Evidence map›Paper›PMID 27848948›Full record

ArticleHeredity2017

Uncovering the genetic signature of quantitative trait evolution with replicated time series data.

S U Franssen, R Kofler, C Schlötterer

Abstract read
In one paragraph

Article in Heredity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

  1. Article
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  6. Microevolutionary change in wild stickleback: Using integrative time-series data to infer responses to selection.Proceedings of the National Academy of Sciences of the United States of America · 2024
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  9. Review
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  13. Long-term gut microbiome dynamics inProceedings. Biological sciences · 2021
    Article
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  19. Review
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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.

S U FranssenInstitut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria.
R KoflerInstitut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria.
C SchlöttererInstitut für Populationsgenetik, Vetmeduni Vienna, Vienna, Austria.

Funding

European Research Council 294485
6 · The paper itself

Abstract

The genetic architecture of adaptation in natural populations has not yet been resolved: it is not clear to what extent the spread of beneficial mutations (selective sweeps) or the response of many quantitative trait loci drive adaptation to environmental changes. Although much attention has been given to the genomic footprint of selective sweeps, the importance of selection on quantitative traits is still not well studied, as the associated genomic signature is extremely difficult to detect. We propose 'Evolve and Resequence' as a promising tool, to study polygenic adaptation of quantitative traits in evolving populations. Simulating replicated time series data we show that adaptation to a new intermediate trait optimum has three characteristic phases that are reflected on the genomic level: (1) directional frequency changes towards the new trait optimum, (2) plateauing of allele frequencies when the new trait optimum has been reached and (3) subsequent divergence between replicated trajectories ultimately leading to the loss or fixation of alleles while the trait value does not change. We explore these 3 phase characteristics for relevant population genetic parameters to provide expectations for various experimental evolution designs. Remarkably, over a broad range of parameters the trajectories of selected alleles display a pattern across replicates, which differs both from neutrality and directional selection. We conclude that replicated time series data from experimental evolution studies provide a promising framework to study polygenic adaptation from whole-genome population genetics data.

Indexed as

Evolution, MolecularGenetics, PopulationModels, GeneticQuantitative Trait LociAdaptation, PhysiologicalAllelesGene FrequencyGenetic FitnessPopulation DensitySelection, Genetic

Identifiers

PMID27848948
PMCPMC5176121

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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.