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927 results for “genetic study”
Data from: Using a reference population yardstick to calibrate and compare genetic diversity reported in different studies: an example from the brown bear.
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Data from: Genome-wide association studies dissect the genetic architecture of seed and yield component traits in cowpea (Vigna unguiculata L. Walp)
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A comparative evaluation of ChatGPT 3.5 and ChatGPT 4 in responses to selected genetics questions - Full study data
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Data from: Genetic assignment with isotopes and habitat suitability (GAIAH), a migratory bird case study
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Data from: Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study
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Data from: Competitive consequences determined by phenotypic but not genetic distance: a study with asexual Daphnia genotypes
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Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations
<p>Data set linked to the paper, "Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations". Pre-print of the paper is here: <a href="https://doi.org/10.1101/2020.06.16.146803">https://doi.org/10.1101/2020.06.16.146803</a>.</p> <p> </p> <p>cross_cancer_sum_stats.txt.gz contains summary genome-wide association statistics for susceptibility to single cancers (breast (BR), prostate (PR), ovarian (OV), endometrial (EN), estrogen receptor (ER)-positive breast (POS), ER-negative breast (NEG), and high-grade serous ovarian (HGS) cancers) and from the cross-cancer meta-analysis (main [main] and subtype-focused [sub]). EA in the header refers to the effect allele, OA is the other allele, EAF is the effect allele frequency in the largest of the single cancer data sets (BR), IMPR2 is the imputation quality in the largest of the single cancer data sets (BR), SE is the standard error, PVAL is the P-value, RE2Cs1 is the RE2C statistic mean effect part, RE2Cs2 is the RE2C statistic heterogeneity part, RE2Cp* is the RE2C* P-value. More on RE2Cp* can be found here: <a href="http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite">http://software.buhmhan.com/RE2C/index.php?mid=contact&act=dispBoardWrite</a> and in <a href="https://academic.oup.com/bioinformatics/article/33/14/i379/3953957">https://academic.oup.com/bioinformatics/article/33/14/i379/3953957</a> SNP names in cross_cancer_sum_stats.txt.gz include the chromosome and build 37 position.</p> <p> </p> <p>main_tetrachoric_corr_matrix.txt and subtype_tetrachoric_corr_matrix.txt provide the tetrachoric correlation matrices used in the main and subtype-focused meta-analyses. These were also used to specify the cryptic.cor argument of the exh.abf function of MetABF. More on MetABF can be found here: <a href="https://github.com/trochet/metabf">https://github.com/trochet/metabf</a> and in <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202">https://onlinelibrary.wiley.com/doi/abs/10.1002/gepi.22202</a></p> <p> </p> <p>prior_sigmas_for_metabf.txt contains the values used to specify the prior.sigma argument of the exh.abf function in MetABF.</p> <p> </p> <p>The breast cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29059683/"><strong>PMID 29059683</strong></a> and can be downloaded from <a href="http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/">http://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas- summary-results-breast-cancer-risk-2017/</a> (this link also includes acknowledgements). The prostate cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/29892016/"><strong>PMID 29892016</strong></a> and can be downloaded from: <a href="http://practical.icr.ac.uk/blog/?page_id=8164">http://practical.icr.ac.uk/blog/?page_id=8164</a> (this link also includes acknowledgements). The ovarian cancer data used are described in <a href="https://pubmed.ncbi.nlm.nih.gov/28346442/"><strong>PMID 28346442</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST004415">https://www.ebi.ac.uk/gwas/studies/GCST004415</a>. The endometrial cancer data are described in <a href="https://pubmed.ncbi.nlm.nih.gov/30093612/"><strong>PMID 30093612</strong></a> and can be downloaded from <a href="https://www.ebi.ac.uk/gwas/studies/GCST006464">https://www.ebi.ac.uk/gwas/studies/GCST006464</a>. These links point to the same data that form the basis of the cross_cancer_sum_stats.txt.gz file.</p> <p> </p> <p><strong>The sample size and precision of the data presented should preclude identification of any individual study participant. However, in downloading these data, you undertake not to attempt to identify individual study participant and not to re-post these data to a third-party website. Please cite the PMIDs highlighted above along with the appropriate acknowledements if you use the cross_cancer_sum_stats.txt.gz file.</strong></p> <p> </p> <p>If you have any questions about this repository, please email Siddhartha Kar at siddhartha dot kar at bristol dot ac dot uk</p>
The influence of a priori grouping on inference of genetic clusters: simulation study and literature review of the DAPC method
Inference of genetic clusters is a key aim of population genetics, sparking development of numerous analytical methods. Within these, there is a conceptual divide between finding de novo structure versus assessment of a priori groups. Recently developed, Discriminant Analysis of Principal Components (DAPC), combines discriminant analysis (DA) with principal component (PC) analysis. When applying DAPC, the groups used in the DA (specified a priori or described de novo) need to be carefully assessed. While DAPC has rapidly become a core technique, the sensitivity of the method to misspecification of groups and how it is being empirically applied, are unknown. To address this, we conducted a simulation study examining the influence of a priori versus de novo group designations, and a literature review of how DAPC is being applied. We found that with a priori groupings, distance between genetic clusters reflected underlying FST. However, when migration rates were high and groups were described de novo there was considerable inaccuracy, both in terms of the number of genetic clusters suggested and placement of individuals into those clusters. Nearly all (90.1%) of 224 studies surveyed used DAPC to find de novo clusters, and for the majority (62.5%) the stated goal matched the results. However, most studies (52.3%) omit key run parameters, preventing repeatability and transparency. Therefore, we present recommendations for standard reporting of parameters used in DAPC analyses. The influence of groupings in genetic clustering is not unique to DAPC, and researchers should consider their goal and which methods will be most appropriate.
Figure 2 in Non-invasive genetic study and population monitoring of the brown bear (Ursus arctos) (Mammalia: Ursidae) in Kastoria region - Greece
Figure 2. (A) Means of estimated LnP (Data) and standard deviations for K = 1 to K = 5. (B) Factorial correspondence analysis plot of multilocus genotypes for 82 brown bear individuals identified in the present study.
Association of common genetic variants with brain microbleeds: A genome-wide association study
<p><strong>Objective:</strong> To identify common genetic variants associated with the presence of brain microbleeds (BMB).</p> <p><strong>Methods:</strong> We performed genome-wide association studies in 11 population-based cohort studies and 3 case-control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMB were rated on susceptibility-weighted or T2*-weighted gradient echo magnetic resonance imaging sequences, and further classified as lobar, or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of <em>APOE</em> ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMB.</p> <p><strong>Results: </strong>BMB were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the <em>APOE</em> region reached genome-wide significance for its association with BMB (lead SNP rs769449; OR<sub>any BMB</sub> (95% CI)=1.33 (1.21-1.45); p=2.5x10-10). <em>APOE</em> ε4 alleles were associated with strictly lobar (OR (95% CI)=1.34 (1.19- 1.50); p=1.0x10-6) but not with mixed BMB counts (OR (95% CI)=1.04 (0.86-1.25); p=0.68). <em>APOE</em> ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.</p> <p><strong>Conclusions: </strong>Genetic variants in the <em>APOE</em> region are associated with the presence of BMB, most likely due to the <em>APOE</em> ε4 allele count related to a higher number of strictly lobar BMB. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.</p>
Microsatellite genotype data from seven loci for a phylogeographic/population genetic study of the South African endemic freshwater crab Potamonautes lividus sampled from eight localities in the KwaZulu-Natal and Eastern Cape provinces in South Africa
<ol> <li>During the present study, the phylogeography of the only southern African IUCN Red Listed vulnerable (VU) freshwater crab, <i>Potamonautes lividus</i> was investigated by surveying several localities in the Eastern Cape and KwaZulu-Natal provinces in South Africa. Both nuclear and mitochondrial DNA markers were used, and it was hypothesized, that marked genetic differentiation should be present, while niche modeling was undertaken to explore the distribution of the species along the east coast of South Africa. <span><span>Further, the shortfalls in the present approach to IUCN Red Listing, as illustrated by a vulnerable species of crabs are discussed</span></span>.</li> <li>Results from the mtDNA revealed the presence of two haploclades confined to specimens from the two provinces respectively and the general absence of maternal dispersal; a fact that was further validated by the marked <i>F</i><sub>ST</sub> data and high F<sub>ST</sub>. Within the Eastern Cape haploclade, low frequency maternal dispersal occurred, corroborated by the low F<sub>ST</sub>. In contrast, no haplotypes were shared in the KwaZulu-Natal haploclade a fact corroborated by marked F<sub>ST</sub> differences. </li> <li>The microsatellite data demonstrated the presence of higher frequency, possibly paternally biased dispersal of specimens between the Eastern Cape and KwaZulu-Natal provinces. Our results suggest that presence of two distinct management units within <i>P. lividus</i>. Divergence time estimation suggest a late Pleistocene cladogenesis between the Eastern Cape and KwaZulu-Natal haploclades. </li> <li>Considering the presence of <i>P. lividus</i> in several newly collected nature conservation areas in both provinces, and its potential presence in the intermediary area based on the MAXENT niche modeling, our data suggest the species IUCN Red Listing status should be downgraded to LC.</li> <li>A comparison of all the EN, VU and CR IUCN Red Listed freshwater crabs for the entire Afrotropical region reveals the lack of recent sampling in the three biodiversity hotspots in West, Central and East Africa, with mountainous areas containing a disproportionate number of species with most species being devoid of phylogeographic study. </li> </ol>
Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. 'Animal models', which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations. However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non-randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology. We first illustrate how the occurrence of non-random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce 'genetic group' methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents. We summarize the conceptual foundations of genetic group animal models and provide extensive, step-by-step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods. Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow. We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.
Data from: QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations – case studies in forage breeding
Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enable the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross pollinated species. Each strategy was compared across three levels of HS family mean heritability (0.1, 0.5 and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.
Data from: Integrating nonadditive genomic relationship matrices into the study of genetic architecture of complex traits
The study of genetic architecture of complex traits has been dramatically influenced by implementing genome-wide analytical approaches during recent years. Of particular interest are genomic prediction strategies which make use of genomic information for predicting phenotypic responses instead of detecting trait-associated loci. In this work, we present the results of a simulation study to improve our understanding of the statistical properties of estimation of genetic variance components of complex traits, and of additive, dominance, and genetic effects through best linear unbiased prediction methodology. Simulated dense marker information was used to construct genomic additive and dominance matrices, and multiple alternative pedigree- and marker-based models were compared to determine if including a dominance term into the analysis may improve the genetic analysis of complex traits. Our results showed that a model containing a pedigree- or marker-based additive relationship matrix along with a pedigree-based dominance matrix provided the best partitioning of genetic variance into its components, especially when some degree of true dominance effects was expected to exist. Also, we noted that the use of a marker-based additive relationship matrix along with a pedigree-based dominance matrix had the best performance in terms of accuracy of correlations between true and estimated additive, dominance, and genetic effects.
Data from: Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice
Manual phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, automated, non-destructive phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale assessment of rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We could explain 30% of the grain yield variance from 2 tiller traits assessed in the early growth stages. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and rice accessions containing haplotype H3 displayed much smaller tiller angles. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.
Data from: Development and evaluation of 200 novel SNP assays for population genetic studies of westslope cutthroat trout and genetic identification of related taxa
DNA sequence data were collected and screened for single nucleotide polymorphisms (SNPs) in westslope cutthroat trout (Oncorhynchus clarki lewisi) and also for substitutions that could be used to genetically discriminate rainbow trout (O. mykiss) and cutthroat trout, as well as several cutthroat trout subspecies. In total, 260 expressed sequence tag-derived loci were sequenced and allelic discrimination genotyping assays developed from 217 of the variable sites. Another 50 putative SNPs in westslope cutthroat trout were identified by restriction-site-associated DNA sequencing, and seven of these were developed into assays. Twelve O. mykiss SNP assays that were variable within westslope cutthroat trout and 12 previously published SNP assays were also included in downstream testing. A total of 241 assays were tested on six westslope cutthroat trout populations (N = 32 per population), as well as collections of four other cutthroat trout subspecies and a population of rainbow trout. All assays were evaluated for reliability and deviation from Hardy–Weinberg and linkage equilibria. Poorly performing and duplicate assays were removed from the data set, and the remaining 200 assays were used in tests of population differentiation. The remaining markers easily distinguished the various subspecies tested, as evidenced by mean GST of 0.74. A smaller subset of the markers (N = 86; average GST = 0.40) was useful for distinguishing the six populations of westslope cutthroat trout. This study increases by an order of magnitude the number of genetic markers available for the study of westslope cutthroat trout and closely related taxa and includes many markers in genes (developed from ESTs).
Data from: Can physiographic regions substitute for genetically-determined conservation units? A case study with the threatened plant, Silene spaldingii
Protecting genetic diversity throughout the range of a species is important for conservation, as doing so provides for long-term evolutionary potential and persistence under a changing environment. Conservation of diversity at the intraspecific level requires identification of all genetically distinct population segments within species; i.e., conservation units (CUs). Silene spaldingii occurs in grasslands of the Columbia Plateau region of western North America and is listed as threatened under the Federal Endangered Species Act. The recovery plan identified five physiographic regions across the range of the species to use as surrogates for genetic CUs. We collected leaf samples from an average of 26 plants from each of 19 of the largest populations across all five physiographic regions and used variable microsatellite and chloroplast DNA markers to determine how genetic variation is distributed across the range of the species and how well physiographic regions reflect population structure within this species. Results of several multivariate analyses clustered our samples into four genetic groups which did not correspond well with the physiographic regions. We observed little genetic differentiation among populations in the main range of the species which encompasses nearly all of four contiguous physiographic regions. However, three other distinct genetic groups were identified: two in the disjunct northeast corner and one at the southeast edge of the main range. Modification of the CUs to reflect the genetic groups rather than the physiographic regions would result in CUs which better reflect historical patterns of population structure. Moreover, use of the genetic units to inform translocation and genetic rescue efforts could improve our ability to mimic natural patterns of gene flow. Our results suggest that physiographic regions may not always be an accurate reflection of population structure for threatened or endangered species.
Data from: Dendritic connectivity shapes spatial patterns of genetic diversity: a simulation-based study
Landscape features notoriously affect spatial patterns of biodiversity. For instance, in dendritic ecological networks (such as river basins), dendritic connectivity has been proposed to create unique spatial patterns of biodiversity. Here, we compared genetic datasets simulated under a lattice-like, a dendritic and a circular landscape to test the influence of dendritic connectivity on neutral genetic diversity. The circular landscape had a level of connectivity similar to that of the dendritic landscape, so as to isolate the influence of dendricity on genetic diversity. We found that genetic diversity and differentiation varied strikingly among the three landscapes. For instance, the dendritic landscape generated higher total number of alleles and higher global Fst than the lattice-like landscape, and these indices also varied between the dendritic and the circular landscape, suggesting an effect of dendricity. Furthermore, in the dendritic landscape, allelic richness was higher in highly connected demes (e.g. confluences in rivers) than in low connected demes (e.g. upstream and downstream populations), which was not the case in the circular landscape, hence confirming the major role of dendricity. This led to bell-shaped distributions of allelic richness along an upstream-downstream gradient. Conversely, genetic differentiation (Fst) was lower in highly than in low connected demes (which was not observed in circular landscape), and significant patterns of isolation-by-distance (IBD) were also observed in the dendritic landscape. We conclude that in dendritic networks, the combined influence of dendricity and connectivity generates unique spatial patterns of neutral genetic diversity, which has implications for population geneticists and conservationists.
Data from: Environmental stress correlates with increases in both genetic and residual variances: a meta-analysis of animal studies
Adaptive evolutionary responses are determined by the strength of selection and the amount of genetic variation within traits, however, both are known to vary across environmental conditions. As selection is generally expected to be strongest under stressful conditions, understanding how the expression of genetic variation changes across stressful and benign environmental conditions is crucial for predicting the rate of adaptive change. While theory generally predicts increased genetic variation under stress, previous syntheses of the field has found limited support for this notion. These studies have focused on heritability, which is dependent on other environmentally sensitive, but non-genetic, sources of variation. Here, we aim to complement these studies with a meta-analysis where we examine changes in coefficient of variation (CV) in maternal, genetic, and residual variances across stressful and benign conditions. Confirming previous analyses, we did not find any clear direction in how heritability changes across stressful and benign conditions. However, when analyzing CV, we found higher genetic and residual variance under highly stressful conditions in life-history traits but not in morphological traits. Our findings are of broad significance to contemporary evolution suggesting that rapid evolutionary adaptive response may be mediated by increased evolutionary potential in stressed populations.
Data from: Sex chromosome turnovers and genetic drift: a simulation study
The recent advances of new genomic technologies has enabled to identify and characterize sex chromosomes in an increasing number of non-model species, revealing that many plants and animals undergo frequent sex chromosome turnovers. What evolutionary forces drive these turnovers remains poorly understood, but it was recently proposed that drift might play a more important role than generally assumed. We analyzed the dynamics of different types of turnovers using individual-based simulations, and show that when mediated by genetic drift, turnovers are usually easier to achieve than substitutions at neutral markers, but that their dynamics and relative likelihoods vary with the type of the resident and emergent sex chromosome system (XY and/or ZW), and the dominance relationships among the sex-determining factors. Focusing on turnovers driven by epistatically dominant mutations, we find that drift-mediated turnovers that preserve the heterogamety pattern are 2-4x more likely than those along which the heterogametic sex changes. This ratio nevertheless decreases along with effective population size, and can even reverse in case of extreme polygyny. This can be attributed to a "drift-induced" selective force, known to influence transitions between male and female heterogamety, but which according to our study, does not affect turnovers that preserve the heterogametic sex.
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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.