A phylogenetic approach for haplotype analysis of sequence data from complex mitochondrial mixtures. (September 2017)

• Record Type: Journal Article
• Authors: Vohr, Samuel H.; Gordon, Rachel; Eizenga, Jordan M.; Erlich, Henry A.; Calloway, Cassandra D.; Green, Richard E.
• Is Part Of: Forensic science international. Volume 30(2017)
• Page Start: 93
• Page End: 105
• Publication Date: 2017-09
• Subjects: Forensics -- Mixtures -- Mitochondrial DNA -- Massively parallel sequencing -- Next-generation sequencing -- Haplogroups -- Deconvolution -- Mixemt; Forensic genetics -- Periodicals; Génétique légale -- Périodiques; Forensic genetics; Electronic journals; Periodicals; 614.1
• DOI: 10.1016/j.fsigen.2017.05.007
• Languages: English
• ISSNs: 1872-4973

Abstract

Highlights: A phylogenetic approach to isolate haplotypes in a mixture of mtDNA sequences. Sensitive detection of minor haplotypes with contributions ≥5%. Reconstruction of haplotypes by assigning sequences to most-likely contributor. mtDNA haplogroup assignment in mixtures of one, two, or more contributors. 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.