Format

Send to

Choose Destination
PeerJ. 2018 Jun 25;6:e5149. doi: 10.7717/peerj.5149. eCollection 2018.

Hi-MC: a novel method for high-throughput mitochondrial haplogroup classification.

Author information

1
Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
2
Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
3
Vanderbilt Eye Institute and Department of Ophthalmology & Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
4
Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA.
5
Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA.
6
Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA.
7
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
8
Department of Pharmacology, Vanderbilt University, Nashville, TN, USA.
9
Center for Mitochondrial and Epigenomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

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 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. Hi-MC is available in the CRAN repository and the source code is freely available at https://github.com/vserch/himc.

KEYWORDS:

Classifier; Genotype; Haplogroup; Mitochondria; mtDNA variation

Conflict of interest statement

The authors declare that they have no competing interests.

Supplemental Content

Full text links

Icon for PeerJ, Inc. Icon for PubMed Central
Loading ...
Support Center