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423 results for “Haplotypes”
Fig. 9 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia
Fig. 9. Distribution map of Ngirhaphium meieri, new species and Ngirhaphium thaicum, new species.
Fig. 7 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia
Fig. 7. Ngirhaphium meieri, new species, female habitus. Scale = 1 mm.
Fig. 6 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia
Fig. 6. Ngirhaphium meieri, new species, male habitus (photo: Abdulloh Samoh). Scale = 1 mm.
Fig. 3 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia
Fig. 3. Ngirhaphium thaicum, new species male habitus. Scale = 1 mm.
Fig. 4 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia
Fig. 4. Ngirhaphium thaicum, new species habitus female. Scale = 1 mm.
Evidence for the association between the intronic haplotypes of ionotropic glutamate receptors and schizophrenia
<p>VCF and BED files for the publication "Evidence for the association between the intronic haplotypes of ionotropic glutamate receptors and schizophrenia".</p>
A haplotype-led approach to increase the precision of wheat breeding
<p>Crop productivity must increase at unprecedented rates to meet the needs of the growing worldwide population. Exploiting natural variation for the genetic improvement of crops plays a central role in increasing productivity. Although current genomic technologies can be used for high-throughput identification of genetic variation, methods for efficiently exploiting this genetic potential in a targeted, systematic manner are lacking. Here, we developed a haplotype-based approach to identify genetic diversity for crop improvement using genome assemblies from 15 bread wheat (<em>Triticum aestivum</em>) cultivars. We used stringent criteria to identify identical-by-state haplotypes and distinguish these from near-identical sequences (~99.95% identity). We showed that each cultivar shares ~59 % of its genome with other sequenced cultivars and we detected the presence of extended haplotype blocks containing hundreds to thousands of genes across all wheat chromosomes. We found that genic sequence alone was insufficient to fully differentiate between haplotypes, as were commonly used array-based genotyping chips due to their gene centric design. We successfully used this approach for focused discovery of novel haplotypes from a landrace collection and documented their potential for trait improvement in modern bread wheat. This study provides a framework for defining and exploiting haplotypes to increase the efficiency and precision of wheat breeding towards optimising the agronomic performance of this crucial crop.</p>
Phylogeography of Limia vittata (Cyprinodontiformes: Poeciliidae): geographical distribution of mitochondrial haplotypes is comparable to other Cuban poeciliids
<p>This is the supplementary material associated with the article "Phylogeography of <em>Limia vittata</em> (Cyprinodontiformes: Poeciliidae): geographical distribution of mitochondrial haplotypes is comparable to other Cuban poeciliids", published by the Biological Journal of the Linnean Society (<a href="https://doi.org/10.1093/biolinnean/blad040">https://doi.org/10.1093/biolinnean/blad040</a>). The following documents (pdf format) are included:</p> <p><strong>Supporting Information 1 </strong></p> <p><strong>Table S1.</strong> Sampling size (n) and haplotype (<em>COI</em>+<em>CR</em>) information for each sampling locality of <em>L. vittata</em> included in this study. Numbers correspond to those indicated in Figure 1. A star (*) represents a new locality record for <em>L. vittata</em>. ANC: acronym of the Acuario Nacional de Cuba Collection, La Habana, Cuba.</p> <p><strong>Supporting Information 2</strong></p> <p><strong>Table S1.</strong> Prior distribution of parameters used for the analysis of the <em>L. vittata</em> data based on the ABC approach using DIYABC v.2.1.0 (<a href="#CIT0008">Cornuet <em>et al</em>., 2014</a>). Time is in generations. W-PR: Western Pinar del Río population, W-C: Western-Central population, C-E: Central-Eastern population.</p> <p><strong>Table S2.</strong> Summary statistics between <em>L. vittata</em> haplogroups for each scenario based on 3 × 10<sup>6</sup> simulated datasets. NHA: number of alleles, NSS: number of segregating sites, MPD: mean of pairwise differences, VPD: variance of pairwise differences, MP2: mean pairwise differences within samples, MPB: mean pairwise differences between samples, HST: F<sub><em>ST</em></sub> between samples.</p> <p><strong>Table S3.</strong> Posterior distributions of the parameters based on scenario B for <em>L. vittata</em>. Data were obtained from 1% of the simulated dataset (1 × 10<sup>6</sup>). Time is in generations. W-PR: Western Pinar del Río population, W-C: Western-Central population, C-E: Central-Eastern population. RMAE: relative median of absolute error.</p> <p><strong>Figure S1.</strong> PCA of the summary statistics of the observed dataset and the dataset generated from the prior distribution of parameters to evaluate the three biogeographic scenarios explaining the current distribution of <em>L. vittata</em>. The analysis was performed using DIYABC v.2.1.0 (<a href="#CIT0008">Cornuet <em>et al.</em>, 2014</a>).</p> <p><strong>Figure S2.</strong> Posterior probability of the biogeographic scenarios tested to explain the current distribution of <em>L. vittata</em>. Logistic regression was used to compute the posterior probability using the ABC approach as implemented in DIYABC v.2.1.0 (<a href="#CIT0008">Cornuet <em>et al.</em>, 2014</a>).</p> <p><strong>Supporting Information 3</strong></p> <p><strong>Figure S1.</strong> Bayesian tree depicting the relationships of 160 partial <em>COI</em>+<em>CR</em> sequences of <em>L. vittata</em>. The red dots depict samples from eastern localities (Yateras, Sabanalamar, and Yacabo Abajo) sharing the W-C haplogroups (H9 and H10). Bootstrap support (≥ 93) and Bayesian posterior probabilities (≥ 0.95) are shown for the main clades.</p> <p><strong>Supporting Information 4</strong></p> <p><strong>Figure S1.</strong> Mismatch distribution of pairwise haplotype differences from partial <em>COI</em> and <em>CR</em> sequences for the three haplogroups recovered in <em>L. vittata</em>. Dashed lines represent the distribution of the observed pairwise nucleotide differences, whereas solid lines represent the values expected after a population growth or decline model.</p> <p><strong>Supporting Information 5</strong></p> <p><strong>Figure S1.</strong> Maximum Likelihood tree and haplotype network of <em>Girardinus falcatus</em>, <em>Girardinus metallicus,</em> and the <em>Gambusia punctata</em> species complex based on <em>cytb</em> partial sequences. Each colour represents a geographic region in Cuba. Green: westernmost Cuba, blue: western Cuba, orange: central Cuba, red: eastern Cuba. Numbers on tree branches are bootstrap values ≥ 95% and hatch marks on the networks are the number of mutations. n: number of individuals, H: number of haplotypes, <em>h</em>: haplotype diversity, π: nucleotide diversity, SD: standard deviation.</p> <p><strong>Table S1.</strong> Sampling size (n), GenBank accession numbers, and studies that made available the sequences of cytochrome <em>b</em> used for the phylogeographic comparisons of the different poeciliids in Cuba.</p>
Complement Factor H Haplotypes and Smoking in Age-related Macular Degeneration
ClinicalTrials.gov study NCT01115231. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Introgression of non-native mitochondrial haplotypes from farmed to wild Atlantic salmon
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Detecting selected haplotype blocks in evolve and resequence experiments
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Data from: Unexpected absence of a multiple-queen supergene haplotype from supercolonial populations of Formica ants
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Validating a target-enrichment design for capturing uniparental haplotypes in ancient domesticated animals
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Towards DNA metabarcoding-based haplotype for monitoring terrestrial arthropod communities
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Linked-read sequencing enables haplotype-resolved resequencing at population scale
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Three haplotype-resolved pentaploid Rosa assemblies with assembled and extracted single copy orthologue (SCO) sequences from Rosa canina genome, diploid Rosa species, and sect. Caninae pollen
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Novel Megaptera novaeangliae (Humpback whale) haplotype reference genome
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Data from: Multi-scale spatial genetic structure within and between populations of wild cherry trees in nuclear genotypes and chloroplast haplotypes
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Contrasting association of Leptin receptor polymorphisms and haplotypes with polycystic ovary syndrome in Bahraini and Tunisian women: a case–control study
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Color scores, COI haplotypes and SNP data for Phelotrupes auratus individuals
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Allen Brain Atlas
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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.