A phylogenetic approach for haplotype analysis of sequence data from complex mitochondrial mixtures

Abstract

Massively parallel (next-generation) sequencing provides a powerful method to analyze DNA from many different sources, including degraded and trace samples. A common challenge, however, is that many forensic samples are often known or suspected mixtures of DNA from multiple individuals. Haploid lineage markers, such as mitochondrial (mt) DNA, are useful for analysis of mixtures because, unlike nuclear genetic markers, each individual contributes a single sequence to the mixture. Deconvolution of these mixtures into the constituent mitochondrial haplotypes is challenging as typical sequence read lengths are too short to reconstruct the distinct haplotypes completely. We present a powerful computational approach for determining the constituent haplotypes in massively parallel sequencing data from potentially mixed samples. At the heart of our approach is an expectation maximization based algorithm that co-estimates the overall mixture proportions and the source haplogroup for each read individually. This approach, implemented in the software package mixemt, correctly identifies haplogroups from mixed samples across a range of mixture proportions. Furthermore, our method can separate fragments in a mixed sample by the most likely originating contributor and generate reconstructions of the constituent haplotypes based on known patterns of mtDNA diversity.

Validation of NGS for mitochondrial DNA casework at the FBI Laboratory

“…This is particularly true when variants are present within a single amplicon or sequence read. Previous studies have demonstrated the utility of NGS for these purposes [44][45][46][47], and though further in depth studies that fully characterize the limitations of the PowerSeq assay for this application are necessary, the results presented here offer additional support.…”

Applications of Probe Capture Enrichment Next Generation Sequencing for Whole Mitochondrial Genome and 426 Nuclear SNPs for Forensically Challenging Samples

“…In addition to GeneMarker ® HTS, the sequenced reads for the mtDNA mixture samples were analyzed using Mixemt developed by Vohr et al [ 54 ]. Prior to analysis using Mixemt, the raw sequence reads were trimmed for adapters, and the overlapping reads were merged using SeqPrep [ 49 ].…”

Contaminations in (meta)genome data: An open issue for the scientific community

“…54 However, these methods still have limitations because they are not able to identify the presence of contaminant DNA in the aDNA samples, which represents an actual hurdle in the analysis of ancient genomes. 14,15 To detect the contaminant DNA in the mixed aDNA samples, the mitochondrial DNA (mtDNA) can be used, as it is inherited only from the mother and it does not undergo recombination during meiosis (thus, each individual will be associated to a single haplotype) 55 and as each cell contains hundreds or thousands of it in identical copies. 55,56 In addition, detailed mtDNA databases, such as EMPOP 57 and MITOMAP, 58 can be used as reference databases for the identification of contaminant "modern" DNA in aDNA genomes.…”

“…14,15 To detect the contaminant DNA in the mixed aDNA samples, the mitochondrial DNA (mtDNA) can be used, as it is inherited only from the mother and it does not undergo recombination during meiosis (thus, each individual will be associated to a single haplotype) 55 and as each cell contains hundreds or thousands of it in identical copies. 55,56 In addition, detailed mtDNA databases, such as EMPOP 57 and MITOMAP, 58 can be used as reference databases for the identification of contaminant "modern" DNA in aDNA genomes. 55,[59][60][61] An iterative approach that can be used to identify the contaminant DNA in aDNA genomes is represented by the program Schmutzi.…”

“…55,56 In addition, detailed mtDNA databases, such as EMPOP 57 and MITOMAP, 58 can be used as reference databases for the identification of contaminant "modern" DNA in aDNA genomes. 55,[59][60][61] An iterative approach that can be used to identify the contaminant DNA in aDNA genomes is represented by the program Schmutzi. 15 This tool assembles the endogenous mitochondrial genome and estimates present-day human mitochondrial contamination in archaic and early modern human aDNA data sets.…”