mtDNA “Nomenclutter” and its Consequences on the Interpretation of Genetic Data

Abstract

AbstractPopulation-based studies of mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 2000 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g. “macro-haplogroups”) vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers’ understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This “clutter” leaves room for grouping errors and inconsistencies between scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation.Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies.To reduce biases introduced by arbitrarily defined secondary nomenclature-based groupings, we suggest the implementation of phylogenetically meaningful algorithm-based groupings to define a standardized set of “macro-haplogroups”, “meso-haplogroups”, and “micro-haplogroups”. Such phylogenetically informative levels of haplogroup groupings can be easily implemented into haplogroup callers such asHaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.