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Forensic Sci Int Genet. 2017 Sep;30:93-105. doi: 10.1016/j.fsigen.2017.05.007. Epub 2017 May 29.

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

Author information

1
Department of Biomolecular Engineering, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA. Electronic address: svohr@ucsc.edu.
2
Center for Genetics, Children's Hospital Oakland Research Institute, 5700 Martin Luther King Jr Way, Oakland, CA 94609, USA.
3
Department of Biomolecular Engineering, University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064, USA.
4
Center for Genetics, Children's Hospital Oakland Research Institute, 5700 Martin Luther King Jr Way, Oakland, CA 94609, USA; Forensic Science Graduate Program, University of California, Davis, 1 Shields Ave, Davis, CA 95616, USA.

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.

KEYWORDS:

Deconvolution; Forensics; Haplogroups; Massively parallel sequencing; Mitochondrial DNA; Mixemt; Mixtures; Next-generation sequencing

PMID:
28667863
DOI:
10.1016/j.fsigen.2017.05.007
[Indexed for MEDLINE]
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