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262 results for “Genetic variability”
Data from: Climate variables explain neutral and adaptive variation within salmonid metapopulations: the importance of replication in landscape genetics
Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity, and persistence. We used riverscape genetics modeling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population specific and pairwise FST) within five metapopulations (79 populations, 4,583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events, and summer mean flow) often explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g., basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.
The origin and genetic variability of vegetatively propagated clones identified from old planted trees and plantations of Thujopsis dolabrata var. hondae in Ishikawa Prefecture, Japan
<p class="Keywords"><span><a name="_Hlk10084612">Clonal plantations of <i>Thujopsis dolabrata</i> var. <i>hondae </i>have been established in Ishikawa Prefecture, Japan, since at least the 1800s. Historical planting of the species has led to the development of vegetatively propagated local cultivars, which originated from 'donor' trees that have often been conserved in sacred groves or avenues at shrines and temples. These donor trees must have been selected from natural populations. In this study we estimated the origin and genetic variability of clones identified among old planted trees and clonal plantations of<i> T. dolabrata </i>var. <i>hondae</i>, using 19 microsatellite markers. We discovered 12 clones among old planted trees, including five identical to members of a set of 14 we previously identified in plantations (giving 21 clones in total). Based on analyses combining assignment and exclusion tests, we inferred origins of eight of those 21 clones: six may have originated from a natural population distributed in Ishikawa, one from Hokkaido & Aomori, and the other from Iwate & Yamagata, suggesting the clones constituting cultivars have multiple origins. The clones identified in plantations have significantly lower genetic variability, and higher relatedness, indicating that clones of cultivars have a much narrower genetic base than those of natural populations. We suggest new clones selected from natural populations</a> elsewhere, as well as Ishikawa, are needed for future breeding of <i>T. dolabrata</i> var. <i>hondae</i> to develop clonal forestry for this species.</span></p>
Data from: The effect of variable frequency of sexual reproduction on the genetic structure of natural populations of a cyclical parthenogen
Cyclical parthenogens are a valuable system in which to empirically test theoretical predictions as to the genetic consequences of sexual reproduction in natural populations, particularly if the frequency of sexual relative to asexual reproduction can be quantified. In this study we utilized a series of lake populations of the cyclical parthenogen, Daphnia pulicaria, that vary consistently in their investment in sexual reproduction, to address the questions of whether the ecological variation in investment in sex is detectable at the genetic level, and if so, whether the genetic patterns seen are consistent with theoretical predictions. We show that there is variation in the genetic structure of these populations in a manner consistent with their investment in sexual reproduction. Populations engaging in a high frequency of sex were in Hardy-Weinberg and gametic phase equilibrium, and showed little genotypic differentiation across sampled years. In contrast, populations with a lower frequency of sex deviated widely from equilibrium, had reduced multi-locus clonal diversity, and showed significant temporal genotypic deviation.
Data from: Modelling the co-evolution of indirect genetic effects and inherited variability
When individuals interact, their phenotypes may be affected by genes in their social partners, a phenomenon known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, competition not only affects trait levels of individuals, but also inflates variation of trait values among individuals. Variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, models of IGE and inherited variability do not allow for such relationship. Models of trait levels show IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to inherited variability. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of genetic and environmental effects of the focal individual and of the difference in trait values between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait which is measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. Our simulations show that the model results in increased variability of body weight with increase of competition. When competition decreases, variability becomes significantly smaller. Our findings suggest we may have been overlooking an entire level of genetic variation in variability, the one due to IGEs.
Data from: Effects of postglacial phylogeny and genetic diversity on the growth variability and climate sensitivity of European silver fir
<p>The zip file contains almost 2000 tree ring width data in Tucson format (rwl) from 78 sites across the Carpathian Mountains in Europe. In addition, genetic data used in the study are also attached. The datasets were used in the paper on <strong>Effects of postglacial phylogeny and genetic diversity on the growth variability and climate sensitivity of European silver fir </strong>published in Journal of Ecology.</p>
Fig. 2 in Morphological and genetic variability of Cotesia tibialis species complex (Hymenoptera: Braconidae: Microgastrinae)
Fig. 2. The variability of C. tibialis female's hind femora and cocoons; antennae.
Data from: Temporal changes in genetic variability in three bumblebee species from Rio Grande do Sul, South Brazil
[No abstract entered]
Epigenetic and genetic variability of Hordeum murinum subsp. leporinum
<p>Binary array from MSAP analysis for epigenetic variability studies in Hordeum murinum subsp. leporinum in response to climatic treatments. Also, a binary array from AFLP analysis provides basis of genetic diversity in this species.</p>
Figure 5 in Morphometric and genetic variability among Mediterranean cereal cyst nematode (Heterodera latipons) populations in Turkey
Figure 5. PCR products of Heterodera latipons using AB28 and TW81 universal primers yielded a single fragment of approximately 1060 bp. L: 100-bp DNA ladder; C: negative control.
Figure 4 in Morphometric and genetic variability among Mediterranean cereal cyst nematode (Heterodera latipons) populations in Turkey
Figure 4. Fenestral area of Heterodera latipons vulval cones from Adana (A), Hatay (B), Kilis (C), Gaziantep (D), and Mardin (E).
Fig. 1 in DNA barcoding and genetic variability of earthworms (Clitellata: Oligochaeta) with new records from Mizoram, India
Fig. 1 Location of sampling sites of earthworms from Mizoram, NER
Fig. 2 in DNA barcoding and genetic variability of earthworms (Clitellata: Oligochaeta) with new records from Mizoram, India
Fig. 2 Results from ABGD analysis showing stable count of 24 OTUs
FIGURE 3 in Genetic diversity and morphological variability in Polygonum aviculare s.l. (Polygonaceae) of Iran
FIGURE 3. NJ tree of populations based on genetic data.
Data from: Environmentally induced changes in correlated responses to selection reveal variable pleiotropy across a complex genetic network
Selection in novel environments can lead to a coordinated evolutionary response across a suite of characters. Environmental conditions can also potentially induce changes in the genetic architecture of complex traits, which in turn could alter the pattern of the multivariate response to selection. We describe a factorial selection experiment using the nematode Caenorhabditis remanei in which two different stress-related phenotypes (heat and oxidative stress resistance) were selected under three different environmental conditions. The pattern of covariation in the evolutionary response between phenotypes or across environments differed depending on the environment in which selection occurred, including asymmetrical responses to selection in some cases. These results indicate that variation in pleiotropy across the stress response network is highly sensitive to the external environment. Our findings highlight the complexity of the interaction between genes and environment that influences the ability of organisms to acclimate to novel environments. They also make clear the need to identify the underlying genetic basis of genetic correlations in order understand how patterns of pleiotropy are distributed across complex genetic networks.
Figure 6 from: Shapoval NA, Lukhtanov VA (2015) Taxonomic interpretation of chromosomal and mitochondrial DNA variability in the species complex close to Polyommatus (Agrodiaetus) dama (Lepidoptera, Lycaenidae). In: Lukhtanov VA, Kuznetsova VG, Grozeva S, Golub NV (Eds) Genetic and cytogenetic structure of biological diversity in insects. ZooKeys 538: 1-20. https://doi.org/10.3897/zookeys.538.6559
Figure 6 - Underside and upperside of the Polyommatus (Agrodiaetus) karindus saravandi ssp. n. wings. A upperside (left) and underside (right) of the male wings B upperside (left) and underside (right) of the female wings.
Figure 4 from: Shapoval NA, Lukhtanov VA (2015) Taxonomic interpretation of chromosomal and mitochondrial DNA variability in the species complex close to Polyommatus (Agrodiaetus) dama (Lepidoptera, Lycaenidae). In: Lukhtanov VA, Kuznetsova VG, Grozeva S, Golub NV (Eds) Genetic and cytogenetic structure of biological diversity in insects. ZooKeys 538: 1-20. https://doi.org/10.3897/zookeys.538.6559
Figure 4 - COI Haplotype analysis. A geographical distribution of haplogroups. Number of studied individuals sharing the same haplogroup is given in parentheses B most parsimonious COI haplotype network; h01–h12 are COI haplotypes; GH1–GH5 are COI haplogroups. Number of studied individuals sharing the same haplotype is given in parentheses.
Figure 5 from: Shapoval NA, Lukhtanov VA (2015) Taxonomic interpretation of chromosomal and mitochondrial DNA variability in the species complex close to Polyommatus (Agrodiaetus) dama (Lepidoptera, Lycaenidae). In: Lukhtanov VA, Kuznetsova VG, Grozeva S, Golub NV (Eds) Genetic and cytogenetic structure of biological diversity in insects. ZooKeys 538: 1-20. https://doi.org/10.3897/zookeys.538.6559
Figure 5 - Holotype of Polyommatus (Agrodiaetus) karindus saravandi, sample W064. Upperside (left) and underside (right) of the male wings.
Figure 3 from: Shapoval NA, Lukhtanov VA (2015) Taxonomic interpretation of chromosomal and mitochondrial DNA variability in the species complex close to Polyommatus (Agrodiaetus) dama (Lepidoptera, Lycaenidae). In: Lukhtanov VA, Kuznetsova VG, Grozeva S, Golub NV (Eds) Genetic and cytogenetic structure of biological diversity in insects. ZooKeys 538: 1-20. https://doi.org/10.3897/zookeys.538.6559
Figure 3 - The Bayesian tree of Polyommatus (Agrodiaetus) dama and Polyommatus (Agrodiaetus) karindus based on analysis of the cytochrome c oxidase subunit I gene from 57 specimens. Numbers at nodes indicate Bayesian posterior probability. Agrodiaetus karindus karindus and Agrodiaetus karindus saravandi clusters highlighted in pink and blue respectively.
Figure 2 from: Shapoval NA, Lukhtanov VA (2015) Taxonomic interpretation of chromosomal and mitochondrial DNA variability in the species complex close to Polyommatus (Agrodiaetus) dama (Lepidoptera, Lycaenidae). In: Lukhtanov VA, Kuznetsova VG, Grozeva S, Golub NV (Eds) Genetic and cytogenetic structure of biological diversity in insects. ZooKeys 538: 1-20. https://doi.org/10.3897/zookeys.538.6559
Figure 2 - Male meiosis I karyotypes of: A Polyommatus (Agrodiaetus) karindus karindus, sample E399, Iran, Kordestan, 40 km SW Saqqez, 1800–1900 m, 2004.VII.29, V. Lukhtanov leg., n = 68 B Polyommatus (Agrodiaetus) karindus saravandi, sample W372, Iran, Nahavand 34°02.57'N; 048°20.22'E, 2173m, 2009.VIII.02, V. Lukhtanov & N. Shapoval leg., n = 73. Scale bar = 10 µm.
Figure 1 from: Shapoval NA, Lukhtanov VA (2015) Taxonomic interpretation of chromosomal and mitochondrial DNA variability in the species complex close to Polyommatus (Agrodiaetus) dama (Lepidoptera, Lycaenidae). In: Lukhtanov VA, Kuznetsova VG, Grozeva S, Golub NV (Eds) Genetic and cytogenetic structure of biological diversity in insects. ZooKeys 538: 1-20. https://doi.org/10.3897/zookeys.538.6559
Figure 1 - Distribution ranges of Polyommatus (Agrodiaetus) dama (green circles), Polyommatus (Agrodiaetus) karindus karindus (red circles) and Polyommatus (Agrodiaetus) karindus saravandi (blue circles). The asterisk indicates the type locality of Polyommatus (Agrodiaetus) karindus karindus.
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Allen Brain Atlas
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