Advertisement

Modelling allelic drop-outs in STR sequencing data generated by MPS

  • Søren B. Vilsen
    Correspondence
    Corresponding author. Permanent address: Skjernvej 4A, 9220 Aalborg East, Denmark.
    Affiliations
    Department of Mathematical Sciences, Aalborg University, Denmark
    Search for articles by this author
  • Author Footnotes
    1 Permanent address: Skjernvej 4A, 9220 Aalborg East, Denmark.
    Torben Tvedebrink
    Footnotes
    1 Permanent address: Skjernvej 4A, 9220 Aalborg East, Denmark.
    Affiliations
    Department of Mathematical Sciences, Aalborg University, Denmark
    Search for articles by this author
  • Author Footnotes
    1 Permanent address: Skjernvej 4A, 9220 Aalborg East, Denmark.
    Poul S. Eriksen
    Footnotes
    1 Permanent address: Skjernvej 4A, 9220 Aalborg East, Denmark.
    Affiliations
    Department of Mathematical Sciences, Aalborg University, Denmark
    Search for articles by this author
  • Author Footnotes
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.
    Christian Hussing
    Footnotes
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.
    Affiliations
    Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
    Search for articles by this author
  • Author Footnotes
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.
    Claus Børsting
    Footnotes
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.
    Affiliations
    Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
    Search for articles by this author
  • Author Footnotes
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.
    Niels Morling
    Footnotes
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.
    Affiliations
    Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
    Search for articles by this author
  • Author Footnotes
    1 Permanent address: Skjernvej 4A, 9220 Aalborg East, Denmark.
    2 Permanent address: Frederik V's Vej 11, 2100 Copenhagen, Denmark.

      Highlights

      • We analysed allelic drop-outs in STR massively parallel sequencing (MPS) data.
      • We extended the capillary electrophoresis (CE) peak height models to model the coverage of MPS data.
      • The coverage model accounts for the large marker imbalances seen with MPS data.
      • We also took into account the increased complexity of the stutter behaviour due to the enhanced resolution of the allelic sequences.
      • We investigated the performance of the coverage model by comparing observed and expected Brier scores.

      Abstract

      We used a Poisson-gamma model to analyse the allele coverage of autosomal short tandem repeat (STR) systems obtained by massively parallel sequencing (MPS). The Poisson-gamma coverage model was created using the peak height models from capillary electrophoresis (CE) based detection of PCR products as a starting point. The CE models were modified to account for the differences between CE and MPS signals by accounting for the large marker imbalances seen for MPS data and by using the Poisson-gamma distribution instead of the normal, log-normal, or gamma distributions that were applied for CE data. We took two approaches to estimate the marker imbalance parameters by (1) using a work-flow data base, and (2) using the results of replicate investigations of the samples.
      The Poisson-gamma model was used to estimate the rate of drop-outs of (1) single contributor dilution series experiments and (2) the minor contributor in two-person mixture samples. We examined the predictive capabilities of the model by comparing the observed and expected Brier scores of each sample. We derived the expected Brier scores and their variances to create asymptotic confidence intervals of the Brier scores. We found that the Poisson-gamma model performed well when using the work-flow data base, but that the replicate approach is not necessarily a viable option.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Forensic Science International: Genetics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Gill P.
        • Whitaker J.
        • Flaxman C.
        • Brown N.
        • Buckleton J.
        An investigation of the rigor of interpretation rules for STRs derived from less than 100 pg of DNA.
        Forensic Sci. Int. 2000; 112: 17-40
        • Gill P.
        • Curran J.
        • Elliot K.
        A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci.
        Nucleic Acids Res. 2005; 33: 632-643
        • Dixon L.A.
        • Dobbins A.E.
        • Pulker H.K.
        • Butler J.M.
        • Vallone P.M.
        • Coble M.D.
        • Parson W.
        • Berger B.
        • Grubweiser P.
        • Mogensen H.S.
        • Morling N.
        • Nielsen K.
        • Sanchez J.J.
        • Petkovski E.
        • Carrecedo A.
        • Sanchez-Diz P.
        • Ramos-Luis E.
        • Brion M.
        • Irwin J.A.
        • Just R.S.
        • Loreille O.
        • Parsons T.J.
        • Syndercombe-Court D.
        • Schmitter H.
        • Stradmann-Bellinghausen B.
        • Bender K.
        • Gill P.
        Analysis of artificially degraded DNA using STRs and SNPs – results of a collaborative European (EDNAP) exercise.
        Forensic Sci. Int. 2006; 164: 33-44
        • Smith P.J.
        • Ballantyne J.
        Simplified low-copy-number DNA analysis by post-PCR purification.
        J. Forensic Sci. 2007; 52: 820-829
        • Forster L.
        • Thomson J.
        • Kutranov S.
        Direct comparison of post-28-cycle PCR purification and modified capillary electrophoresis methods with the 34-cycle “low copy number” (LCN) method for analysis of trace forensic DNA samples.
        Forensic Sci. Int. Genet. 2008; 2: 318-328
        • Western A.A.
        • Nagel J.H.A.
        • Benschop C.C.G.
        • Weiler N.E.C.
        • de Jong B.J.
        • Sijen T.
        Higher capillary electrophoresis injection settings as an efficient approach to increase the sensitivity of STR typing.
        J. Forensic Sci. 2009; 54: 591-598
        • Gill P.
        • Buckleton J.
        A universal strategy to interpret DNA profiles that does not require a definition of low-copy-number.
        Forensic Sci. Int. Genet. 2010; 4: 221-227
        • Petricevic S.
        • Whitaker J.
        • Buckleton J.
        • Vintiner S.
        • Patel J.
        • Simon P.
        • Ferraby H.
        • Hermiz W.
        • Russell A.
        Validation and development of interpretation guidelines for low copy number (LCN) DNA profiling in New Zealand using the AmpFSTR1 SGM plus™ multiplex.
        Forensic Sci. Int. Genet. 2009; 4: 305-310
        • Benschop C.C.G.
        • van der Beek C.P.
        • Meiland H.C.
        • van Gorp A.G.M.
        • Western A.A.
        • Sijen T.
        Low template STR typing: effect of replicate number and consensus method on genotyping reliability and DNA database search results.
        Forensic Sci. Int. Genet. 2011; 5: 316-328
        • Cowen S.
        • Debenham P.
        • Dixon A.
        • Kutranov S.
        • Thomson J.
        • Way K.
        An investigation of the robustness of the consensus method of interpreting low-template DNA profiles.
        Forensic Sci. Int. Genet. 2011; 5: 400-406
        • Western A.A.
        • Grol L.J.W.
        • Harteveld J.
        • Matai A.S.
        • De Knijff P.
        • Sijen T.
        Assessment of the stochastic threshold, back-, and forward stutter filters and low template techniques for NGM.
        Forensic Sci. Int. Genet. 2012; 6: 708-715
        • Pand C.M.
        • Klein-Unseld R.
        • Klintschar M.
        • Wiegand P.
        Comparison of different interpretation strategies for low template DNA mixtures.
        Forensic Sci. Int. Genet. 2012; 6: 716-727
        • Børsting C.
        • Mogensen H.S.
        • Morling N.
        Forensic genetic SNP typing of low-template DNA and highly degraded DNA from crime case samples.
        Forensic Sci. Int. Genet. 2013; 7: 345-352
        • Tvedebrink T.
        • Eriksen P.S.
        • Mogensen H.S.
        • Morling N.
        Estimating the probability of allelic drop-out of STR alleles in forensic genetics.
        Forensic Sci. Int. Genet. 2009; 3: 222-226
        • Tvedebrink T.
        • Eriksen P.S.
        • Asplund M.
        • Mogensen H.S.
        • Morling N.
        Allelic drop-out probabilities estimated by logistic regression – further considerations and practical implementation.
        Forensic Sci. Int. Genet. 2012; 6: 263-267
        • Tvedebrink T.
        • Asplund M.
        • Eriksen P.S.
        • Mogensen H.S.
        • Morling N.
        Estimating drop-out probabilities of STR alleles accounting for stutters, detection threshold truncation and degradation.
        Forensic Sci. Int. Genet. Suppl. Ser. 2013; 4: e51-e52
        • Taylor D.
        • Bright J.-A.
        • Buckleton J.S.
        The interpretation of single source and mixed DNA profile.
        Forensic Sci. Int. Genet. 2013; 7: 516-528
        • Cowell R.G.
        • Graversen T.
        • Lauritzen S.L.
        • Mortera J.
        Analysis of forensic DNA mixtures with artefacts.
        J. R. Stat. Soc. Ser. C: Appl. Stat. 2015; 64: 1-32
        • Bleka O.
        • Storvik G.
        • Gill P.
        EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts.
        Forensic Sci. Int. Genet. 2016; 21: 35-44
        • Steele C.D.
        • Greenhalgh M.
        • Balding D.J.
        Evaluation of low-template DNA profiles using peak heights.
        Stat. Appl. Genet. Mol. Biol. 2016; 15: 431-445
        • Vilsen S.B.
        • Tvedebrink T.
        • Mogensen H.S.
        • Morling N.
        Statistical modelling of Ion PGM HID STR 10-plex MPS data.
        Forensic Sci. Int. Genet. 2017; 28: 82-89
        • Cowell R.G.
        • Lauritzen S.
        • Mortera J.
        Probabilistic expert systems for handling artifacts in complex DNA mixtures.
        Forensic Sci. Int. Genet. 2011; 5: 202-209
        • Graversen T.
        • Lauritzen S.
        Computational aspects of DNA mixture analysis.
        Stat. Comput. 2015; 25: 527-541
        • Vilsen S.B.
        • Tvedebrink T.
        • Eriksen P.S.
        • Bøsting C.
        • Hussing C.
        • Mogensen H.S.
        • Morling N.
        Stutter analysis of complex STR MPS data.
        Forensic Sci. Int. Genet. 2018; 35: 107-112
        • Hussing C.
        • Huber C.
        • Bytyci R.
        • Mogensen H.S.
        • Morling N.
        • Børsting C.
        Sequencing of 231 forensic genetic markers using the Illumina® ForeSeq™ workflow – an evaluation of the assay and software.
        Forensic Sci. Res. 2018; (in press)
        • Hussing C.
        • Huber C.
        • Bytyci R.
        • Morling N.
        • Børsting C.
        The Danish STR sequence database: duplicate typing of 363 Danes with the ForenSeq™ DNA Signature Prep Kit.
        Int. J. Leg. Med. 2018; (in press)
        • Woerner A.E.
        • King J.L.
        • Budowle B.
        Fast STR allele identification with STRait Razor 3.0.
        Forensic Sci. Int. Genet. 2017; 30: 18-23
        • Brier G.W.
        Verification of forecasts expressed in terms of proability.
        Mon. Weather Rev. 1950; 78: 1-3
        • Benedetti R.
        Scoring Rules for Forecast Verification.
        Mon. Weather Rev. 2010; 138: 203-211