been missed and where are they most likely to be located? Ajay and colleagues [14] have gone some way to addressing this problem by showing that at the commonly used threshold of 30X coverage, 30% of protein coding regions have insufficient read depth to confidently call genotypes. We set out to extend this observation to a wide range of read depths and compare the consistency of SNV calling sensitivity between genomic regions. To measure the sensitivity of SNV detection we have applied down-sampling to deeply sequenced exomes and asked how well the down-sampled alignments perform at calling known SNVs that are both present in the full alignment and genotyped by the HapMap Phase III project [15]. This approach provides us with a goldstandard set of SNVs that can be generated for any deeply sequenced human target and allows cross-comparison of platforms and laboratories without the need to repeatedly and deeply sequence a known reference. With this gold-standard we have investigated the relationship of SNV detection sensitivity to both average coverage over the entire targeted exome and per-nucleotide coverage for each variant. Calibrating SNV detection sensitivity with pernucleotide coverage allows us to estimate how many genuine SNVs may have been missed in a specific targeted region and evaluate whether an exon or gene can be considered thoroughly screened for SNVs in an individual. It also provides the potential to correct the observed rate of polymorphism for biases in coverage between genomic regions. This is crucial for the robust application of population based measures of mutation, selection and demography [16] that are becoming increasingly important for the functional interpretation of non-coding regulatory sequences [17]. The relationship between mean on-target read depth and SNV sensitivity is important for study design where a researcher must generally decide a priori how to handle the trade-off between the number of individuals to study and the depth at which to sequence them. To explore this relationship we have collected exome sequence data, generated at unusually deep coverage, from separate laboratories and utilising four different capture technologies. Our principal aim here is not a comprehensive comparison of competing exome capture technologies, such studies have been published elsewhere [12,18-20], rather to understand real world variances in target capture efficiency and uniformity. In the case of rare genetic disorders, the focus is on how much sequencing is required to confidently identify the majority of variants in the targeted regions of an individual. We address the problem in the context of both autosomal dominant and recessive disorders, in which both heterozygous and homozygous mutations may be implicated. In this work, we asked what level of mean on-target read depth is required to accurately identify a given percentage of SNVs, where accuracy includes position, alleles, and genotype. Results and discussion Simulating shallower sequencing Thirty capture-targeted and sequenced human exomes, encompassing two different laboratories and four different capture methods, were aligned to the reference genome using a standard protocol (see Methods, Additional file 1: Table S1a,b). Twelve of these exomes are part of an ongoing disease study, subsequ