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344 results for “genetic testing”
Fig. 1 in Testing microsatellite loci and preliminary genetic study for Eurasian otter in South Korea
Fig. 1. Spraints collection sites along Ungokcheon Stream, Bonghwa-gun, Gyeongsangbuk-do.
Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"
<p>Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"</p> <p>alignments: alignments used for BEAST ("bayes") and PopArt ("popart"). The "popart" folder also has a traits file per each species.</p> <p>bayesian_plots: TSVs ("tsv") and PDF files ("ogs") generated by BEAST. The "tsv" folder also has the scripts for plotting the results in R.</p> <p>easysfs: scripts, population file and results from the VCF to SFS conversione done by easySFS.</p> <p>fineRADstructure: fineRADstructure input files and output PDF plots ("plots") for <em>C. formosanus</em> ipyrad and Stacks ("stacks") data. </p> <p>logs: logs for ipyrad, ModelTest, PGDspider, PopArt ("popart") and Stacks ("stacks"). The "popart" folder also have the generated networks in a TXT file. The "stacks" folder also has ODS files for calculating the amount of loci per each M/n fixed value.</p> <p>snapclust: STR files used with R for snapclust. Scripts included.</p> <p>stairway_plot: input (blueprint files) and outputs for Stairway Plot 2 analyses. The <em>C. formosanus</em> folder ("chordodes") also has scripts for R plotting.</p> <p>vcfs: VCF and HDF5 files used in this study. Also scripts for filtering/converting data and plotting the PCA with ipyrad (activate python first!) for <em>C. formosanus</em>.</p> <p>"acutogordius" = <em>A. taiwanensis</em><br> "chordodes" = <em>C. formosanus</em><br> "gordius" = <em>G. chiashanus</em></p>
Using inbreeding to test the contribution of non-additive genetic effects to additive genetic variance: A case study in Drosophila serrata
<p>Additive genetic variance, <em>V<sub>A</sub></em>, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g., increased close-relative inbreeding) can alter <em>V<sub>A</sub></em>, but how depends on the, typically unknown, gene action and allele frequencies across many loci. For example, <em>V<sub>A</sub></em> increases proportionally with the inbreeding coefficient when allelic effects are additive, but larger (or smaller) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for rare, recessive alleles to inflate <em>V<sub>A</sub></em> under inbreeding. Applying a powerful paired pedigree design in <em>Drosophila serrata</em>, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity (not allele frequency). Despite close inbreeding and substantial power to detect small <em>V<sub>A</sub></em>, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of <em>V<sub>A</sub></em>, providing insight into historical selection and future evolutionary potential.</p>
Data for: Who defines the "personal utility" of genetic and genomic testing?
<div> <div> <div> <p><strong>Importance</strong>: Expansion in the clinical use of genetic and genomic testing has led to a recognition that these tests provide personal as well as clinical utility to patients and families. It is essential to ensure that members of diverse sociodemographic backgrounds are included in defining and measuring personal utility.</p> <p><strong>Objective</strong>: To determine the demographics of participants contributing to the development of a definition of personal utility for genetic and genomic testing.</p> <p><strong>Evidence</strong> <strong>Review</strong>: We searched PubMed, Scopus, Web of Science, and Embase for peer-reviewed literature published between 2003 and January 2022 on the personal utility of genetic or genomic sequencing. Our review included both qualitative and quantitative studies with samples that included patients, their family members, or the general public. Eligible studies could examine any clinical genetic or genomic test and required the use of the term "utility." Authors extracted and reviewed study and participant characteristics including number of participants, study location (U.S. or international), primary methodology (qualitative or quantitative), race and ethnicity, gender, income, and education data.</p> <p><strong>Findings</strong>: Our final review included 53 studies and 13,315 total participants. Gender was provided for 95.6% of participants (n=12,724), of whom 61.5% were female (n=7,823). Race and/or ethnicity was provided for 83.0% of participants (n=11,048), of whom 82.2% (n=9,083) were White. The remaining participants were identified as Hispanic/Latinx (5.5%, n=607), Asian American and Pacific Islander (3.8%, n=421), Black (3.5%, n=387), multiracial (0.2%, n=27), and various other racial or ethnic categories (3.7%, n=412). Educational attainment was reported for 82.6% of participants. Among these participants, 71.2% (n=7,830) had a bachelor's degree or higher. Income was reported for 66.5% of participants (n=8,857), and 66% of these participants (n=5,831) reported income above the U.S. median.</p> <p><strong>Conclusions and Relevance</strong>: Our results suggest that the concept of personal utility in genetic and genomic testing in the U.S. is disproportionately defined by the perspectives of a narrow subset of the population – specifically non-Hispanic White, well-educated women with above-average household incomes. If we are to provide equitable care in the areas of genomics and genetics, we will need to expand research to include more diverse and representative samples.</p> </div> </div> </div>
Dog guardians and genetic testing: Survey textbox responses & human-animal bond influences
<p>Project was exported from Atlas.ti version 23, data analysis was completed using version 9. This subset data was derived from Wisdom Panel<sup>TM </sup>consumers in which participants provided qualitative responses and answered HAB related questions (see <a href="https://doi.org/10.3390/ani12233360">https://doi.org/10.3390/ani12233360</a>). Qualitative data were obtained via open-ended textboxes. In light of a number of participants providing meaningful data, a post-hoc analysis was added to report central themes present and provide further characterization of potential human-dog relationship influences.</p> <p>Please contact the author directly with any questions about the data.</p>
Trans-eQTL effects on risk of type 1 diabetes: a test of the sparse effector (omnigenic) hypothesis of complex trait genetics (supplementary data)
<p>This repository contains summary-level data generated by performing <a href="https://github.com/molepi-precmed/trans-qtls">Genomewide aggregated trans- effects (GATE) analysis</a> in case-control study of Type 1 Diabetes (T1D).</p>
Data from: Testing concordance and conflict in spatial replication of landscape genetics inferences
<p class="MsoNormal">The degree to which landscape genetics findings can be extrapolated to different areas of a species range is poorly understood. Here, we used a broadly distributed ectothermic lizard (<em>Sceloporus occidentalis</em>, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses in five areas within the Sierra Nevada mountain range. Genetic distances calculated from thousands of ddRAD markers were used to optimize landscape resistance surfaces and infer the effects of landscape and topographic features. Across study areas, we found a great deal of consistency in the primary environmental gradients impacting genetic connectivity, along with some site-specific differences, and a range in the proportion genetic variance explained by environmental factors across study sites. High-elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High temperature seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Across study areas, canyon valleys were always implicated as facilitators to dispersal and key features linking populations and maintaining genetic connectivity, though the relative importance varied in different areas. We emphasize that spatial data layers are complex and multidimensional, and careful consideration of spatial data correlation structure and robust analytic frameworks will be critical to our continued understanding of spatial genetics processes.</p>
Genetic Testing to Understand and Address Renal Disease Disparities Across the United States
ClinicalTrials.gov study NCT04191824. IPD Sharing: YES. Countries: 1. Publications: 1.
Genetic Testing for All Breast Cancer Patients (GET FACTS)
ClinicalTrials.gov study NCT04245176. IPD Sharing: YES. Countries: 1. Publications: 1.
The Moran AMD Genetic Testing Assessment Study
ClinicalTrials.gov study NCT05265624. IPD Sharing: NO. Countries: 1. Publications: 1.
Testing Trametinib and Dabrafenib as a Potential Targeted Treatment in Cancers With BRAF Genetic Changes (MATCH-Subprotocol H)
ClinicalTrials.gov study NCT04439292. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Testing Crizotinib as Potentially Targeted Treatment in Cancers With MET Genetic Changes (MATCH - Subprotocol C1)
ClinicalTrials.gov study NCT06357975. IPD Sharing: YES. Countries: 1. Publications: 0.
Study to Test Genetic Alterations Among Different Dermoscopic Types of Melanocytic Nevi.
ClinicalTrials.gov study NCT00422448. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Pre-Test Genetic Education and Remote Genetic Counseling in Communicating Tumor Profiling Results to Patients With Advanced Cancer
ClinicalTrials.gov study NCT02823652. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Testing AZD4547 as a Potential Targeted Treatment in Cancers With FGFR Genetic Changes (MATCH-Subprotocol W)
ClinicalTrials.gov study NCT04439240. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ALDH2 Genetic Testing in East Asian Community
ClinicalTrials.gov study NCT07392775. IPD Sharing: NO. Countries: 1. Publications: 5.
Targeted Therapy Directed by Genetic Testing in Treating Pediatric Patients With Relapsed or Refractory Advanced Solid Tumors, Non-Hodgkin Lymphomas, or Histiocytic Disorders (The Pediatric MATCH Scre
ClinicalTrials.gov study NCT03155620. IPD Sharing: Not stated. Countries: 4. Publications: 3.
Genetic Testing to Understand and Address Renal Disease Disparities Across the United States Pharmacogenetic Substudy
ClinicalTrials.gov study NCT06748040. IPD Sharing: YES. Countries: 1. Publications: 1.
Testing AZD5363 as a Potential Targeted Treatment in Cancers With AKT Genetic Changes (MATCH-Subprotocol Y)
ClinicalTrials.gov study NCT04439123. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Genetic Testing for Type 2 Diabetes
ClinicalTrials.gov study NCT01060540. IPD Sharing: Not stated. Countries: 1. Publications: 4.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.