Evidence map›Paper›PMID 33769498›Full record

ArticleGenetics2021

Estimating divergence times from DNA sequences.

Per Sjödin, James McKenna, Mattias Jakobsson

Abstract read
In one paragraph

Article in Genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  15. The deep population history in Africa.Human molecular genetics · 2021
    Review
  16. Multiple migrations to the Philippines during the last 50,000 years.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Per SjödinHuman Evolution, Department of Organismal Biology, Uppsala University, Norbyvägen 18 A, Uppsala 752 36, Sweden.
James McKennaHuman Evolution, Department of Organismal Biology, Uppsala University, Norbyvägen 18 A, Uppsala 752 36, Sweden.
Mattias JakobssonHuman Evolution, Department of Organismal Biology, Uppsala University, Norbyvägen 18 A, Uppsala 752 36, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The patterns of genetic variation within and among individuals and populations can be used to make inferences about the evolutionary forces that generated those patterns. Numerous population genetic approaches have been developed in order to infer evolutionary history. Here, we present the "Two-Two (TT)" and the "Two-Two-outgroup (TTo)" methods; two closely related approaches for estimating divergence time based in coalescent theory. They rely on sequence data from two haploid genomes (or a single diploid individual) from each of two populations. Under a simple population-divergence model, we derive the probabilities of the possible sample configurations. These probabilities form a set of equations that can be solved to obtain estimates of the model parameters, including population split times, directly from the sequence data. This transparent and computationally efficient approach to infer population divergence time makes it possible to estimate time scaled in generations (assuming a mutation rate), and not as a compound parameter of genetic drift. Using simulations under a range of demographic scenarios, we show that the method is relatively robust to migration and that the TTo method can alleviate biases that can appear from drastic ancestral population size changes. We illustrate the utility of the approaches with some examples, including estimating split times for pairs of human populations as well as providing further evidence for the complex relationship among Neandertals and Denisovans and their ancestors.

Indexed as

Mutation RatePolymorphism, GeneticAnimalsDNA, AncientGenetic DriftHumansNeanderthalsPopulationTimeDNA, Ancientdivergence timeeffective population sizehuman evolutionpopulation divergence

Identifiers

PMID33769498
PMCPMC8049563

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.