A SNP profiling panel for sample tracking in whole-exome sequencing studies
© Pengelly et al.; licensee BioMed Central Ltd. 2013
Received: 22 July 2013
Accepted: 16 September 2013
Published: 27 September 2013
Whole-exome sequencing provides a cost-effective means to sequence protein coding regions within the genome, which are significantly enriched for etiological variants. We describe a panel of single nucleotide polymorphisms (SNPs) to facilitate the validation of data provenance in whole-exome sequencing studies. This is particularly significant where multiple processing steps necessitate transfer of sample custody between clinical, laboratory and bioinformatics facilities. SNPs captured by all commonly used exome enrichment kits were identified, and filtered for possible confounding properties. The optimised panel provides a simple, yet powerful, method for the assignment of intrinsic, highly discriminatory identifiers to genetic samples.
Whole-exome sequencing (WES) is presently one of the most efficient means of identifying aetiological genetic mutations , minimising some of the challenges associated with whole-genome sequencing, such as high cost and data processing burden, analysis and interpretation. In WES, protein-coding regions of the genome are targeted and enriched via specific hybridisation of genomic fragments with complementary oligonucleotides, or 'baits’. These targeted regions are then sequenced using high throughput next-generation sequencing (NGS) technologies .
The high start-up investment required for in-house WES is currently prohibitive to many groups so sample preparation and/or sequencing is commonly outsourced. This transference of sample custody, combined with the complex sample preparation workflow, makes sample mix-ups possible, and difficult to detect. In both clinical and research contexts, ensuring provenance of data is essential to allow the accurate assignment of clinical details to sequence data. It is possible that samples may be misidentified at any stage of the analytical process, both in vitro and in silico. Therefore, sample tracking must be contiguous throughout both data generation and analysis.
Consequent to sample mix-ups in a research setting, erroneous data and sample matching may result in a loss of power for identification of causal variants . In a clinical setting, this may lead to delayed or inaccurate reporting of results to patients. Whilst good practice in the handling of samples and increased laboratory automation minimises potential for error, additional checkpoints are still required to support quality control . A method for post hoc confirmation of sample identity is therefore highly desirable.
Genetic sample identification methods have an advantage over alternative sample management systems in that the genetic 'label’ is intrinsic to the biological sample itself, removing the possibility of manual labelling errors. Single nucleotide polymorphisms (SNPs) are increasingly utilised for DNA-based identification of human samples, with several benefits compared to standard forensic methods [5–7]. Existing SNP panels for human forensic identification and commercial SNP panels for sample identification, such as the iPLEX Sample ID Plus panel (Sequenom, San Diego, CA, USA), utilise pan-genome SNPs, the majority of which are non-exonic, and are therefore not useful for WES studies, as the majority of markers will not lie within the enriched regions of the genome. In addition to existing SNP panels, short tandem repeat markers can be used for genetic sample tracking. However, again, markers applied are frequently outside exomic regions and, if captured, will be prone to erroneous NGS genotyping using standard pipelines due to the repetitive nature of the markers .
Several methods for genetic tracking of human biological samples have been previously described, some of which are application specific - for example, for transcriptome microarray studies [3, 9, 10]. Although software for the validation of NGS (including WES) sample identity, such as verifyBamID, exist , for the detection of sample misidentifications external array-based genotypes of the samples are required, without which only contamination of the samples can be assessed.
Here we describe an optimised panel of SNPs for which WES data are typically informative, the genotypic profile of which can be utilised to extract intrinsic identifiers from human genomic DNA. These SNP profiles have high discriminatory power, even in large datasets. The profile derived from this panel can be compared to an independently genotyped profile for the same individual, allowing accurate validation of data and sample pairings, at a modest cost per sample.
Candidate identification and panel selection
Regions of overlap between three current commonly used whole-exome enrichment kits, (namely Agilent SureSelect Human All Exon V4, Illumina TruSeq Exome Enrichment and Nimblegen SeqCap EZ Human Exome Library V3.0 kits), and common SNPs (dbSNP 137, ) were established using BEDTools . SNPs were further filtered for inclusion based upon their presence in genes targeted by the Illumina TruSight Exome kit, which targets only genes of clinical interest.
Primary candidate selection criteria required SNPs to: 1) represent bi-allelic substitutions, excluding substitutions of complementary bases, that is, A↔T and G↔C transversions; 2) be technically amenable to both accurate WES and orthogonal genotyping, that is, not present in large-scale genomic repeats , or homopolymeric tracts of ≥5 bp, GC content for the flanking 250 bp was restricted to a range of between 40% and 55% and no other variant within 50 bp with an alternative-allele frequency (AF) ≥0.01 was permitted; 3) conform to desirable phase 3 HapMap AFs across several populations, explicitly AFs of between 0.2 and 0.8 in: CEPH (Utah residents with ancestry from northern and western Europe; CEU), Japanese in Tokyo, Japan (JPT), Han Chinese in Beijing, China (CHB) and Yoruba in Ibidan, Nigeria (YRI)  and; 4) not alter the primary sequence of the encoded protein or have an associated Online Mendelian Inheritance in Man (OMIM) record .
Following primary candidate identification steps, SNPs were further optimised by the following requirements: 1) be located at least 10 bp from exon boundaries; 2) not be situated in regions with a high sequence similarity to non-target regions, that is, no non-target BLAT score >100 , as this could result in non-specific genotyping; and 4) be outside of linkage disequilibrium with all other selected SNPs.
Finally, SNPs were prioritised for inclusion in the panel by proximity of the AFs to 0.5, across HapMap populations, in order to maximise discriminatory power.
SNP coverage in whole-exome sequencing data
A set of 91 in-house exome samples was evaluated for depth of sequence coverage for the candidate SNPs. Exome capture was performed using Agilent SureSelect Human All Exon V3 (n = 22) and V4 (n = 55), Illumina TruSeq Exome Enrichment (n = 9) and Nimblegen SeqCap EZ Human Exome Library V3.0 (n = 5). Exome enrichment, sequencing and in silico analysis of samples was performed as previously described [18, 19].
Optimised panel validation
The power of sample resolution for the panel was validated using data from phase 1 of the 1000 Genomes Project (n = 1,092)  and the UK10K project (n = 2,688; 2,432 of which are whole-genome data) . Genotypes were extracted from data using custom scripts and Tabix . Quantification of mismatches between samples was performed using MEGA5 .
Simulated datasets were generated by taking individual population AFs for each SNP as input and generating random SNP profiles in accordance with Hardy-Weinberg equilibrium based upon this; the randomisation of each SNP was independent of all other SNPs. We then quantified the rate of non-unique profiles per simulated dataset. We performed 20,000 independent replicates of dataset generation in all cases.
We applied the panel to a batch of 48 samples exome sequenced by an external service provider, for which orthogonal genotypes were obtained concurrently through an independent genotyping provider using KASP genotyping (LGC Genomics, Hoddeston, UK). Following plating of DNA samples for dispatch, a replicate plate was made directly from the primary plate, to be dispatched for the orthogonal genotyping. Genotypes derived from exome data and orthogonal genotyping assays were compared using PLINK  and custom scripts.
This study was approved by the Southampton and South West Hampshire Research Ethics Committee (09/H0504/125). Informed consent was obtained for all participants.
Optimised panel of identifying SNPs
HapMap 3 AF
The 24 biallelic SNPs afford 48 points of allelic comparison. Testing the optimised panel in the 1000 Genomes Project data (n = 1,092) , an average of 18.0 (standard deviation = 3.3) allelic differences between all pairwise combinations was observed, with a range of 3 to 34. As such, there will be, on average, 18 differential alleles between any two samples, enabling discrimination.
On addition of the UK10K data (n = 2,688) to the 1000 Genomes Project data (ncombined = 3,780), there remained an average of 17.8 allele mismatches across the profiles. Eighteen UK10K sample pairs produced duplicate profiles. On investigation of these pairs, they were found to share >98% genotypic concordance across an extended panel of 1,662 SNPs in all cases, compared to an average of 42%, with a range of 27 to 77%, for all sample pairs with unique SNP profiles (Additional file 2). As such, these pairs represent extreme outliers, and are derived from genetically identical biological samples, either from the same individual or monozygotic twins, and were therefore excluded from the mismatch average.
Profile collisions per simulated dataset of 10,000 individuals with various population AFs
Average collisions per dataset (±SD)
1000 Genomes average
HapMap phase 3:
Theoretical perfect a
Validation of sample identity is essential in order to ensure data integrity and validity of conclusions drawn from data. We have described a powerful tool for the identification and validation of data provenance throughout the workflow of WES data collection and analysis. The power of discrimination, that is, the precision with which samples can be uniquely identifiable, is sufficient and robust for most projects on the current scale of up to 10,000 samples, with inbuilt redundancy of SNPs to protect against technical failures. In WES, the exome enrichment process provides the limiting step for the availability of data on SNPs for use in sample identification. As such, this panel will also be of utility for whole-genome sequencing data, where there is no such limitation on SNP coverage. This will be beneficial where there are mixed datasets of both whole-genome sequence and WES data.
NGS is now developing as the diagnostic methodology of choice across a range of applications, including mutation scanning in targeted gene panels and WES for congenital disorders, as well as high depth analysis for tumour profiling. Whilst the service model for delivery of these tests is not fully resolved at this stage, there will certainly be economic arguments for centralising certain tests. This will have the effect of increasing the throughput requirements as well as physically moving samples between labs. Both of these factors will increase the opportunity for sample misidentification.
Even for testing within a single lab, the use of inherent sample and data identification methods, as described in this study, seems a robust approach to fulfil the regulatory requirement for providing a full audit trail and ensuring data provenance [26, 27]. The SNP panel presented here is immediately usable across all commonly used exome capture kits, and would be equally applicable to any gene panel by incorporating, or 'spiking’, the SNP regions into the custom capture kit at the design stage. Where it can be shown that there are no expected repeat profiles (that is, no paired samples from the same individual are being analysed), it may even be beneficial from a process perspective to use the SNP profile as the primary method for sample tracking.
The discriminatory power of the panel may be reduced for various reasons, such as geographically localised variation in AFs, and degradation of DNA samples, resulting in incomplete data. We have shown our panel to have a high discriminatory power across a diverse range of populations. Additionally, the discriminatory power will be marginally reduced where many relatives are sequenced. In the case of highly consanguineous families, sample tracking methods such as barcoding will afford optimal certainty in these particular cases. Should concerns over insufficient discriminatory power arise, additional SNPs may be added to the panel from the existing list of candidates (Additional file 1), also allowing the tailoring of an enhanced panel to the population(s) of interest, should this be desired. Nevertheless, we have demonstrated our panel to be sufficiently robust to withstand power reductions without loss of utility for most purposes.
We have also presented a recent case in which use of this panel has allowed us to identify, confirm, and resolve a sample switch, highlighting the importance of using such a tool. Monetary cost will vary with the technology used for orthogonal genotyping and sample throughput. We have intentionally designed the panel to be platform non-specific, allowing for the establishment of in-house assays using preferred genotyping methodology or outsourced where required. Our own chosen methodology costs approximately £5 GBP per sample, representing a small fraction of the cost of exome data generation.
The size of held NGS datasets continues to increase, with the UK Government recently committing to the sequencing of 100,000 samples as part of healthcare provisions . As such, the demand for the development of effective tools for bioinformatic analysis, data compression, mutation effect prediction and quality control is high. We have described a panel of SNPs for the discrimination of human biological samples on the basis of data intrinsic to WES data derived from samples processed using common capture kits. We recommend the routine use of this panel to maintain data integrity and protect sample provenance.
CEPH (Utah residents with ancestry from northern and western Europe)
Han Chinese in Beijing, China
Southern Han Chinese
Japanese in Tokyo, Japan
single nucleotide polymorphism
Yoruba in Ibidan, Nigeria.
The authors thank the Technology Strategy Board and the University of Southampton for funding, and Dr Dietrich Lueerssen for discussion. The authors also acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. This study makes use of data generated by the UK10K Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.UK10K.org. Funding for UK10K was provided by the Wellcome Trust under award WT091310.
- Need AC, Shashi V, Hitomi Y, Schoch K, Shianna KV, McDonald MT, Meisler MH, Goldstein DB: Clinical application of exome sequencing in undiagnosed genetic conditions. J Med Genet. 2012, 49: 353-361, 10.1136/jmedgenet-2012-100819View ArticlePubMedPubMed CentralGoogle Scholar
- Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, Shendure J: Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011, 12: 745-755, 10.1038/nrg3031View ArticlePubMedGoogle Scholar
- Westra H-J, Jansen RC, Fehrmann RSN, te Meerman GJ, van Heel D, Wijmenga C, Franke L: MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics. 2011, 27: 2104-2111, 10.1093/bioinformatics/btr323View ArticlePubMedGoogle Scholar
- Lam CW, Jacob E: Implementing a laboratory automation system: experience of a large clinical laboratory. J Lab Autom. 2012, 17: 16-23.View ArticlePubMedGoogle Scholar
- Pakstis AJ, Speed WC, Fang R, Hyland FC, Furtado MR, Kidd JR, Kidd KK: SNPs for a universal individual identification panel. Hum Genet. 2010, 127: 315-324, 10.1007/s00439-009-0771-1View ArticlePubMedGoogle Scholar
- Zietkiewicz E, Witt M, Daca P, Zebracka-Gala J, Goniewicz M, Jarzab B, Witt M: Current genetic methodologies in the identification of disaster victims and in forensic analysis. J Appl Genet. 2012, 53: 41-60, 10.1007/s13353-011-0068-7View ArticlePubMedPubMed CentralGoogle Scholar
- Freire-Aradas A, Fondevila M, Kriegel AK, Phillips C, Gill P, Prieto L, Schneider PM, Carracedo A, Lareu MV: A new SNP assay for identification of highly degraded human DNA. Forensic Sci Int Genet. 2012, 6: 341-349, 10.1016/j.fsigen.2011.07.010View ArticlePubMedGoogle Scholar
- Highnam G, Franck C, Martin A, Stephens C, Puthige A, Mittelman D: Accurate human microsatellite genotypes from high-throughput resequencing data using informed error profiles. Nucleic Acids Res. 2013, 41: e32, 10.1093/nar/gks981View ArticlePubMedPubMed CentralGoogle Scholar
- Castro F, Dirks WG, Fähnrich S, Hotz-Wagenblatt A, Pawlita M, Schmitt M: High-throughput SNP-based authentication of human cell lines. Int J Cancer. 2013, 132: 308-314, 10.1002/ijc.27675View ArticlePubMedPubMed CentralGoogle Scholar
- Xu W, Gao H, Seok J, Wilhelmy J, Mindrinos MN, Davis RW, Xiao W: Coding SNPs as intrinsic markers for sample tracking in large-scale transcriptome studies. Biotechniques. 2012, 52: 386-388.View ArticlePubMedPubMed CentralGoogle Scholar
- Jun G, Flickinger M, Hetrick KN, Romm JM, Doheny KF, Abecasis GR, Boehnke M, Kang HM: Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data. Am J Hum Genet. 2012, 91: 839-848, 10.1016/j.ajhg.2012.09.004View ArticlePubMedPubMed CentralGoogle Scholar
- Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001, 29: 308-311, 10.1093/nar/29.1.308View ArticlePubMedPubMed CentralGoogle Scholar
- Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010, 26: 841-842, 10.1093/bioinformatics/btq033View ArticlePubMedPubMed CentralGoogle Scholar
- Repeat Masker.http://www.repeatmasker.org/,
- International HapMap Consortium: Integrating common and rare genetic variation in diverse human populations. Nature. 2010, 467: 52-58, 10.1038/nature09298View ArticleGoogle Scholar
- OMIM - Online Mendelian Inheritance in Man.http://www.omim.org/,
- Kent WJ: BLAT–The BLAST-Like Alignment Tool. Genome Res. 2002, 12: 656-664.View ArticlePubMedPubMed CentralGoogle Scholar
- Christodoulou K, Wiskin AE, Gibson J, Tapper W, Willis C, Afzal NA, Upstill-Goddard R, Holloway JW, Simpson MA, Beattie RM, Collins A, Ennis S: Next generation exome sequencing of paediatric inflammatory bowel disease patients identifies rare and novel variants in candidate genes. Gut. 2013, 62: 977-984, 10.1136/gutjnl-2011-301833View ArticlePubMedPubMed CentralGoogle Scholar
- Gibson J, Tapper W, Ennis S, Collins A: Exome-based linkage disequilibrium maps of individual genes: functional clustering and relationship to disease. Hum Genet. 2013, 132: 233-243, 10.1007/s00439-012-1243-6View ArticlePubMedGoogle Scholar
- 1000 Genomes Project Consortium: An integrated map of genetic variation from 1, 092 human genomes. Nature. 2012, 491: 56-65, 10.1038/nature11632View ArticleGoogle Scholar
- UK10K Study Samples.http://www.uk10k.org/studies/,
- Li H: Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics. 2011, 27: 718-719, 10.1093/bioinformatics/btq671View ArticlePubMedPubMed CentralGoogle Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011, 28: 2731-2739, 10.1093/molbev/msr121View ArticlePubMedPubMed CentralGoogle Scholar
- Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007, 81: 559-575, 10.1086/519795View ArticlePubMedPubMed CentralGoogle Scholar
- 1000 Genomes Project Consortium: A map of human genome variation from population-scale sequencing. Nature. 2010, 467: 1061-1073, 10.1038/nature09534View ArticleGoogle Scholar
- International Organization for Standardization: Medical Laboratories - Requirements for Quality and Competence. 2012, ISO 15189:2012Google Scholar
- Rehm HL, Bale SJ, Bayrak-Toydemir P, Berg JS, Brown KK, Deignan JL, Friez MJ, Funke BH, Hegde MR, Lyon E: ACMG clinical laboratory standards for next-generation sequencing. Genet Med. 2013, 15: 733-747, 10.1038/gim.2013.92View ArticlePubMedPubMed CentralGoogle Scholar
- DNA tests to revolutionise fight against cancer and help 100, 000 NHS patients.https://www.gov.uk/government/news/dna-tests-to-revolutionise-fight-against-cancer-and-help-100000-nhs-patients,
This article is published under license to 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.