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2,445 results for “Genetics: population”
FIGURE 1 in Combining morphology and population genetic analysis uncover species delimitation in the widespread African tree genus Santiria (Burseraceae)
FIGURE 1. Genetic clusters (GC) detected in Santiria samples from western Central Africa. Bayesian clustering analyses were performed on 479 individuals genotyped at 10 microsatellites loci. A. Variation in means of Ln (likelihood) of the data as a function of the number of hypothetical genetic clusters (K), showing a plateau at K=3. B. Histogram of genetic assignment of the 481 individuals at K = 3. C. Distribution of the three genetic clusters in western Central Africa, and delimitation of the distribution of each genetic cluster (dotted line: GC1, solid line: GC2, dashed line: GC3). We extended the distribution ranges of GC2 and GC3 because morphotypes of both genetic clusters were observed in the south of the Republic of the Congo. Note: Interm. GCx and GCy = intermediate individuals between GCx and GCy.
FIGURE 2 in Combining morphology and population genetic analysis uncover species delimitation in the widespread African tree genus Santiria (Burseraceae)
FIGURE 2. Extended Principal Component Analysis (the Hill-Smith ordination) of quantitative and qualitative traits assessed in 103 Santiria herbarium samples assigned to GC1 (N = 46, open circles), GC2 (N = 21, stars) and GC3 (N = 36, open triangles). Note: NL = number of leaflets per leaf; LL = length of leaves; LP = length of petiole; WP = width of petiole; LP/WP = ratio between LP and WP; TPeL = terminal petiolule length; TLL = terminal leaflet length; TLW = terminal leaflet width; TLL/TLW = ratio between TLL and TLW; TLWe = terminal leaflet weight dry portion; AL = apex length; GD = glandular dots; Le = lenticels.
FIGURE9 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE9. Maximum Parsimony tree of Juglans regia species based on ITS sequences (Populations 1–7 are according to Table 1). Values above branches are bootstrap value.
FIGURE 8 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE 8. TCS Network of Juglans regia cultivars based on nrDNA ITS sequences (Populations 1–7 are according to Table 1). Culivars of Iran (Red colored) are differentiated from Italian cultivars (blue colored).
FIGURE7 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE7. UPGMA tree of Juglans regia cultivars based on nrDNA ITS sequences (Populations 1–7 are according to Table 1).
FIGURE6 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE6. PCoA of SRAP data after 1000 times permutation in the studied Persian walnut populations (Populations 1–7 are according to Table 1).
FIGURE5 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE5. STRUCTURE plot of the studied Persian walnut populations based on k = 2 (Populations 1–7 are according to Table 1).
FIGURE4 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE4. UPGMA plot of ISSR data in Persian walnut populations (Populations 1–7 are according to Table 1).
FIGURE3 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE3. PCA biplot of morphological characters in Persian walnut populations studied (Populations 1–7 are according to Table 1)
FIGURE2 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE2. UPGMA dendrogram of morphological characters in Persian walnut populations studied (Populations 1–7 are according to Table 1).
FIGURE1 in Population genetic study in Juglans regia L. (Persian walnut) and its taxonomic status within the genus Juglans L.
FIGURE1. Distribution map of the studied J. regia wild and cultivated populations (Populations code are according table1).
Figure 6 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 6. Manhattan plots showing SNP levels of ROH per autosome for A, All L. s. svecica individuals, B, Sve_Krk population. The Manhattan plot portrays ROH analysis across 28 autosomes. The height of the peak represents the percentage of individuals sharing homozygous SNP per ROH.
Figure 4 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 4. SNP-based analyses of population structure. A, discriminant analysis of the principal components (DAPC) analysis of genetic structure for two subspecies' genetic clusters (on the left) and, B, for seven populations (on the right). Each colour shade represents subspecies or population genetic clusters, respectively. Every point represents an individual, while inertia ellipses represent 67% of the individuals. Discriminant analysis eigenvalues are displayed by small insets. C, admixture analysis for K = 2. Each vertical bar shows an individual level of shared ancestry between the two subspecies. The two bands below the admixture plot mark individual's subspecies and population affiliation, respectively.
Figure 5 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 5. Pairwise FineRADStructure co-ancestry analysis of 148 genotyped specimens. Ancestral population labels are displayed on the vertical and horizontal axes. Upper horizontal bar stands for subspecies genetic clusters: L. s. svecica—left label, intermediate—centre, L. s. cyanecula—right. Lower horizontal bar depicts population origin if the individuals using the same coding as in Fig. 3.
Figure 3 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 3. The Cytb haplotype network of red-spotted and white-spotted bluethroat populations. Pie charts illustrate the haplotype variants shared among populations. Each circle represents a unique haplotype variant. Sizes of the circles are proportional to the number of individuals. Hatch marks on the branches represent the number of mutational steps that separate haplotypes. Black circles represent hypothetical haplotypes. Red-spotted bluethroat populations are: 1. Krkonoše Mountains (Sve_Krk); 2. Kola (Sve_Klp); 3. Abisko (Sve_Abi). Whitespotted populations are: 1. Třeboňsko (Cya_Trb); 2. St Petersburg (Cya_Stp); 3. Vomáčka (Cya_Vmk); 4. Krkonose Mountains (Cya_Krk). The haplotype marked with the red asterisk is a shared haplotype found in both subspecies.
Figure 1 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 1. Locations of sampled individuals of red-spotted (L. s. svecica) and white-spotted (L. s. cyanecula) bluethroat. L. s. svecica locations: Kola peninsula, Russia (Sve_Klp); Abisko, Sweden (Sve_Abi) and Krkonoše Mountains, Czech Republic (Sve_Krk). L. s. cyanecula locations are: Krkonoše Mountains, Czech Republic (Cya_Krk); Vomáčka, Czech Republic (Cya_Vmk);Třeboňsko, Czech Republic (Cya_Trb) and St. Petersburg, Russia (Cya_Stp). Inset: close-up of the populations in the Czech Republic.
Figure 2 in Limited genetic depletion despite extinction risk: genomic diversity of a peripheral population of red-spotted bluethroats in Central Europe
Figure 2. Box plot comparison of genome-wide heterozygosity by segregating sites at subspecies level (left) and at population level (right). Mean values are marked by a horizontal bar.
Simulation and empirical data for "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations"
<p>Simulation data and empirical data used to generate figures from "Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations". Can be used with code provided on the associated github to regenerate the figures. </p>
MRBIGR: a versatile toolbox for genetic causal inference from population-scale multi-omics data
<p>MRBIGR is a multifunctional toolkit for pre-GWAS, GWAS and post-GWAS of both traditional and multi-omics data. MRBIGR provides all the components needed to build a complete GWAS pipeline, and integrates with rich post-GWAS analysis tools such as QTL annotation and haplotype analysis. In particular, Mendelian randomization (MR) analysis, MR-based network construction, module identification and gene ontology analysis are proposed for further genetic regulation studies. Additionally, it also produces rich plots for visualization of the analysis results and other formatted data.</p> <p>This dataset is used to generate images in MRBIGR papers and can also serve as an example to demonstrate how to use MRBIGR.</p>
Data from: Genetic erosion in wild populations makes resistance to a pathogen more costly
Populations that have suffered from genetic erosion are expected to exhibit reduced average trait values or decreased variation in adaptive traits when experiencing periodic or emergent stressors such as infectious disease. Genetic erosion may consequentially modify the ability of a potential host population to cope with infectious disease emergence. We experimentally investigate this relationship between genetic variability and host response to exposure to an infectious agent both in terms of susceptibility to infection and indirect parasite-mediated responses that also impact fitness. We hypothesized that the deleterious consequences of exposure to the pathogen (Batrachochytrium dendrobatidis) would be more severe for tadpoles descended from European tree frog (Hyla arborea) populations lacking genetic variability. Although all exposed tadpoles lacked detectable infection, we detected this relationship for some indirect host responses, predominantly in genetically depleted animals, as well as an interaction between genetic variability and pathogen dose on lifespan during the post-metamorphic period. Lack of infection and a decreased mass and post-metamorphic lifespan in low genetic diversity tadpoles lead us to conclude that genetic erosion, while not affecting the ability to mount effective resistance strategies, also erodes the capacity to invest in resistance, increased tadpole growth rate and metamorphosis relatively simultaneously.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.