Hi-MC: A novel method for high-throughput mitochondrial haplogroup classification
A peer-reviewed article of this Preprint also exists.
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Abstract
Effective approaches for assessing mitochondrial DNA (mtDNA) variation are important to multiple scientific disciplines. Mitochondrial haplogroups characterize branch points in the phylogeny of mtDNA. Several tools exist for mitochondrial haplogroup classification. However, most require full or partial mtDNA sequence which is often cost prohibitive for studies with large sample sizes. The purpose of this study was to develop Hi-MC, a high-throughput method for mitochondrial haplogroup classification that is cost effective and applicable to large sample sizes making mitochondrial analysis more accessible in genetic studies. Using rigorous selection criteria, we defined and validated a custom panel of mtDNA single nucleotide polymorphisms (SNPs) that allows for accurate classification of European, African, and Native American mitochondrial haplogroups at broad resolution with minimal genotyping and cost. We demonstrate that Hi-MC performs well in samples of European, African, and Native American ancestries, and that Hi-MC performs comparably to a commonly used classifier. Implementation as a software package in R enables users to download and run the program locally, grants greater flexibility in the number of samples that can be run, and allows for easy expansion in future revisions. The source code is freely available at https://github.com/vserch/himc .
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2017. Hi-MC: A novel method for high-throughput mitochondrial haplogroup classification. PeerJ Preprints 5:e3420v1 https://doi.org/10.7287/peerj.preprints.3420v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ for review.
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Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Sabrina L Mitchell conceived and designed the experiments, performed the experiments, analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Eric H Farber-Eger performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, reviewed drafts of the paper.
Olivia J Veatch performed the experiments, analyzed the data, reviewed drafts of the paper.
Robert J Goodloe performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, reviewed drafts of the paper.
Quinn S Wells contributed reagents/materials/analysis tools, reviewed drafts of the paper.
Deborah G Murdock conceived and designed the experiments, contributed reagents/materials/analysis tools, reviewed drafts of the paper.
Dana C Crawford conceived and designed the experiments, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
GitHub: https://github.com/vserch/himc
Funding
This work was supported in part by National Institutes of Health grant [U01 HG004798] and associated American Recovery and Reinvestment Act (ARRA) supplements. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
