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

Abstract

Despite 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.

The Value of Whole-Genome Sequencing for Mitochondrial DNA Population Studies: Strategies and Criteria for Extracting High-Quality Mitogenome Haplotypes

“…Unfortunately, confirmation testing using an enrichment method such as long-range amplification to minimize NUMT interference was not possible for the SweGen dataset. In these cases, the mtDNA haplogroup of the major and minor haplotypes may provide some guidance to the source of the mixture (Table S1) [35,36], and potentially assist in the identification of NUMTs [37]. However, phylogenetic analysis does not eliminate the possibility that the mixture results from a mega-NUMT from a "modern" mtDNA haplogroup, as was detected in Lutz-Bonengel et al [6].…”

“…Classification of PHPs or NUMTs is further complicated, as np 16093 is a known heteroplasmic hotspot [39] with variations observed across sample types [44]. Cihlar et al [26] have previously discussed the difficulty of distinguishing authentic heteroplasmy at np 16093 from NUMT variants in small amplicon NGS data, and suggested the use of phylogenetic information as a possible solution [37]. Another, more conservative option for a hotspot like np 16093 could be to ignore the position in all data interpretations due to its variability and susceptibility to NUMT interference.…”

The time is now for ubiquitous forensicmtMPSanalysis

“…For example, a population study to assess of the efficacy of an mtMPS approach in forensic laboratories (Avila et al, 2019), concluded that mtMPS analysis should be the method of choice. Other studies have illustrated that mtMPS is a powerful means of deconvoluting two or three‐person mixtures (Smart et al, 2021; Churchill et al, 2018; Vohr et al, 2017; Kim et al, 2015).…”

“…MPS can detect and resolve mixed mtDNA sequences of low‐level minor variants providing a powerful means of deconvoluting two and three‐person mixtures (Smart et al, 2021; Nakanishi et al, 2020; Churchill et al, 2018; Vohr et al, 2017; Kim et al, 2015), whereas STS cannot achieve this feat. Specifically, MPS can detect and resolve mixtures, including heteroplasmy, at threshold levels as low as 1–2%, whereas STS can only detect mixed sites when the minor variant reaches 5–10%, and in some cases not until 20% depending on the noise encountered in the data (González et al, 2020; Holland et al, 2011, 2018, 2019; Irwin et al, 2009; Just, Irwin, et al, 2015; Kloss‐Brandstätter et al, 2015; McElhoe et al, 2014; Melton, 2004; Parson et al, 2015).…”

Post hoc deconvolution of human mitochondrial DNA mixtures by EMMA 2 using fine-tuned Phylotree nomenclature

“…In this study we developed and evaluated a strategy to deconvolute mtDNA mixtures using phylogenetic principles. Earlier attempts to separate human mtDNA mixtures applied physical methods, such as denaturing high-performance liquid chromatography [6] , [18] , base composition profiling using mass spectrometry [12] , quantitative data in a statistical framework [9] and MPS-based data [5] , [13] , [21] with a continuous statistical phasing framework [28] . While some of these strategies have proven useful, there is still a lack of technology-agnostic, non-quantitative and easy to use mtDNA mixture deconvolution tools that provide traceable splittings on any kind of sequencing data (Sanger and MPS).…”