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Phylogenetic Network of the mtDNA Haplogroup U in Northern Finland Based on Sequence Analysis of the Complete Coding Region by Conformation-Sensitive Gel Electrophoresis

Authors
Journal
The American Journal of Human Genetics
0002-9297
Publisher
Elsevier
Publication Date
Volume
66
Issue
3
Identifiers
DOI: 10.1086/302802
Keywords
  • Original Articles
Disciplines
  • Biology
  • Medicine

Abstract

Mutations in mtDNA have accumulated sequentially, and maternal lineages have diverged to form population-specific genotypes. Classification of the genotypes has been made based on differences found in restriction fragment analysis of the coding region or in the sequence of the hypervariable segment I. Both methods have shortcomings, as the former may not detect all the important polymorphisms and the latter makes use of a segment containing hypervariable nucleotide positions. Here, we have used conformation-sensitive gel electrophoresis (CSGE) to detect polymorphisms within the coding region of mtDNA from 22 Finns belonging to haplogroup U. Sixty-three overlapping PCR fragments covering the entire coding region were analyzed by CSGE, and the fragments that differed in their migration pattern were sequenced. CSGE proved to be a sensitive and specific method for identifying mtDNA substitutions. The phylogenetic network of the 22 coding-region sequences constituted a perfect tree, free of homoplasy, and provided several previously unidentified common polymorphisms characterizing subgroups of U. After contrasting this data with that of hypervariable segment I, we concluded that position 16192 seems to be prone to recurrent mutations and that position 16270 has experienced a back mutation. Interestingly, all 22 samples were found to belong to subcluster U5, suggesting that this subcluster is more frequent in Finns than in other European populations. Complete sequence data of the mtDNA yield a more reliable phylogenetic network and a more accurate classification of the haplogroups than previous ones. In medical genetics, such networks may help to decide between a rare polymorphism and a pathogenic mutation; in population genetics, the networks may enable more detailed analyses of population history and mtDNA evolution.

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