Genetic Structure and Eco-Geographical Differentiation of Wild Sheep Fescue (Festuca ovina L.) in Xinjiang, Northwest China

Abstract

Glaciation and mountain orogeny have generated new ecologic opportunities for plants, favoring an increase in the speciation rate. Moreover, they also act as corridors or barriers for plant lineages and populations. High genetic diversity ensures that species are able to survive and adapt. Gene flow is one of the most important determinants of the genetic diversity and structure of out-crossed species, and it is easily affected by biotic and abiotic factors. The aim of this study was to characterize the genetic diversity and structure of an alpine species, Festuca ovina L., in Xinjiang, China. A total of 100 individuals from 10 populations were analyzed using six amplified fragment length polymorphism (AFLP) primer pairs. A total of 583 clear bands were generated, of which 392 were polymorphic; thus, the percentage of polymorphic bands (PPB) was 67.24%. The total and average genetic diversities were 0.2722 and 0.2006 (0.1686–0.2225), respectively. The unweighted group method with arithmetic mean (UPGMA) tree, principal coordinates analysis (PCoA) and Structure analyses revealed that these populations or individuals could be clustered into two groups. The analysis of molecular variance analysis (AMOVA) suggested that most of the genetic variance existed within a population, and the genetic differentiation (Fst) among populations was 20.71%. The Shannon differentiation coefficient (G’st) among populations was 0.2350. Limited gene flow (Nm = 0.9571) was detected across all sampling sites. The Fst and Nm presented at different levels under the genetic barriers due to fragmentation. The population genetic diversity was significant relative to environmental factors such as temperature, altitude and precipitation.

AFLP-based genetic diversity of wild orchardgrass germplasm collections from Central Asia and Western China, and the relation to environmental factors

“…Six informative and reliable pairs of Mse I ( ) and Eco RI ( ) primers used in amplification were selected from a set of 100 primer pairs. The amplification PCR reaction was performed in a final volume of 20 μL as Zhang et al [ 39 ]. The capillary electrophoresis was performed using ABI 3500 DNA analyzer (Applied Biosystems, USA) and those fragments were analyzed using GeneMarker ® (version 2.6, SoftGenetics, LLC).…”

Analysis of Genetic Diversity and Structure Pattern of Indigofera Pseudotinctoria in Karst Habitats of the Wushan Mountains Using AFLP Markers

“…Meanwhile, a PCoA for 364 individuals was computed using R package “stats” [ 65 ] to ordinate the spatial relationships among the individuals. Moreover, Bayesian model-based cluster analysis was performed to infer genetic structure and to define the number of clusters in the dataset using the software STRUCTURE, version 2.3.4 [ 33 ]. Correlated allele frequencies and an admixed model were applied with a burn-in period of 50,000 and 100,000 MCMC replicates after burn-in.…”

“…It is capable of generating a large number of reproducible fragments with genome wide distribution [ 3 , 32 ]. Due to high efficiency and reproducibility of AFLP, it has been widely used to assess genetic relationships and intraspecific genetic diversity of wild or natural plant populations [ 5 , 33 ]. To date, no AFLP marker has been used to assess the genetic diversity of native I. pseudotinctoria populations of China.…”

Phenotype- and SSR-Based Estimates of Genetic Variation between and within Two Important Elymus Species in Western and Northern China

“…Meanwhile, environment parameters are highly correlated with the magnitude and distribution of genetic diversity [ 7 , 43 ]. A species must accumulate more genetic diversity in order to adapt itself to diverse environmental pressures [ 37 ]. In this study, a correlation between genetic diversity and the annual mean temperature, annual precipitation, altitude, and latitude change was found for Elymus accessions.…”

“…The Shannon differentiation coefficient (Gst) was calculated according to the following formula: Gst = (Hsp − Hpop)/Hsp (Hsp, total Shannon information index; Hpop, average Shannon information index within the population). Gene flow (Nm) was calculated as Fst (Nm = (1 − Fst)/4Fst) [ 37 ]. In addition, to determine the relationships between genetic diversity and environmental factors (annual mean temperature, annual precipitation, altitude, and latitude), a linear regression analysis was conducted on SPSS 19.0 (SPSS Inc., Chicago, IL, USA).…”