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5,153 results for “Genetic data”
Data and Code for Publication "Inferring human neutral genetic variation from craniodental phenotypes"
<p>Data and code for publication: H. Rathmann et al., Inferring human neutral genetic variation from craniodental phenotypes. PNAS Nexus.</p> <p>The repository contains:</p> <ul> <li>“<em>R code for DP-DG analysis.txt</em>”: R code for testing levels of neutral evolutionary signals preserved in five craniodental data types: cranial metrics, dental metrics, cranial non-metric traits, dental non-metric traits, and craniodental metrics and non-metric traits combined.</li> </ul> <ul> <li>“<em>Cranial metric data.csv</em>”: Dataset consisting of 37 cranial metric variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by T. Hanihara and originally presented in the publication titled: T. Hanihara, Comparison of craniofacial features of major human groups. <em>Am. J. Phys. Anthropol.</em> 99, 389–412 (1996) (<a href="https://doi.org/10.1002/(SICI)1096-8644(199603)99:3%3c389::AID-AJPA3%3e3.0.CO;2-S">https://doi.org/10.1002/(SICI)1096-8644(199603)99:3<389::AID-AJPA3>3.0.CO;2-S</a>).</li> </ul> <ul> <li>“<em>Dental metric data.csv</em>”: Dataset comprising 28 dental metric variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by T. Hanihara and originally presented in the publication titled: T. Hanihara, H. Ishida, Metric dental variation of major human populations. <em>Am. J. Phys. Anthropol.</em> 128, 287–298 (2005) (<a href="https://doi.org/10.1002/ajpa.20080">https://doi.org/10.1002/ajpa.20080</a>).</li> </ul> <ul> <li>“<em>Cranial non-metric trait data.csv</em>”: Dataset consisting of 24 cranial non-metric trait variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected for the most part by T. Hanihara and presented in the publication titled: T. Hanihara, H. Ishida, Y. Dodo, Characterization of biological diversity through analysis of discrete cranial traits. <em>Am. J. Phys. Anthropol.</em> 121, 241–251 (2003) (<a href="https://doi.org/10.1002/ajpa.10233">https://doi.org/10.1002/ajpa.10233</a>).</li> </ul> <ul> <li>“<em>Dental non-metric trait data.csv</em>”: Dataset comprising 25 dental non-metric trait variables for 26 worldwide modern populations, provided in a comma-separated values file format. The data were collected by C. G. Turner II, G. R. Scott, and J. D. Irish. This individual-level dataset was artificially created from population-level trait frequency information presented in the publications: G. R. Scott, J. D. Irish, <em>Human Tooth Crown and Root Morphology </em>(Cambridge University Press, 2017) (<a href="https://doi.org/10.1017/9781316156629">https://doi.org/10.1017/9781316156629</a>); and: J. D. Irish, A. Morez, L. Girdland Flink, E. L. W. Phillips, G. R. Scott, Do dental nonmetric traits actually work as proxies for neutral genomic data? Some answers from continental- and global-level analyses. <em>Am. J. Phys. Anthropol. </em>172, 347–375 (2020) (<a href="https://doi.org/10.1002/ajpa.24052">https://doi.org/10.1002/ajpa.24052</a>).</li> </ul> <ul> <li>“<em>SNP data.txt</em>”: Dataset comprising 8,821 SNP markers for 26 worldwide modern populations, provided in a genepop file format. The data were obtained from various published sources: I. Lazaridis et al., Ancient human genomes suggest three ancestral populations for present-day Europeans. <em>Nature </em>513, 409–413 (2014) (<a href="https://doi.org/10.1038/nature13673">https://doi.org/10.1038/nature13673</a>); P. Qin, M. Stoneking, Denisovan ancestry in east Eurasian and native American populations. <em>Mol. Biol. Evol. </em>32, 2665–2674 (2015) (<a href="https://doi.org/10.1093/molbev/msv141">https://doi.org/10.1093/molbev/msv141</a>); P. Skoglund et al., Genomic insights into the peopling of the Southwest Pacific. <em>Nature </em>538, 510–513 (2016) (<a href="https://doi.org/10.1038/nature19844">https://doi.org/10.1038/nature19844</a>); M. R. Nelson et al., The Population Reference Sample, POPRES: a resource for population, disease, and pharmacological genetics research. <em>Am. J. Hum. Genet. </em>83, 347–358 (2008) (<a href="https://doi.org/10.1016/j.ajhg.2008.08.005">https://doi.org/10.1016/j.ajhg.2008.08.005</a>); J. K. Pickrell, J. K. Pritchard, Inference of population splits and mixtures from genome-wide allele frequency data. <em>PLoS Genet. </em>8, e1002967 (2012) (<a href="https://doi.org/10.1371/journal.pgen.1002967">https://doi.org/10.1371/journal.pgen.1002967</a>); A. Bergström et al., Insights into human genetic variation and population history from 929 diverse genomes. <em>Science </em>367 (2020) (<a href="https://doi.org/10.1126/science.aay5012">https://doi.org/10.1126/science.aay5012</a>); B. M. Henn et al., Genomic ancestry of North Africans supports back-to-Africa migrations. <em>PLoS Genet. </em>8, e1002397 (2012) (<a href="https://doi.org/10.1371/journal.pgen.1002397">https://doi.org/10.1371/journal.pgen.1002397</a>); S. Mallick et al., The Simons Genome Diversity Project: 300 genomes from 142 diverse populations. <em>Nature </em>538, 201–206 (2016) (<a href="https://doi.org/10.1038/nature18964">https://doi.org/10.1038/nature18964</a>); Lao et al., Correlation between genetic and geographic structure in Europe. <em>Curr. Biol. </em>18, 1241–1248 (2008) (<a href="https://doi.org/10.1016/j.cub.2008.07.049">https://doi.org/10.1016/j.cub.2008.07.049</a>); and M. Lipson et al., Population Turnover in Remote Oceania Shortly after Initial Settlement. <em>Curr. Biol. </em>28, 1157-1165.e7 (2018) (<a href="https://doi.org/10.1016/j.cub.2018.02.051">https://doi.org/10.1016/j.cub.2018.02.051</a>).</li> </ul> <p>For population and variable names and abbreviations, see Supplementary Information in: H. Rathmann et al., Inferring human neutral genetic variation from craniodental phenotypes. PNAS Nexus.</p>
Data from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'
<p>Data sets from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'. These include the test statistics from analyses of real and simulated data, and the data used to generate the figures relating to the goodness-of-fit of the generalised extreme value distribution to the GPS test statistics under the null. Please see the enclosed README for more details.</p>
Fig. 5 in Additional data on Spinitectus petterae (Nematoda: Rhabditida) from Clarias gariepinus (Siluriformes: Clariidae) in the Vaal River system: conserved morphology or high intraspecific genetic variability?
Fig. 5. Scanning electron micrographs of immature female of Spinitectus petterae Boomker, 1993 collected from Clarias gariepinus (Burchell). A – apical view of cephalic region; B – vulva; C – conical tail end; D – conical tail. Abbrevations: A – anus; CA – caudal papilla; L – labium; MT – mucron tip; PL – pseudolabium.
Fig. 4 in Additional data on Spinitectus petterae (Nematoda: Rhabditida) from Clarias gariepinus (Siluriformes: Clariidae) in the Vaal River system: conserved morphology or high intraspecific genetic variability?
Fig. 4. Illustrations of Spinitectus petterae Boomker, 1993 – male, reproductive structures and tail end. A – lateral aspect of posterior section with left and right spicules, and associated structures; B – tip of left spicule from two views and tip of the right spicule with fleshy extension; C – ventral aspect of posterior section with caudal papillae and cloacal opening. Abbreviation: C – cloacal opening; CCO – cytoplasmic core opening; LS – left spicule; LSB – left spicule blade; LSS – left spicule shaft; M – manubrium; PcP – postcloacal papillae; PP – precloacal papillae; RP – rugosa plates; RS – right spicule; SM – spicule muscle; SP – spicular pouch; VD – vas deferens.
Fig. 7 in Additional data on Spinitectus petterae (Nematoda: Rhabditida) from Clarias gariepinus (Siluriformes: Clariidae) in the Vaal River system: conserved morphology or high intraspecific genetic variability?
Fig. 7. Phylogenetic relationships of Spinitectus spp. based on available cox1 mtDNA for Spinitectus based on Bayesian inference (BI)), with Rhabdochona xiphophori Caspeta-Mandujano, Moravec et Salgado-Maldonado, 2001 as the designated outgroup. Posterior probability (BI) and 1,000 bootstrap replicate (maximum likelihood (ML)) support indicated (BI/ML), nodes with less than 0.5 (50 %) support not annotated. Data shaded in colour from indicated geographical locality or river system, and three haplotypes recorded from the Vaal River system indicated (VRS1–VRS3).
Fig. 2 in Additional data on Spinitectus petterae (Nematoda: Rhabditida) from Clarias gariepinus (Siluriformes: Clariidae) in the Vaal River system: conserved morphology or high intraspecific genetic variability?
Fig. 2. Light and scanning electron micrographs of adult females of Spinitectus petterae Boomker, 1993 collected from Clarias gariepinus (Burchell). A – neck showing spines on annular rings; B – first three rings on neck, rings indicated numerically and spine length measurement illustrated; C – apical view of the cephalic region; D – lateral view of cephalic region; E – apical view of cephalic structures; F – excretory pore; G – diminishing spines; H – posterior end; inlay gonopore with vulva I – posterior end with gonopore, vulva position indicated; J – conical tail tip; K – conical tail and mucron tip. Abbreviations: A – anus; AP – amphid; CP – cephalic papillae; L – labia; MT – mucron tip; PL – pseudolabia; PS – porous structure; OO – oral opening; V – vulva; SL – sublabium.
Data and codes from "How does dispersal shape the genetic structure of animal populations in European cities? A simulation approach"
<p>Codes and data used for "Savary et al. How does dispersal shape the genetic structure of animal populations in European cities? A simulation approach".</p> <p> </p>
All data for the preprint Population genetics of Glossina palpalis gambiensis in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease
<p>Data set for the paper titled "Population genetics of <em>Glossina palpalis gambiensis</em> in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease"</p>
Fig. 1. A in Additional data on Spinitectus petterae (Nematoda: Rhabditida) from Clarias gariepinus (Siluriformes: Clariidae) in the Vaal River system: conserved morphology or high intraspecific genetic variability?
Fig. 1. A – map of South Africa; B – map of the river systems in the inlay showing the sampling sites where Spinitectus petterae Boomker, 1993 was collected in Clarias gariepinus (Burchell). Abbreviations: 1 – down-stream of the Vaal River Barrage; 2 – in the Vaal Dam reservoir; 3 – down-stream of the Grootdraai Dam; 4 – Crocodile River.
Data from: A few essential genetic loci distinguish Penstemon species with flowers adapted to pollination by bees or hummingbirds
<p>In the formation of species, adaptation by natural selection generates distinct combinations of traits that function well together. The maintenance of adaptive trait combinations in the face of gene flow depends on the strength and nature of selection acting on the underlying genetic loci. Floral pollination syndromes exemplify the evolution of trait combinations adaptive for particular pollinators. The North American wildflower genus <em>Penstemon</em> displays remarkable floral syndrome convergence, with at least 20 separate lineages that have evolved from ancestral bee pollination syndrome (wide blue-purple flowers that present a landing platform for bees and small amounts of nectar) to hummingbird pollination syndrome (bright red narrowly tubular flowers offering copious nectar). Related taxa that differ in floral syndrome offer an attractive opportunity to examine the genomic basis of complex trait divergence. In this study, we characterized genomic divergence among 229 individuals from a <em>Penstemon </em>species complex that includes both bee and hummingbird floral syndromes. Field plants are easily classified into species based on phenotypic differences and hybrids displaying intermediate floral syndromes are rare. Despite unambiguous phenotypic differences, genomewide differentiation between species is minimal. Hummingbird-adapted populations are more genetically similar to nearby bee-adapted populations than to geographically distant hummingbird-adapted populations, in terms of genomewide <em>d<sub>XY</sub>.</em> However, a small number of genetic loci are strongly differentiated between species. These ~ 20 "species-diagnostic loci", which appear to have nearly fixed differences between pollination syndromes, are sprinkled throughout the genome in high recombination regions. Several map closely to previously established floral trait QTLs. The striking difference between the diagnostic loci and the genome as whole suggests strong selection to maintain distinct combinations of traits, but with sufficient gene flow to homogenize the genomic background. A surprisingly small number of alleles confer phenotypic differences that form the basis of species identity in this species complex.</p>
Supplementary data files for Manzano-Marín et. al. 2023 "Evolution of an alternative genetic code in the Providencia symbiont of the haematophagous leech Haementeria acuecueyetzin"
<p>The data set consists of siz folders:</p> <p><strong>1)</strong> "genome_data": GenBank-formatted annotation files for newly sequenced <em>Providencia siddallii</em> endosymbionts.</p> <p><strong>2)</strong> "orthoMCL_data": Flat-text output files from the OrthoMCL pipeline.</p> <p><strong>3)</strong> "phylongey": MrBayes run files for <em>Providencia</em> spp. Bayesian phylogenetic inference.</p> <p><strong>4)</strong> "UGA_proteins_and_genes": FASTA-formatted alignments of UGA-containing protein-coding gene sequences from figure 3 and table 3.</p> <p><strong>5)</strong> "RepeatModeler_Prsiddallii": Log files for RepeatModeler runs of <em>P. siddallii</em> genomes.</p> <p><strong>6)</strong> "checkM2_Psiddallii": checkM2 input and output files for <em>P. siddallii</em> protein sets.</p> <p><strong>7)</strong> "breseq_runs": breseq output folders for variant calling on newly assembled genomes.</p> <p><strong>8)</strong> "GSAlign_Prsiddallii_GTOCOR": output files of variant calling using GSAlign between <em>P. siddallii</em> strain GTOCOR1 (sampled in 2015 and reported in Manzano-Marín <em>et. al.</em> 2015 <em>GBE</em>) and GTOCOR2 (sampled in 2019 and reported in the associated work).</p>
Data from: Phenotypic plasticity and genetic diversity shed light on endemism of rare Boechera perstellata and its potential vulnerability to climate warming
<p>Premise of the study: The rapid pace of contemporary environmental change puts many species at risk, especially rare species constrained by limited capacity to adapt or migrate due to low genetic diversity and/or fitness. But the ability to acclimate can provide another way to persist through change. We compared the capacity of rare <em>Boechera perstellata</em> (Braun's rockcress) and widespread <em>B. laevigata</em> to acclimate to change.</p> <p>Methods: We investigated the phenotypic plasticity of growth, biomass allocation, and leaf morphology of individuals of <em>B. perstellata</em> and <em>B. laevigata</em> propagated from seed collected from several populations throughout their ranges in a growth chamber experiment to assess their capacity to acclimate. Concurrently, we assessed the genetic diversity of sampled populations using 17 microsatellite loci to assess evolutionary potential.</p> <p>Key results: Plasticity was limited in both rare <em>B. perstellata</em> and widespread <em>B. laevigata</em>, but differences in the plasticity of root traits between species suggest that <em>B. perstellata</em> may have less capacity to acclimate to change. In contrast to its widespread congener, <em>B. perstellata</em> exhibited no plasticity in response to temperature and weaker plastic responses to water availability. As expected, <em>B. perstellata</em> also had lower levels of observed heterozygosity than <em>B. laevigata</em> at the species level, but population-level trends in diversity measures were inconsistent due to high heterogeneity among <em>B. laevigata</em> populations.</p> <p>Conclusions: Overall, the ability of phenotypic plasticity to broadly explain the rarity of <em>B. perstellata</em> vs. commonness of <em>B. laevigata </em>is limited. However, some contextual aspects of our plasticity findings compared with its relatively low genetic variability may shed light on the narrow range and habitat associations of <em>B. perstellata</em> and suggest its vulnerability to climate warming due to acclimatory and evolutionary constraints.</p>
Replication Data for "Exploring Genetic Improvement of the Carbon Footprint of Web Pages"
<p>## Overview</p> <p>In this study, we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed).</p> <p>In an evaluation, our tool outperforms two baselines---the original page and randomized changes---in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools.</p> <p>## Data Contained in This Package</p> <p>- experiment_data/Subject Project-XX-X.xlsx</p> <p>Each spreadsheet contains data collected as part of our experiments, including the fitness scores of the final solutions.</p>
Data, code, and supplementary materials for Pearman P. B., Broennimann, O., et al. Monitoring species genetic diversity in Europe varies greatly and overlooks potential climate change impacts. Nature Ecology & Evolution
<p>The repository contains several archives of digital materials that were used and/or produced in the analyses presented in Pearman, P. B. and Broennimann et al. Monitoring species genetic diversity in Europe varies greatly and overlooks potential climate change impacts. <strong>Nature Ecology & Evolution</strong>, likely 2023. These archives include (1) Supplementary Materials files ; (2) Data and code to generate country-level maps and plots; and (3) data and code to generate all maps of species and joint climate niche marginality, all in G-zipped tar archives. Readme files are available in each archive to guide running of the scripts and identification of objects in the Supplementary Materials. Please see the paper for all co-authors names, and the methods, the results obtained, and discussion of their implications.</p> <p>This work is dedicated to the memory of our friend and colleague Michael Bruford (1963-2023).</p>
Data used in: Phenological sensitivities to climate are similar in two Clarkia congeners: Indirect evidence for facilitation, convergence, niche conservatism, or genetic constraints
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Data and code for: Plastic and quantitative genetic divergence mirror environmental gradients among wild, fragmented populations of Impatiens capensis
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Data from: Individual genetic diversity and probability of infection by avian malaria parasites in blue tits (Cyanistes caeruleus)
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Data for: Genetic structuring and species boundaries in the Atlantic stony coral Favia (Scleractinia, Faviidae)
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Data from: Genomic data reveal deep genetic structure but no support for current taxonomic designation in a grasshopper species complex
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Data from: Association genetics of growth and adaptive traits in loblolly pine (Pinus taeda L.) using whole-exome-discovered polymorphisms
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.