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159 results for “genetic heterogeneity”
Data from: Using genetic relatedness to understand heterogeneous distributions of urban rat-associated pathogens
<p>Urban Norway rats (<i>Rattus norvegicus</i>) carry several pathogens transmissible to people. However, pathogen prevalence can vary across fine spatial scales (i.e., by city block). Using a population genomics approach, we sought to describe rat movement patterns across an urban landscape, and to evaluate whether these patterns align with pathogen distributions. We genotyped 605 rats from a single neighborhood in Vancouver, Canada and used 1,495 genome-wide single nucleotide polymorphisms to identify parent-offspring and sibling relationships using pedigree analysis. We resolved 1,246 pairs of relatives, of which only 1% of pairs were captured in different city blocks. Relatives were primarily caught within 33 meters of each other leading to a highly leptokurtic distribution of dispersal distances. Using binomial generalized linear mixed models we evaluated whether family relationships influenced rat pathogen status with the bacterial pathogens <i>Leptospira interrogans</i>, <i>Bartonella tribocorum</i>, and <i>Clostridium difficile</i>, and found that an individual's pathogen status was not predicted any better by including disease status of related rats. The spatial clustering of related rats and their pathogens lends support to the hypothesis that spatially restricted movement promotes the heterogeneous patterns of pathogen prevalence evidenced in this population. <span>Our findings also highlight the utility of evolutionary tools to understand movement and rat-associated health risks in urban landscapes.</span></p>
Figure 3 in Genetic and morphological heterogeneity within Eucyclops serrulatus (Fischer, 1851) (Crustacea: Copepoda: Cyclopidae)
Figure 3. Eucyclops serrulatus (Fischer) from the Dniester Liman (A, C, E) and Zakarpattia regions (B, D, F) of Ukraine. (A, B) P4, caudal side with feature abbreviations used; (C, D) antenna, caudal side; (E, F) caudal ramus with abbreviations.
FIGURE 2 in Population genetics of three threatened catfish species in heterogeneous environments of the Cauca River, Colombia
FIGURE 2 | Population structure suggested by STRUCTURE (A–C) and the Discriminant Analysis of the Principal Components (D–F) for Pimelodus grosskopfii (A, D), Sorubim cuspicaudus (B, E), and Ageneiosus pardalis (C, F).
FIGURE 1 in Population genetics of three threatened catfish species in heterogeneous environments of the Cauca River, Colombia
FIGURE 1 | Location of sampling sites of Pimelodus grosskopfii, Sorubim cuspicaudus, and Ageneiosus pardalis in the middle and lower sections of the Cauca River.
Data from: Influence of environmental heterogeneity on genetic diversity and structure in an endemic southern Californian oak
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Data from: Using genetic relatedness to understand heterogeneous distributions of urban rat-associated pathogens
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Heterogeneous genetic basis of age at maturity in salmonid fishes
<p>Understanding the genetic basis of repeated evolution of the same phenotype across taxa is a fundamental aim in evolutionary biology and has applications to conservation and management. However, the extent to which interspecific life-history trait polymorphisms share evolutionary pathways remains under-explored. We address this gap by studying the genetic basis of a key life-history trait, age at maturity, in four species of Pacific salmon (genus <i>Oncorhynchus</i>) that exhibit intra- and interspecific variation in this trait – Chinook Salmon, Coho Salmon, Sockeye Salmon, and Steelhead Trout. We tested for associations in all four species between age at maturity and two genome regions, <i>six6 </i>and <i>vgll3</i>, that are strongly associated with the same trait in Atlantic Salmon (<i>Salmo salar</i>). We also conducted a genome-wide association analysis in Steelhead to assess whether additional regions were associated with this trait. We found the genetic basis of age at maturity to be heterogeneous across salmonid species. Significant associations between <i>six6 </i>and age at maturity were observed in two of the four species, Sockeye and Steelhead, with the association in Steelhead being particularly strong in both sexes (p = 4.46x10<sup>-9</sup> after adjusting for genomic inflation). However, no significant associations were detected between age at maturity and the <i>vgll3 </i>genome region in any of the species, despite its strong association with the same trait in Atlantic Salmon. We discuss possible explanations for the heterogeneous nature of the genetic architecture of this key life-history trait, as well as the implications of our findings for conservation and management.</p>
Supporting datasets PubFig05 for: "Heterogeneous Ensemble Combination Search using Genetic Algorithm for Class Imbalanced Data Classification"
<p><strong>Faces Dataset: PubFig05</strong></p> <p>This is a subset of the ''PubFig83'' dataset [1] which provides 100 images each of 5 most difficult celebrities to recognise (referred as class in the classification problem). For each celebrity persons, we took 100 images and separated them into training and testing sets of 90 and 10 images, respectively:</p> <p><strong>Person: </strong>Jenifer Lopez; Katherine Heigl; Scarlett Johansson; Mariah Carey; Jessica Alba</p> <p> </p> <p><strong>Feature Extraction</strong></p> <p>To extract features from images, we have applied the HT-L3-model as described in [2] and obtained 25600 features.</p> <p><strong>Feature Selection</strong></p> <p>Details about feature selection followed in brief as follows:</p> <ol> <li> <p><strong>Entropy Filtering:</strong> First we apply an implementation of Fayyad and Irani's [3] entropy base heuristic to discretise the dataset and discarded features using the minimum description length (MDL) principle and only 4878 passed this entropy based filtering method.</p> </li> <li> <p><strong>Class-Distribution Balancing:</strong> Next, we have converted the dataset to binary-class problem by separating into 5 binary-class datasets using one-vs-all setup. Hence, these datasets became <em>imbalanced</em> at a ratio of 1:4. Then we converted them into <em>balanced binary-class</em> datasets using random sub-sampled method. Further processing of the dataset has been described in the paper.</p> </li> <li> <p><strong>(alpha,beta)-k Feature selection:</strong> To get a good feature set for training the classifier, we select the features using the approach based on the (alpha,beta)-k feature selection [4] problem. It selects a minimum subset of features that maximise both within class similarity and dissimilarity in different classes. We applied the entropy filtering and (alpha,beta)-k feature subset selection methods in three ways and obtained different numbers of features (in the Table below) after consolidating them into binary class dataset.</p> </li> </ol> <ul> <li> <p><strong>UAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>union</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>IAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets and we took the <em>intersection</em> of selected features for each binary-class datasets. Finally, we applied the (alpha,beta)-k feature set selection method on each of the binary-class datasets and get a set of features.</p> </li> <li> <p><strong>UEAB:</strong> We applied (alpha,beta)-k feature set method on each of the balanced binary-class datasets. Then, we applied the entropy filtering and (alpha,beta)-k feature set selection method on each of the balanced binary-class datasets. Finally, we took the <em>union</em> of selected features for each <em>balanced binary-class</em> datasets and get a set of features.</p> </li> </ul> <p>All of these datasets are inside the compressed folder. It also contains the document describing the process detail.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pinto, N., Stone, Z., Zickler, T., & Cox, D. (2011). Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on (pp. 35–42).</p> <p>[2] Cox, D., & Pinto, N. (2011). Beyond simple features: A large-scale feature search approach to unconstrained face recognition. In Automatic Face Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 8–15).</p> <p>[3] Fayyad, U. M., & Irani, K. B. (1993). Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In International Joint Conference on Artificial Intelligence (pp. 1022–1029).</p> <p>[4] Berretta, R., Mendes, A., & Moscato, P. (2005). Integer programming models and algorithms for molecular classification of cancer from microarray data. In Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 (pp. 361–370). 1082201: Australian Computer Society, Inc.</p> <p> </p>
Minimal dataset for the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank".
<p>Dataset "lin2024-sex_combined_interaction-association_signifincant_in_one_or_more_tests-summary.txt" is a minimal dataset to reproduce the figures and tables in the manuscript "Better together against genetic heterogeneity: a sex-combined joint main and interaction analysis of 290 quantitative traits in the UK Biobank". </p> <p><br>To generate this dataset, see "https://github.com/BoxiLin/t2meta" Steps 0, 1.</p> <p>This dataset is the input for Steps 2, 3, 4, 5 to generate Figures 1-3 and Table 2-3.</p> <p> </p> <p>##### Column information ########################</p> <p>The following columns are annotations on each variant in the GWAS, calculated across the analysis subset of 361,194 samples by the Neale lab:</p> <p>code: Phenotype identifier in the form of "[UKB Data field]_raw"<br>variant: Unique variant identifier in the form "chr:pos:ref:alt", where "ref" is aligned to the forward strand.<br>chr: Chromosome of the variant.<br>pos: Position of the variant in GRCh37 coordinates.<br>rsid: rs ID<br>ref: Reference allele on the forward strand.<br>alt: Alternate allele (not necessarily minor allele).<br>p_hwe: Hardy-Weinberg p-value.<br>info: Imputation INFO score as provided by UK Biobank.</p> <p> </p> <p>The following columns are sex-stratified test statistics calculated by the Neale lab:</p> <p>minor_allele.x: Minor allele (AF < 0.5) in the female GWAS <br>minor_AF.x: Minor allele frequency in the female GWAS <br>beta.x: Estimated effect size of alt allele in the female GWAS <br>se.x: Estimated standard error of beta in the female GWAS<br>tstat.x: t-statistic of beta estimate (= beta/se) in the female GWAS <br>pval.x: p-value of beta significance test in the female GWAS </p> <p>minor_allele.y: Minor allele (AF < 0.5) in the male GWAS <br>minor_AF.y: Minor allele frequency in the male GWAS <br>beta.y: Estimated effect size of alt allele in the male GWAS <br>se.y: Estimated standard error of beta in the male GWAS <br>tstat.y: t-statistic of beta estimate (= beta/se) in the male GWAS <br>pval.y: p-value of beta significance test in the male GWAS </p> <p> </p> <p><br>The following columns are sex-combined test statistics calculated in our analysis:</p> <p>T.I: test statsitic for interaction effect-only <br>p.T.I: p-value of the interaction effect-only test <br>TSG.L: test statsitic for inverse variance weighted meta-analysis<br>p.TSG.L: p-value of the inverse variance weighted meta-analysis<br>TSG.Q: test statsitic for the omnibus meta-analysis<br>p.TSG.Q: p-value for the omnibus meta-analysis</p>
Genetic divergence and local adaptation of Liriodendron driven by heterogeneous environments
<p><span>Ecological adaptive differentiation alters both the species diversity and intraspecific genetic diversity in forests, thus affecting the stability of forest ecosystems. Therefore, knowledge of the genetic underpinnings of the ecological adaptive differentiation of forest species is critical for effective species conservation. In this study, single-nucleotide polymorphisms (SNPs) from population transcriptomes were used to investigate the spatial distribution of genetic variation in <i>Liriodendron</i> to assess whether environmental variables can explain genetic divergence. We examined the contributions of environmental variables to population divergence and explored the genetic underpinnings of local adaptation using a landscape genomic approach. Niche models and statistical analyses showed significant niche divergence between <i>L. chinense</i> and <i>L. tulipifera</i>, suggesting that ecological adaptation may play a crucial role in driving interspecific divergence. We detected a new fine-scale genetic structure in <i>L. chinense</i>, and divergence of the six groups occurred during the late Pliocene to early Pleistocene. Redundancy analysis (RDA) revealed significant associations between genetic variation and multiple environmental variables. Environmental association analyses identified 67 environmental association loci (EALs; nonsynonymous SNPs) that underwent interspecific or intraspecific differentiation, 28 of which were associated with adaptive genes. These 28 candidate adaptive loci provide substantial evidence for local adaptation in <i>Liriodendron</i>. Our findings reveal ecological adaptive divergence pattern between <i>Liriodendron</i> species and provide novel insight into the role of heterogeneous environments in shaping genetic structure and driving local adaptation among populations, informing future <i>L. chinense</i> conservation efforts. </span></p>
Heterogeneous microgeographic genetic structure of the common cockle (Cerastoderma edule) in the Northeast Atlantic Ocean: biogeographic barriers and environmental factors
<p>Knowledge of genetic structure at the finest level is essential for conservation of genetic resources. Despite no visible barriers limiting gene flow, significant genetic structure has been shown in marine species. The common cockle (<em>Cerastoderma</em> <em>edule</em>) is a bivalve of great commercial and ecological value inhabiting the Northeast Atlantic Ocean. Previous population genomics studies demonstrated significant structure both across the Northeast Atlantic, but also within small geographic areas, highlighting the need to investigate fine-scale structuring. Here, we analysed two geographic areas that could represent opposite models of structure for the species: 1) the SW British Isles region, highly fragmented due to biogeographic barriers, and 2) Galicia (NW Spain), a putative homogeneous region. 9,250 SNPs genotyped by 2b-RAD on 599 individuals from 22 natural beds were used for the analysis. The entire SNP dataset mostly confirmed previous observations related to genetic diversity and differentiation, however, neutral and divergent SNP outlier datasets enabled disentangling physical barriers from abiotic environmental factors structuring both regions. While Galicia showed a homogeneous structure, the SW British Isles region was split into four reliable genetic regions related to oceanographic features and abiotic factors, such as sea surface salinity and temperature. The information gathered supports specific management policies of cockle resources in SW British and Galician regions also considering their particular socio-economic characteristics; further, these new data will be added to those recently reported in the Northeast Atlantic to define sustainable management actions across the whole distribution range of the species.</p>
Wind direction and strength determine the genetic structure of an insect-pollinated plant across heterogeneous landscape
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Genetic divergence and local adaptation of Liriodendron driven by heterogeneous environments
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Data from: Bee movement across heterogeneous tropical forests: multi-paternal genetic analyses reveal the importance of neighborhood composition for pollen-mediated gene flow
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Data from: Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance
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Data from: Variation in DNA methylation transmissibility, genetic heterogeneity and fecundity-related traits in natural populations of the perennial herb Helleborus foetidus
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Heterogeneous genetic basis of age at maturity in salmonid fishes
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Heterogeneous microgeographic genetic structure of the common cockle (Cerastoderma edule) in the Northeast Atlantic Ocean: biogeographic barriers and environmental factors
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Plant intraspecific functional trait variation is related to within-habitat heterogeneity and genetic diversity in Trifolium montanum L.
<p>Intraspecific trait variation (ITV), based on available genetic diversity, is one of the major means plant populations can respond to environmental variability. The study of functional trait variation and diversity has become popular in ecological research, e.g. as a proxy for plant performance influencing fitness. Up to now, it is unclear which aspects of intraspecific functional trait variation (iFDCV) can be attributed to the environment or genetics under natural conditions. Here, we examined 260 individuals from 13 locations of the rare (semi-)dry calcareous grassland species Trifolium montanum L. in terms of iFDCV, within-habitat heterogeneity, and genetic diversity. The iFDCV was assessed by measuring functional traits (releasing height, biomass, leaf area, specific leaf area, leaf dry matter content, Fv/Fm, performance index, stomatal pore surface, and stomatal pore area index). Abiotic within-habitat heterogeneity was derived from altitude, slope exposure, slope, leaf area index, soil depth, and further soil factors. Based on microsatellites, we calculated expected heterozygosity (He) because it best-explained, among other indices, iFDCV. We performed multiple linear regression models quantifying relationships among iFDCV, abiotic within-habitat heterogeneity and genetic diversity, and also between separate functional traits and abiotic within-habitat heterogeneity or genetic diversity. We found that abiotic within-habitat heterogeneity influenced iFDCV twice as strong compared to genetic diversity. Both aspects together explained 77% of variation in iFDCV (Radj² = 0.77, F2, 10 = 21.66, p < 0.001). The majority of functional traits (releasing height, biomass, specific leaf area, leaf dry matter content, Fv/Fm and performance index) were related to abiotic habitat conditions indicating responses to environmental heterogeneity. In contrast, morphology-related functional traits (releasing height, biomass and leaf area) were only related to genetics. Our results suggest that both within-habitat heterogeneity and genetic diversity affect iFDCV, and are thus crucial to consider when aiming to understand or predict changes of plant species performance under changing environmental conditions.</p>
Data from: Sun skink landscape genomics: assessing how microevolutionary processes shape genetic and phenotypic diversity across a heterogeneous and fragmented landscape
Incorporating genomic data sets into landscape genetic analyses allows for powerful insights into population genetics, explicitly geographical correlates of selection, and morphological diversification of organisms across the geographical template. Here, we utilize an integrative approach to examine gene flow and detect selection, and we relate these processes to genetic and phenotypic population differentiation across South-East Asia in the common sun skink, Eutropis multifasciata. We quantify the relative effects of geographic and ecological isolation in this system and find elevated genetic differentiation between populations from island archipelagos compared to those on the adjacent South-East Asian continent, which is consistent with expectations concerning landscape fragmentation in island archipelagos. We also identify a pattern of isolation by distance, but find no substantial effect of ecological/environmental variables on genetic differentiation. To assess whether morphological conservatism in skinks may result from stabilizing selection on morphological traits, we perform FST–PST comparisons, but observe that results are highly dependent on the method of comparison. Taken together, this work provides novel insights into the manner by which micro-evolutionary processes may impact macro-evolutionary scale biodiversity patterns across diverse landscapes, and provide genomewide confirmation of classic predictions from biogeographical and landscape ecological theory.
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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.