Evidence map›Paper›PMID 25079034›Full record

ArticleBMC genetics2014

Genetic diversity and population structure of the Tibetan poplar (Populus szechuanica var. tibetica) along an altitude gradient.

Dengfeng Shen, Wenhao Bo, Fang Xu, Rongling Wu

Abstract read
In one paragraph

Article in BMC genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. A Statistical Mechanics Model to Decode Tissue Crosstalk During Graft Formation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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  6. Genetic adaptation of Tibetan poplar (Ecology and evolution · 2020
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  12. Genetic diversity and differentiation amongEcology and evolution · 2018
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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

4 authors.

Dengfeng Shen
Wenhao Bo
Fang Xu
Rongling Wu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Tibetan poplar (Populus szechuanica var. tibetica Schneid), which is distributed at altitudes of 2,000-4,500 m above sea level, is an ecologically important species of the Qinghai-Tibet Plateau and adjacent areas. However, the genetic adaptations responsible for its ability to cope with the harsh environment remain unknown.

resultsIn this study, a total of 24 expressed sequence tag microsatellite (EST-SSR) markers were used to evaluate the genetic diversity and population structure of Tibetan poplars along an altitude gradient. The 172 individuals were of genotypes from low-, medium- and high-altitude populations, and 126 alleles were identified. The expected heterozygosity (HE) value ranged from 0.475 to 0.488 with the highest value found in low-altitude populations and the lowest in high-altitude populations. Genetic variation was low among populations, indicating a limited influence of altitude on microsatellite variation. Low genetic differentiation and high levels of gene flow were detected both between and within the populations along the altitude gradient. An analysis of molecular variance (AMOVA) showed that 6.38% of the total molecular variance was attributed to diversity between populations, while 93.62% variance was associated with differences within populations. There was no clear correlation between genetic variation and altitude, and a Mantel test between genetic distance and altitude resulted in a coefficient of association of r = 0.001, indicating virtually no correlation.

conclusionMicrosatellite genotyping results showing genetic diversity and low differentiation suggest that extensive gene flow may have counteracted local adaptations imposed by differences in altitude. The genetic analyses carried out in this study provide new insight for conservation and optimization of future arboriculture.

Indexed as

AltitudeGenetic VariationDNA, PlantGene FlowGenetics, PopulationGenotypeMicrosatellite RepeatsModels, GeneticPopulusSequence Analysis, DNATibetDNA, Plant

Identifiers

PMID25079034
PMCPMC4118629

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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.