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

Autor:	Smieszek S; Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.; Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA., Mitchell SL; Vanderbilt Eye Institute and Department of Ophthalmology & Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Farber-Eger EH; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA., Veatch OJ; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA., Wheeler NR; Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.; Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA., Goodloe RJ; Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA., Wells QS; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Pharmacology, Vanderbilt University, Nashville, TN, USA., Murdock DG; Center for Mitochondrial and Epigenomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA., Crawford DC; Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.; Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
Jazyk:	angličtina
Zdroj:	PeerJ [PeerJ] 2018 Jun 25; Vol. 6, pp. e5149. Date of Electronic Publication: 2018 Jun 25 (Print Publication: 2018).
DOI:	10.7717/peerj.5149
Abstrakt:	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. Competing Interests: The authors declare that they have no competing interests.
Databáze:	MEDLINE
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