Evidence map›Paper›PMID 30678640›Full record

ArticleBMC genomics2019

Genome-wide signatures of local adaptation among seven stoneflies species along a nationwide latitudinal gradient in Japan.

Maribet Gamboa, Kozo Watanabe

Abstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

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

7 citing papers in PubMed.

  1. Article
  2. Genome-wide detection ofFrontiers in cellular and infection microbiology · 2023
    Article
  3. Article
  4. Article
  5. Article
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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

2 authors.

Maribet GamboaDepartment of Civil and Environmental Engineering, Ehime University, Matsuyama, 790-0871, Japan. gamboa@cee.ehime-u.ac.jp.ORCID http://orcid.org/0000-0003-3744-1339
Kozo WatanabeDepartment of Civil and Environmental Engineering, Ehime University, Matsuyama, 790-0871, Japan.

Funding

Japan Society for the Promotion of Science 13F03366, 17F17908, P13366, PU17908
6 · The paper itself

Abstract

backgroundEnvironmental heterogeneity continuously produces a selective pressure that results in genomic variation among organisms; understanding this relationship remains a challenge in evolutionary biology. Here, we evaluated the degree of genome-environmental association of seven stonefly species across a wide geographic area in Japan and additionally identified putative environmental drivers and their effect on co-existing multiple stonefly species. Double-digest restriction-associated DNA (ddRAD) libraries were independently sequenced for 219 individuals from 23 sites across four geographical regions along a nationwide latitudinal gradient in Japan.

resultsA total of 4251 candidate single nucleotide polymorphisms (SNPs) strongly associated with local adaptation were discovered using Latent mixed models; of these, 294 SNPs showed strong correlation with environmental variables, specifically precipitation and altitude, using distance-based redundancy analysis. Genome-genome comparison among the seven species revealed a high sequence similarity of candidate SNPs within a geographical region, suggesting the occurrence of a parallel evolution process.

conclusionsOur results revealed genomic signatures of local adaptation and their influence on multiple, co-occurring species. These results can be potentially applied for future studies on river management and climatic stressor impacts.

Indexed as

Genome, InsectAnimalsBiological EvolutionEnvironmentGenomicsJapanNeopteraPolymorphism, Single NucleotideSequence Analysis, DNAAdaptationddRADEnvironmental associationsLandscape genetics

Identifiers

PMID30678640
PMCPMC6346529

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Registered trials

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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.