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359 results for “trait associations”
Genotyping-by-sequencing (GBS) dataset for genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))
<p>These vcf-files constitute underlying raw data material for the manuscript "Genome wide associations of growth, phenology and plasticity traits in willow (Salix viminalis (L.))". For more detailed information please consult the README file in the repository.</p>
Aligned bam files for "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits"
<p>Aligned bam files used for analysis in "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits".</p> <p>This is an accompanying dataset to Datasets and code for "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits" (https://zenodo.org/record/7442105).</p>
Supplementary data: Agro-morphological and molecular characterization reveal deep insights in promising genetic diversity and marker-trait associations in Fagopyrum esculentum and F. tataricum
<p>Our study focuses on the global/European buckwheat germplasm collected as part of the ECOBREDD project. The potential of this highly diverse collection for organic buckwheat breeding was evaluated at two complementary levels: phenotypic and genetic. Here, we characterized the phenotypic and genetic diversity of a global collection of the two cultivated buckwheat species <em>Fagopyrum esculentum</em> and <em>F. tataricum</em> (190 and 51 accessions, respectively) using 37 agro-morphological traits and 24 SSR markers (Simple Sequence Repeats) (see publication and info sheet of the data).</p>
Periphyton Abundance and Structural Traits, Diatom Taxa Relative Abundance, and Associated Environmental Data from Samples Collected from the Greater Everglades, Florida, USA from September 2005 - ongoing
This data package contains benthic algae (periphyton) and environmental data collected annually during the wet season between 2005 and 2021 from sites distributed throughout the greater Everglades ecosystem. This project is part of the Comprehensive Everglades Restoration Program's Monitoring and Assessment Plan (CERP MAP) intended to document baseline variability in periphyton attributes for assessing the effectiveness of restoration projects. A total of 200 primary sampling units (PSU) of 800 m x 800 m are nested in 32 landscape units (LSU) and each year, random coordinates are 'drawn' within each PSU and one draw is visited in each sampleable PSU. Sampled periphyton is processed for aggregate structural traits (i.e., biomass, chlorophyll-a, organic content, and phosphorus concentration) and for diatom taxa. For diatoms, slides are prepared, and at least 500 frustules are enumerated and identified to the lowest possible taxonomic resolution per slide. Taxon abundances are then relativized to the total count. These data accompany environmental and spatial data for each sampled draw. In addition to the CERP MAP data, this dataset also includes data on the same variables collected from up to 21 primary sampling units in the Broward County Water Preserve Area beginning in 2020. The data in this package replace and supersede those in package knb-lter-fce.1210 (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-fce&identifier=1210).
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits
<p>Genetic association studies have identified hundreds of independent genetic signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a relevant T2D tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million variants. We anticipate that deep learning methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.</p>
Data for Linkage mapping of root shape traits associated with market class in two biparental carrot populations
<p> </p> <p>This repository contains essential data to support the findings presented in the forthcoming publication titled "Linkage Mapping of Root Shape Traits Associated with Market Class in Two Biparental Carrot Populations." It includes VCF files for two distinct carrot biparental populations, as well as R code for filtering, constructing linkage maps, and conducting QTL analysis. Furthermore, the repository hosts phenotypic data gathered from these two biparental populations during the years 2020 and 2021.</p> <p>Two carrot genetic maps, one for each population, have been made available alongside their respective phenotypic data.</p> <p>The provided R code contains absolute working directory paths that may not function as intended on your system. The primary purpose of sharing this code is to offer readers insight into the techniques employed in this study. You may need to adapt the directory paths to suit your specific setup. </p> <p>To assist readers in understanding the logical sequence of steps involved in our linkage mapping project, the R code scripts have been sequentially numbered from 0 to 10.</p> <p>For more info contact: vegaalfaro@wisc.edu.</p>
Fungal traits associated with soil organic matter formation, Harvard Forest, Petersham MA, 2020-2023
Soil microbes are a major source of organic residues that accumulate as soil organic matter (SOM), the largest terrestrial reservoir of carbon on Earth. As such, there is growing interest in determining the microbial traits that drive SOM formation and stabilization; however, whether certain microbial traits consistently predict SOM accumulation across different functional pools (e.g., total vs. stable SOM) is unresolved. To address these uncertainties, we incubated individual species of fungi in SOM-free model soils, allowing us to directly relate the physiological, morphological, and biochemical traits of fungi to their SOM formation potentials. We find that the formation of different SOM functional pools is associated with distinct fungal traits, and that ‘multifunctional’ species with intermediate investment across this key grouping of traits (namely, carbon use efficiency, growth rate, turnover rate, and biomass protein and phenol contents) promote SOM formation, functional complexity, and stability. Our results highlight the limitations of categorical trait-based frameworks that describe binary (high/low) trade-offs between microbial traits, instead emphasizing the importance of synergies among microbial traits for the formation of functionally complex SOM.
Fig. 4 in Kidney anatomy, histology and histometric traits associated to renosomatic index in Gymnotus inaequilabiatus (Gymnotiformes: Gymnotidae)
Fig. 4. Histological cross-sections of the head and exocrine kidney demonstrating different granulomatous structures in Gymnotus inaequilabiatus. a. severe aggregation of MMCs outer to granuloma in the exocrine kidney; PAS, bar scale = 20 µm. b. a clump of melanogenic macrophages (dark pigmented cells) adjacent to granuloma in the head kidney. The hematopoietic tissue is edematous and atypical. Some rodlet cells (black arrow) are observed. Granuloma presents a compact and thick layer of collagen with internal necrotic content surrounded by epithelioid cells (white arrows); H&E, bar scale = 10 µm. c. two well-defined granulomas with substantial internal necrotic content involved by fibrous tissue. A slight layer of MMCs aggregates close to the external wall of granuloma. TM, bar scale = 10 µm. d. MMCs aggregates both internally and externally to granuloma in the exocrine kidney. HE, bar scale = 10 µm.
Fig. 2 in Kidney anatomy, histology and histometric traits associated to renosomatic index in Gymnotus inaequilabiatus (Gymnotiformes: Gymnotidae)
Fig. 2. Cross-section of the head and exocrine kidney in Gymnotus inaequilabiatus. a. a transitional area between head (HE) and exocrine kidney (EX) drained by postcardinal vein (PCV). Lymphohematopoietic tissue is abundant in the head portion. MMCs are diffusely distributed in both portions. A thick fibrous capsule covers the organ (white arrow); HE, bar scale = 500 µm. b. the general aspect of the head kidney. Vascular sinusoids (white arrows) surrounding the lymphohematopoietic tissue enclosing the MMCs (black arrow); HE, bar scale = 200 µm. c. interstitial lymphohematopoietic tissue with MMCs deposits characterizes the exocrine kidney, in addition, the contoured tubules; renal corpuscle (white arrow); HE, bar scale = 200 µm. d. the exocrine kidney with a proximal convoluted tubule showing hyaline degeneration of the tubular epithelium often found in this species; HE, bar scale = 100 µm. e. exocrine kidney showing the corpuscle of Stannius (*). This structure is not lobulated, and is delimited by a fibrous capsule, and situated at the junction between head and exocrine portions; HE, bar scale = 500 µm. f. detail of junction between lymphohematopoietic tissue and corpuscle of Stannius in the head kidney. Fiber bundles subdivide both structures. Rodlet cells (white arrow), macrophages (M) and eosinophilic granulocytic cells (black arrow); granulocytic (G) and secretory cells (SC) internally fill the corpuscle; HE, bar scale = 50 µm.
Data for Are Changes in Alcohol Use and Personality Traits associated? A Cohort Study among Young Swiss Men
<p>These are the data and metadata for the article </p> <p><strong>Are Changes in Alcohol Use and Personality Traits associated? A Cohort Study among Young Swiss Men</strong></p> <p>by </p> <p><strong>Gerhard Gmel, Simon Marmet, Joseph Studer, and Matthias Wicki</strong></p> <p><strong>to be published in Frontiers of Psychiatry</strong></p>
Differential associations between nucleotide polymorphisms and physiological traits in Norway spruce (Picea abies Karst.) provenances under contrasting water regimes
<p>Three datasets are provided here, yielded by a study on drought-stressed and control (well-watered) seedlings of Norway spruce (Picea abies Karst.), coming from 5 provenances distributed along a steep altitudinal gradient from 550 to 1,280 m a.s.l. in central Slovakia:</p> <p>1. physiological traits</p> <p>2. double-digest restriction-site associated sequencing data (ddRAD)</p> <p>3. nuclear microsatellite (nSSR) genotypes</p>
Data from: Association genetics of growth and adaptive traits in loblolly pine (Pinus taeda L.) using whole-exome-discovered polymorphisms
In the United States, forest genetics research began over 100 years ago and loblolly pine breeding programs were established in the 1950s. However, the genetics underlying complex traits of loblolly pine remains to be discovered. To address this, adaptive and growth traits were measured and analyzed in a clonally tested loblolly pine (Pinus taeda L.) population. Over 2.8 million single nucleotide polymorphism (SNP) markers detected from exome sequencing were used to test for single locus associations, SNP-SNP interactions and correlation of individual heterozygosity with phenotypic traits. A total of 36 SNP-trait associations were found for specific leaf area (5 SNPs), branch angle (2), crown width (3), stem diameter (4), total height (9), carbon isotope discrimination (4), nitrogen concentration (2), and pitch canker resistance traits (7). Eleven SNP-SNP interactions were found to be associated with branch angle (1 SNP-SNP interaction), crown width (2), total height (2), carbon isotope discrimination (2), nitrogen concentration (1), and pitch canker resistance (3). Non-additive effects imposed by dominance and epistasis account for a large fraction of the genetic variance for the quantitative traits. Genes that contain the identified SNPs have a wide spectrum of functions. Individual heterozygosity positively correlated with water use efficiency and nitrogen concentration. In conclusion, multiple effects identified in this study influence the performance of loblolly pines, provide resources for understanding the genetic control of complex traits, and have potential value for assessing with breeding through marker assisted selection and genomic selection.
Growth traits of a tropical timber species at Southeast Asia, Shorea macrophylla, and scripts for genome wide association study and genomic prediction
<p><em><span>Shorea macrophylla</span></em><span> is a commercially important tropical tree species grown for timber and oil. It is amenable to plantation forestry due to its fast initial growth. Genomic selection (GS) has been used in tree breeding studies to shorten long breeding cycles but has not previously been applied to <em>S. macrophylla</em>. To build genomic prediction models for GS, leaves and growth trait data were collected from a half-sib progeny population of <em>S. macrophylla</em> in Sari Bumi Kusuma forest concession, central Kalimantan, Indonesia. 18037 SNP markers were identified in two ddRAD-seq libraries. Genomic prediction models based on these SNPs were then generated for breast height and total height in the 7th year from planting (D7 and H7). These traits were chosen because of their relatively high narrow-sense genomic heritability and because seven years was considered long enough to assess initial growth. Genomic prediction models were built using 12 methods with the full set of identified SNPs and subsets of 48, 96, and 192 SNPs selected based on the results of a genome-wide association study (GWAS). The GBLUP and RKHS methods gave the highest predictive ability (PA) for D7 and H7 and showed that D7 has an additive genetic architecture while H7 has an epistatic genetic architecture. LightGBM and CNN1D also achieved high PA for D7 with 48 and 96 selected SNPs, and for H7 with 96 and 192 selected SNPs, showing that gradient boosting decision trees and deep learning can be useful in genomic prediction. For almost all methods and both traits, PA was higher when SNPs were selected based on their GWAS P-values than when using the full set of SNPs. These results suggest that GS with GWAS-based SNP selection could be used in <em>S. macrophylla </em>breeding to improve initial growth and reduce genotyping costs for next generation seedlings.</span></p>
Data from: The genetic basis of traits associated with the evolution of serpentine endemism in monkeyflowers
<p>The floras on chemically and physically challenging soils, such as gypsum, shale, and serpentine, are characterized by narrowly endemic species. The evolution of edaphic endemics may be facilitated or constrained by genetic correlations among traits contributing to adaptation and reproductive isolation across soil boundaries. The yellow monkeyflowers in the <em>Mimulus guttatus</em> species complex are an ideal system in which to examine these evolutionary patterns. To determine the genetic basis of adaptive and prezygotic isolating traits, we performed genetic mapping experiments with F2 hybrids derived from a cross between a serpentine endemic, <em>M. nudatus</em>, and its close relative <em>M. guttatus</em>. Few large effect and many small effect QTL contribute to interspecific divergence in life history, floral and leaf traits, and a history of directional selection contributed to trait divergence. Loci contributing to adaptive traits and prezygotic reproductive isolation overlap, and their allelic effects are largely in the direction of species divergence. These loci contain promising candidate genes regulating flowering time and plant organ size. Together our results suggest that genetic correlations among traits can facilitate the evolution of adaptation and speciation and may be a common feature of the genetic architecture of divergence between edaphic endemics and their widespread relatives.</p>
Genetic Architecture Reconciles Linkage and Association Studies of Complex Traits
<p>This (zipped) folder contains 3 sub-folders:</p> <p>#**********************************************************************************************************<br>The "bin" folder contains fuctions and gentic maps needed for analyes<br>bin \<br> predLink.R - function to predict linkage <br> sibREML_v0.1.1.R - function to run SibREML<br> sim-sib-array.R - script to simulate sib-pairs from parental haplotypes<br> Summarised_genetic_map_bcf.txt - genetic map per 0.5-cM long segments, based on map from bcftools <br> (BCFtools: https://samtools.github.io/bcftools/bcftools.html)<br> Summarised_genetic_map_OMNI.txt - genetic map per 0.5-cM long segments, based on OMNI map <br> (https://github.com/joepickrell/1000-genomes-genetic-maps/tree/master/interpolated_OMNI)<br>#**********************************************************************************************************</p> <p> </p> <p>#**********************************************************************************************************<br>The "SIM" folder contains the simulation pipeline (scripts 01-15) as well as IBD sharing and simulated phenotypes for Simulated sib-pairs.<br>SIM \<br> 01_sim-sib-array.sh *pre-run*<br> 02_bed_recode_bcf_map.sh *pre-run*<br> 03_make_merlin.R *pre-run*<br> 04_error_merlin.sh *pre-run*<br> 05_merlin_IBD.sh *pre-run*<br> 06_sample_causal_snps.R *pre-run*<br> 07_simulate_pheno.sh *pre-run*<br> 08_bhat_gwas.R *can be run using provided data* <br> 09_Linkage_VH.R *can be run using provided data* <br> 10_predLink.R *can be run using provided data*<br> 11_phi_hat.R *can be run using provided data*<br> 12_IBD_Mb.R *can be run using provided data*<br> 13_IBD_cM_recombrate_stratified.R *can be run using provided data*<br> 14_SibREML.R *can be run using provided data*<br> 15_SibREML_stratified_Q4.R *can be run using provided data*<br> causal_snps \ *provided causal SNPs*<br> IBD_results \ *provided IBD-probabilities for 1000 simulated sib-pairs*<br> Linkage_VH_results \ <br> pheno \ *provided simulated phenotypes (h2=1) for 8 genetic architectures*<br> Phi_hat_results.txt<br> predicted \<br> README<br> SibREML_results.txt<br> SibREML_stratified_Q4.txt</p> <p>The data can be used to run Linkage analysis, predict linkage, estimate phi_hat, <br>as well as estimate non-stratified and recombination rate stratified sib-heritability (h2_FS and c).<br>The README is provided within the folder. <br>#**********************************************************************************************************</p> <p> </p> <p>#**********************************************************************************************************<br>The "HT_BMI" folder contains data and scripts to predict linkage and estimate phi_hat for height and BMI.<br>HT_BMI \<br> 01_predLink_HT_BMI.R<br> 02_phi_hat_HT_BMI.R<br> gws_sumstats \ *provided summary GWAS summary statistics to predict linkage for height and BMI*<br> Linkage_results \ *provided linkage meta-analysis results for height and BMI from this study*<br> Phi_hat_results_HT_BMI.txt<br> PREDLINK_bmi.txt<br> PREDLINK_height.txt<br> README<br>The README is provided within the folder.<br>#**********************************************************************************************************</p> <p><strong> </strong></p>
Code and data for "Species traits associated with rapid shifts in elevational distributions of Swiss birds"
<p><strong>Abstract</strong><br> Global change in climate and land use have profound effects on species’ geographic and elevational distributions. In European birds, while species are predicted to track their climatic niches upslope, lowland agricultural intensification and high elevation land abandonment can drive elevational shifts. Species traits that can predict response to change in climate and land use can inform conservation, but thorough examination of their relationships with elevational shifts in European birds are lacking. We estimate change in the elevational distributions of 71 species from 1996 to 2016 in a region of the western Palearctic with wide elevational gradients (approximately 3,000 m) and large changes in temperature. We model the relationships between elevational shifts and species traits associated with resource preference and adaptive capacity at five reference points including the cool edge, warm edge, and the core of species’ elevational distributions. When intermediate reference points were removed changes to the results were negligible, indicating that three reference points are likely sufficient. We found significant upslope and downslope shifts in 56% and 23% of our study species, respectively. Asymmetric rates of shifts in the cool and warm edges caused significant contractions in elevational extent in 30% of our study species. The effect of elevational preference (i.e. midpoint elevation) was habitat dependent. Movement in alpine birds was unidirectionally upslope, with nearly half displaying significant or apparent elevational range contractions. In woodland birds, asymmetries of shifts in reference points led to expansions in extent in low elevation species and contractions in high elevation species. Generally, migrants, species with smaller mass, smaller relative brain size, smaller hand-wing index, and generalists in diet, habitat, and elevation had greater upslope shifts. While elevational shifts in European birds were heterogenous and species-specific, many were rapid, and species traits associated with resource preference and adaptive capacity were associated with common patterns of elevation.</p>
Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)
<p>This vcf file constitute underlying raw data material for the manuscript "Determination of traits responding to iron toxicity stress at different stages and genome-wide association analysis for iron toxicity tolerance in rice (Oryza sativa L.)". <br> The SNP genotype data came from a whole-genome resequencing and were called using the Nipponbare IRGSP 1.0 rice reference genome. SNPs with a miss rate greater than 30% and minor allele frequency (MAF) less than 5% were removed. Heterozygous alleles were also excluded. Finally, 160,498 SNPs were selected and used in the GWAS analysis. </p>
Genome-wide association implicates numerous genes underlying ecological trait variation in natural populations of Populus trichocarpa
In order to uncover the genetic basis of phenotypic trait variation, we used 448 unrelated wild accessions of black cottonwood (Populus trichocarpa) from much of its range in western North America. Extensive data from large-scale trait phenotyping (with spatial and temporal replications within a common garden) and genotyping (with a 34 K Populus single nucleotide polymorphism (SNP) array) of all accessions were used for gene discovery in a genome-wide association study (GWAS). We performed GWAS with 40 biomass, ecophysiology and phenology traits and 29 355 filtered SNPs representing 3518 genes. The association analyses were carried out using a Unified Mixed Model accounting for population structure effects among accessions. We uncovered 410 significant SNPs using a Bonferroni-corrected threshold (P < 1.7 × 10−6). Markers were found across 19 chromosomes, explained 1–13% of trait variation, and implicated 275 unique genes in trait associations. Phenology had the largest number of associated genes (240 genes), followed by biomass (53 genes) and ecophysiology traits (25 genes). The GWAS results propose numerous loci for further investigation. Many traits had significant associations with multiple genes, underscoring their genetic complexity. Genes were also identified with multiple trait associations within and/or across trait categories. In some cases, traits were genetically correlated while in others they were not.
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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)
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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.