A Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures

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dc.creatorSmart, Utpal
dc.creatorCihlar, Jennifer Churchill
dc.creatorMandape, Sammed N.
dc.creatorMuenzler, Melissa
dc.creatorKing, Jonathan L.
dc.creatorBudowle, Bruce
dc.creatorWoerner, August E.
dc.creator.orcid0000-0001-9796-193X (Cihlar, Jennifer Churchill)
dc.creator.orcid0000-0002-9372-1127 (Woerner, August E.)
dc.date.accessioned2022-11-28T20:48:01Z
dc.date.available2022-11-28T20:48:01Z
dc.date.issued2021-01-20
dc.description.abstractDespite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture's genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic.
dc.description.sponsorshipThis work was supported by Award Number 2017-DN-BX-0134 by the National Institute of Justice Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the U.S. Department of Justice.
dc.identifier.citationSmart, U., Cihlar, J. C., Mandape, S. N., Muenzler, M., King, J. L., Budowle, B., & Woerner, A. E. (2021). A Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures. Genes, 12(2), 128. https://doi.org/10.3390/genes12020128
dc.identifier.issn2073-4425
dc.identifier.issue2
dc.identifier.urihttps://hdl.handle.net/20.500.12503/31980
dc.identifier.volume12
dc.publisherMDPI
dc.relation.urihttps://doi.org/10.3390/genes12020128
dc.rights.holder© 2021 by the authors.
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceGenes (Basel)
dc.subjectBayesian inference
dc.subjectDEploid
dc.subjectIon Torrent
dc.subjectr
dc.subjectbioinformatics
dc.subjectcomputational phasing
dc.subjectforensic genetics
dc.subjectmassively parallel sequencing
dc.subjectmtDNA mixture deconvolution
dc.subjectpopulation genomics
dc.subject.meshAlgorithms
dc.subject.meshBayes Theorem
dc.subject.meshComputational Biology / methods
dc.subject.meshDNA, Mitochondrial
dc.subject.meshForensic Genetics / methods
dc.subject.meshGenome, Mitochondrial
dc.subject.meshGenomics / methods
dc.subject.meshHigh-Throughput Nucleotide Sequencing / methods
dc.subject.meshHumans
dc.subject.meshModels, Statistical
dc.subject.meshPolymorphism, Single Nucleotide
dc.subject.meshReproducibility of Results
dc.subject.meshSequence Analysis, DNA / methods
dc.titleA Continuous Statistical Phasing Framework for the Analysis of Forensic Mitochondrial DNA Mixtures
dc.typeArticle
dc.type.materialtext

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