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118 results for “Age estimation”
Relate-estimated coalescence rates, allele ages, and selection p-values for the 1000 Genomes Project
<p><strong>Overview</strong></p> <p>Coalescence rates, allele ages, and p-values for evidence of positive selection calculated for 2478 samples of the 1000 Genomes Project using Relate.</p> <p>We estimated the joint genealogy of all 1000 GP populations and then extracted the embedded genealogy for each population.<br> For the genealogy of each population, we jointly estimated the population size history and branch lengths. <br> Variants segregating in more than one population therefore have correlated but different allele ages in each population.</p> <p>Please refer to <a href="https://www.nature.com/articles/s41588-019-0484-x">Speidel et al. Nature Genetics (2019)</a> for more details or email leo.speidel@outlook.com for any queries.</p> <p><strong>Coalescence rates</strong></p> <p>The zipped directory coalescence_rates.zip contains coalescence rates for 26 populations in the 1000 Genomes Project data set.</p> <ul> <li>The .coal files show the haploid coalescence rates, please refer to the <a href="https://myersgroup.github.io/relate/modules.html#PopulationSizeScript_FileFormats">Relate documentation</a> for the file format.</li> <li>The popsize.RData file is an R data frame storing the diploid population sizes (0.5/coalescence rate) calculated using the .coal files. The columns of this data frame, named "pop_size", are <ul> <li>gens_ago: Time in generations at which epoch starts. (To get years from generations, we multiply by 28.)</li> <li>population_size: Diploid population size in this epoch.</li> <li>population: Name of population </li> <li>region: Name of region (AFR, AMR, EAS, EUR, SAS)</li> </ul> </li> </ul> <p><strong>Allele ages and selection p-values</strong></p> <p>The zipped directories allele_ages_*.zip contain R data frames for each 1000GP population storing allele ages and selection p-values.<br> Please note that only mutations that segregate in the population and map to a unique branch in the Relate-estimated marginal trees are included. Selection p-values are only provided for mutations of DAF > 2 that pass quality filters (see Speidel et al., 2019). </p> <p>To get an age estimate for a neutral mutation, use 0.5*(lower_age + upper_age). To get years from generations, we multiply by 28.</p> <p>The columns of these data frames, named "allele_ages", are</p> <ul> <li>CHR: chromosome index</li> <li>BP: base-pair position (GRCh37)</li> <li>ID: id of SNP</li> <li>lower_age: Age in generations of coalescence event at the lower end of the branch onto which the mutation maps</li> <li>upper_age: Age in generations of coalescence event at the upper end of the branch onto which the mutation maps</li> <li>ancestral/derived: Ancestral/derived allele</li> <li>upstream: Upstream (5') allele</li> <li>downstream: Downstream (3') allele</li> <li>DAF: Derived-allele frequency</li> <li>pvalue: log10 p-value for selection evidence</li> </ul>
Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins
<p class="MsoNormal"><span>Age is necessary information for the study of life history of wild animals. A general method to estimate the age of odontocetes is counting dental growth layer groups (GLGs). However, this method is highly invasive as it requires the capture and handling of individuals to collect their teeth.</span><span> Recently, the development of DNA-based age </span><span>estimation methods has been actively studied as an alternative to such invasive methods, of which many have used biopsy samples. However, if DNA-based age estimation can be developed from fecal samples, age estimation can be performed without touching or disrupting individuals, thus establishing an entirely non-invasive method. </span><span>We developed an age estimation model using the methylation rate of two gene regions, <em>GRIA2</em> and <em>CDKN2A,</em> measured through methylation-sensitive high-resolution melting (MS-HRM) from fecal samples of wild Indo-Pacific bottlenose dolphins (<em>Tursiops aduncus</em>). The age of individuals was known through conducting longitudinal individual identification surveys underwater. Methylation rates were quantified from 36 samples. Both gene regions showed a significant correlation between age and methylation rate. The age estimation model was constructed based on the methylation rates of both genes which achieved sufficient accuracy (after LOOCV: MAE = 5.08, <em>R<sup>2</sup></em> = 0.34) for the ecological studies of the Indo-Pacific bottlenose dolphins, with a lifespan of 40-50 years. This is the first study to report the use of non-invasive fecal samples to estimate the age of marine mammals.</span></p>
Data from: Methylation-based markers for the estimation of age in African Cheetah, Acinonyx jubatus
<p>This is a dataset for methylation analyses in cheetah done by EpiTYPER mass array.</p>
Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)
<p>Age is an important parameter for bettering the understanding of biodemographic trends-development, survival, reproduction and environmental effects-critical for conservation. However, current age estimation methods are challenging to apply to many species, and no standardised technique has been adopted yet. This study examined the potential use of methylation-sensitive high-resolution melting (MS-HRM), a labour, time, and cost-effective method to estimate chronological age from DNA methylation in Asian elephants (<em>Elephas maximus</em>). The objective of this study was to investigate the accuracy and validation of MS-HRM use for age determination in long-lived species, such as Asian elephants. The average lifespan of Asian elephants is between 50-70 years but some have been known to survive for more than 80 years. DNA was extracted from 53 blood samples of captive Asian elephants across 11 zoos in Japan, with known ages ranging from a few months to 65 years. Methylation rates of two candidate age-related epigenetic genes, <em>RALYL</em> and <em>TET2,</em> were significantly correlated with chronological age. Finally, we established a linear, unisex age estimation model with a mean absolute error (MAE) of 7.36 years. This exploratory study suggests an avenue to further explore MS-HRM as an alternative method to estimate the chronological age of Asian elephants.</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Figure 5. Bayesian phylogeny, with species divergence age estimates reconstructed with BEAST using all the 26 in Complete mitochondrial genomes from museum specimens clarify millipede evolution in the Eastern Arc Mountains
Figure 5. Bayesian phylogeny, with species divergence age estimates reconstructed with BEAST using all the 26 mitochondrial genomes generated in this study. The dataset was supplemented with Thyropygus sp. and Abacion magnum as outgroups, derived from GenBank. GenBank accession numbers are provided in parentheses. Blue bars indicate the 95% highest probability density intervals for node ages. Age estimation for lineage divergence was based on a general arthropod mitochondrial DNA substitution rate and should be considered with caution. *Thyropygus sp. (red font) is very likely to be a misidentification; for more information, see the Discussion.
Figure. Measurement data plotted for all nestlings as a function of age for the black stork: a) wing length (WL), b) head length (HL), c) bill length (BL), and d) tarsus length. in Age estimation of black stork (Ciconia nigra) nestlings from wing, bill, head, and tarsus lengths at the time of ringing
Figure. Measurement data plotted for all nestlings as a function of age for the black stork: a) wing length (WL), b) head length (HL), c) bill length (BL), and d) tarsus length.
Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
<p>Age is an important ecological tool in wildlife conservation. However, it is difficult to estimate in most animals, including felines — most of whom are endangered. Here, we developed the first DNA methylation-based age-estimation technique — as an alternative to current age-estimation methods — for two feline species that share a relatively long genetic distance with each other: domestic cat (<i>Felis catus</i>; 79 blood samples) and an endangered <i>Panthera</i>, the snow leopard (<i>Panthera uncia</i>; 11 blood samples). We measured the methylation rates of two gene regions <span>using </span>methylation-sensitive high-resolution melting (MS-HRM). Domestic cat age was estimated with a mean absolute deviation (MAD) of 3.83 years. Health conditions influenced accuracy of the model. Specifically, the models built on cats with chronic kidney disease (CKD) had lower accuracy than those built on healthy cats. The snow leopard-specific model (i.e. the model that resets the model settings for snow leopards) had a better accuracy (MAD = 2.10 years) than that obtained on using the domestic cat model directly. This implies that our markers could be utilised across species, although changing the model settings when targeting different species could lead to better estimation accuracy. The snow leopard-specific model also successfully distinguished between sexually immature and mature individuals.</p>
Age estimation of the Southern Ocean squid Moroteuthopsis longimana using beaks collected from predators' diet: number and increment widths in the rostrum sagittal section of the lower beak
<p>In this work we used upper and lower beaks of <em>Moroteuthopsis longimana </em>collected from the diet of Antarctic toothfish <em>Dissostichus mawsoni </em>from the Pacific (Ross and Amundsen Seas) and Atlantic (South Sandwich Islands) sectors of the Southern Ocean to study the feasibility of using beaks collected from predators’ stomachs to study the age of Southern Ocean oceanic squid and to estimate the age and growth patterns of <em>M. longimana. </em>We analysed the presence of micro-increments in the rostrum sagittal section (RSS) and the lateral wall of both upper and lower beaks. Lower beak’s RSS was the only section where it is possible to observe and count a readable sequence of increments that allowed to estimate the age of oceanic squid using beaks collected from predators’ stomachs.</p> <p>This dataset contains the number and width of increments found in the RSS of the 10 <em>M. longimana</em>’s lower beaks used in this study. Further information related with the studied beaks such as beak measurements, origin and sample date can be found in the published article.</p> <p>This dataset is associated with the article: Queirós JP, Bartolomé A, Piatkowski U, Xavier JC, Perales-Raya C (2023) Age and growth estimation of Southern Ocean squid <em>Moroteuthopsis longimana</em>: can we use beaks collected from predators’ stomachs? Marine Biology 170, 10. DOI 10.1007/s00227-022-04156-2</p>
Data from: Biological clocks as age estimation markers in animals: a review and meta-analysis
<p>Complete dataset of Methylation and Telomere studies searched, retrieved, analysed, extracted and compared in the review and meta-analysis "<strong>Biological clocks as age estimation markers in animals: a review and meta-analysis".</strong></p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
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Data from: Cochlear tuning in early aging estimated with three methods
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Data from: Age and growth of stocked juvenile Shoal Bass in a tailwater: environmental variation and accuracy of daily age estimates
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Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins
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Data from: Revised age estimates for Northern Resident killer whales (Orcinus orca) based on observed life-history events and demographic discounting
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Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)
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Estimated Age of Carbon within the Soil Organic Layer for Murphy Dome study site in 2013
This file contains radiocarbon data used to estimate age of the soil organic layer for black spruce and Alaska paper birch forest in the Murphy Dome fire scar near Fairbanks, AK. All information was collected in summer 2013 and subsequently processed and analyzed in the laboratory.
Data from: Age and sex prevalence estimate of Joubert Syndrome in Italy
Objective: To estimate the prevalence of Joubert syndrome (JS) in Italy applying standards of descriptive epidemiology, and to provide a molecular characterization of the described patients cohort. Methods: We enrolled all patients with a neuroradiologically confirmed diagnosis of JS and resident in Italy in 2018, and calculated age and sex prevalence, assuming a Poisson distribution. We also investigated the correlation between proband chronological age and age at diagnosis, and performed Next-Generation Sequencing (NGS) analysis on probands' DNA when available. Results: We identified 284 JS patients: the overall, female- and male-specific population-based prevalence rates were 0.47 (95% CI 0.41-0.53), 0.41 (95% CI 0.32-0.49) and 0.53 (95% CI 0.45-0.61) per 100,000 population, respectively. When considering only patients in the age range from 0 to 19 years, the corresponding population-based prevalence rates rose to 1.7 (95% CI 1.49-1.97), 1.62 (95% CI 1.31-1.99) and 1.80 (95% CI 1.49-2.18) per 100,000 population. NGS analysis allowed identifying the genetic cause in 131 out of 219 screened probands. Age at diagnosis was available for 223 probands, with a mean of 6.67 ± 8.10 years, and showed a statistically significant linear relationship with chronological age (r2=0.91; p<0.001). Conclusions: We estimated for the first time the age and sex prevalence of JS in Italy, and investigated their genetic profile. The obtained population-based prevalence rate was approximately 10 times higher than that available in literature for children population.
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
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