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Explaining large mitochondrial sequence differences within a population sample

Overview of attention for article published in Royal Society Open Science, November 2017
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Article details
Title
Explaining large mitochondrial sequence differences within a population sample
Published in
Royal Society Open Science, November 2017
DOI 10.1098/rsos.170730
Pubmed ID
Authors
Abstract

Mitochondrial DNA sequence is frequently used to infer species' boundaries, as divergence is relatively rapid when populations are reproductively isolated. However, the shared history of a non-recombining gene naturally leads to correlation of pairwise differences, resulting in mtDNA clusters that might be mistaken for evidence of multiple species. There are four distinct processes that can explain high levels of mtDNA sequence difference within a single sample. Here, we examine one case in detail as an exemplar to distinguish among competing hypotheses. Within our sample of tree wētā (Hemideina crassidens; Orthoptera), we found multiple mtDNA haplotypes for a protein-coding region (cytb/ND1) that differed by a maximum of 7.9%. From sequencing the whole mitochondrial genome of two representative individuals, we found evidence of constraining selection. Heterozygotes were as common as expected under random mating at five nuclear loci. Morphological traits and nuclear markers did not resolve the mtDNA groupings of individuals. We concluded that the large differences found among our sample of mtDNA sequences were simply owing to a large population size over an extended period of time allowing an equilibrium between mutation and drift to retain a great deal of genetic diversity within a single species.

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Mendeley demographics

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The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 10 23%
Student > Ph. D. Student 8 18%
Student > Master 7 16%
Student > Doctoral Student 3 7%
Student > Bachelor 3 7%
Other 7 16%
Unknown 6 14%
Readers by discipline
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 32%
Agricultural and Biological Sciences 14 32%
Environmental Science 2 5%
Veterinary Science and Veterinary Medicine 1 2%
Arts and Humanities 1 2%
Other 3 7%
Unknown 9 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 January 2018.
All research outputs
#18,164,776
of 23,335,153 outputs
Outputs from Royal Society Open Science
#3,426
of 4,174 outputs
Outputs of similar age
#308,114
of 440,332 outputs
Outputs of similar age from Royal Society Open Science
#118
of 141 outputs
Altmetric has tracked 23,335,153 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,174 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 50.0. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.