Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
214
datasets available to search
ShareScore release 0.9.0
Dataset results
214 results for “Quantitative traits”
Unlocking the grain quality enigma: A KASP-driven voyage through bread wheat's quantitative trait nucleotides under heat adversity
<p>Heat stress is a critical factor affecting global wheat production and productivity. In this study, out of 500 studied accessions a diverse panel of 126 wheat genotypes grown under twelve distinct environmental conditions was analyzed. Using 35K single-nucleotide polymorphism (SNP) genotyping assays and trait data on five biochemical parameters, including grain protein content (GPC), grain amylose content (GAC), grain total soluble sugars (TSS), grain iron (Fe), and zinc (Zn) content, six multi-locus GWAS models were employed for association analysis. This revealed 67 significantly associated QTNs linked to grain quality parameters, explaining phenotypic variations ranging from 3% to 44% under heat stress conditions. By considering the results in consensus to at least three GWAS models and three locations, the final QTNs were reduced to 17, with 14 being novel findings. Notably, two novel markers, AX-94461119 (chromosome 6A) and AX-95220192 (chromosome 7D), associated with grain iron and zinc, respectively, were validated through KASP approach. Candidate genes, such as chaperonin Cpn60/GroEL/TCP-1 family, P-loop containing nucleoside triphosphate hydrolases (NTPases), Bowman-Birk type proteinase inhibitor (BBI), and NPSN13 protein, were identified from the associated genomic regions, which could be potentially targeted for improving quality traits and heat tolerance in wheat.</p>
FIGURE 4 in Geographic variation in quantitative skull traits and systematic of southern populations of the leaf-eared mice of the Phyllotis xanthopygus complex (Cricetidae, Phyllotini) in southern South America
FIGURE 4. Dice-Leraas diagrams elaborated with the geometric mean (a) and individual scores of the first principal component (b) over selected localities of the geographical groups busw, rnsc, chnc, chew, chce, chse, scnw, scne, and scwc in a NE-SW transect. The black spots represent the means; the gray area represent the 95% confidence intervals and the bars the range. For the acronyms, see Materials and Methods section.
FIGURE 3 in Geographic variation in quantitative skull traits and systematic of southern populations of the leaf-eared mice of the Phyllotis xanthopygus complex (Cricetidae, Phyllotini) in southern South America
FIGURE 3. Specimen scores of adult individuals (ages 3–5) of Phyllotis (N = 336) for: a) Shape variable principal components 1 and 2 (light green = busw; dark green = coce and slno; orange = menw, mewe and nqne; blue = mesw, nqnw, nqso; red = chce, chnc, chse, chsw, chew, rnsc, scne, scnw, scwc); b) Shape variable canonical variates 1 and 2 extracted from 18-group discriminant function analysis based on samples grouped by its geographical origin (colors as in A); c) Shape variable principal components 1 and 2 (light green = bona; dark green = cent; yellow = nort; blue = west; red = sout); and ds) Shape variable canonical variates 1 and 2 extracted from 5-group discriminant function analysis based on samples grouped by its membership to mitochondrial lineages (colors as in C). For the acronyms, see Materials and Methods section.
FIGURE 2 in Geographic variation in quantitative skull traits and systematic of southern populations of the leaf-eared mice of the Phyllotis xanthopygus complex (Cricetidae, Phyllotini) in southern South America
FIGURE 2. Specimen scores of adult individuals (ages 3–5) of Phyllotis (N = 336) for: a) Size dependent principal components 1 and 2 (light green = busw; dark green = coce and slno; orange = menw, mewe and nqne; blue = mesw, nqnw, nqso; red = chce, chnc, chsc, chse, chsw, chew, rnsc, scne, scnw, scwc); b) Size dependent canonical variates 1 and 2 extracted from 18-group discriminant function analysis based on samples grouped by its geographical origin (colors as in A); c) Size dependent principal components 1 and 2 (light green = bona; dark green = cent; yellow = nort; blue = west; red = sout); and d) Size dependent canonical variates 1 and 2 extracted from 5-group discriminant function analysis based on samples grouped by its membership to mitochondrial lineages (colors as in C). For the acronyms, see Materials and Methods section.
FIGURE 1. a in Geographic variation in quantitative skull traits and systematic of southern populations of the leaf-eared mice of the Phyllotis xanthopygus complex (Cricetidae, Phyllotini) in southern South America
FIGURE 1. a) Map of southern South America indicating the placement of the collection localities of the specimens of Phyllotis studied in this work (see Appendix 1 for a detail); type localities of nominal forms discussed in the text were indicated by red stars. b) Geographical groups recognized in this study; different colors correspond to the mitochondrial lineages documented by Riverón (2011) as follow: light green = bona (P. bonariensis); dark green = central Argentina; orange = northcentral Argentina; blue = west-central Argentina; red = southern Argentina and Chile. For the acronyms, see Materials and Methods section. Specimens from busw correspond to P. bonariensis, while the remaining samples could be referred to the current concept of P. xanthopygus; individuals from coce, menw, mese, meso, mesw, nqne, nqnw and slno are usually included under P. x. vaccarum, while those of chnc, chwe, chce, chse, chsw, chsc, nqso, rnce, rnsc, scne, scwc, and scnw are traditionally assigned to P. x. xanthopygus.
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
Data from: Admixture mapping of quantitative traits in Populus hybrid zones: power and limitations
Uncovering the genetic architecture of species differences is of central importance for understanding the origin and maintenance of biological diversity. Admixture mapping can be used to identify the number and effect sizes of genes that contribute to the divergence of ecologically important traits, even in taxa that are not amenable to laboratory crosses due to their long generation time or other limitations. Here, we apply admixture mapping to naturally occurring hybrids between two ecologically divergent Populus species. We map quantitative trait loci (QTL) for eight leaf morphological traits using 77 mapped microsatellite markers from all 19 chromosomes of Populus. We apply multivariate linear regression analysis allowing the modeling of additive and non-additive gene action and identify several candidate genomic regions associated with leaf morphology using an information-theoretic approach. We perform simulation studies to assess the power and limitations of admixture mapping of quantitative traits in natural hybrid populations for a variety of genetic architectures and modes of gene action. Our results indicate that (1) admixture mapping has considerable power to identify the genetic architecture of species differences if sample sizes and marker densities are sufficiently high, (2) modeling of non-additive gene action can help to elucidate the discrepancy between genotype and phenotype sometimes seen in interspecific hybrids, and (3) the genetic architecture of leaf morphological traits in the studied Populus species involves complementary and overdominant gene action, providing the basis for rapid adaptation of these ecologically important forest trees.
Germline Immunomodulatory Expression Quantitative Trait Loci (ieQTLs) Associated with Immune-Related Toxicity from Checkpoint Inhibition
<p>Robert Ferguson<sup>1,2,3*</sup>, Vylyny Chat<sup>1,2,3*</sup>, Leah Morales<sup>1,2,3</sup>, Danny Simpson<sup>1,2,3</sup>, Kelsey Monson<sup>1,2,3</sup>, Elisheva Cohen<sup>1,2,3</sup>,Sarah Zusin<sup>1,2,3</sup>, Gabriele Madonna<sup>4</sup>, Mariaelena Capone<sup>4</sup>, Ester Simeone<sup>4</sup>, Anna Pavlick<sup>5</sup>, Jason Luke<sup>6,7</sup>, Thomas F Gajewski<sup>8,9,10</sup>, Iman Osman<sup>1,3,11,12</sup>, Paolo Antonio Ascierto<sup>4</sup>, Jeffrey Weber<sup>1,3,11</sup>, Tomas Kirchhoff<sup>1,2,3</sup></p> <p>1Laura and Isaac Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA<br> 2Departments of Population Health and Environmental Medicine, New York University- Grossman School of Medicine, New York, NY, USA<br> 3The Interdisciplinary Melanoma Cooperative Group, New York University-Grossman School of Medicine, New York, NY, USA<br> 4Melanoma Cancer Immunotherapy and Innovative Therapy Unit, Istituto Nazionale Tumori IRCCS Fondazione G. Pascale, Napoli, Italy<br> 5Division of Hematology & Medical Oncology, the Cutaneous Oncology Program, Weill Cornell Medicine and New York-Presbyterian, New York, USA<br> 6Department of Immunology, University of Pittsburgh, Pittsburgh, PA 15213, USA.<br> 7UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA.<br> 8Department of Pathology, University of Chicago, Chicago, IL, USA.</p> <p>9Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA.<br> 10Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA.<br> 11Department of Medicine, New York University-Grossman School of Medicine, New York, NY, USA<br> 12Ronald O. Perelman Department of Dermatology, New York University-Grossman School of Medicine, New York, NY, USA</p> <p>*These authors contributed equally to the work</p> <p>Corresponding author: Tomas Kirchhoff, PhD</p> <p><strong>ABSTRACT</strong><br> <strong>Background:</strong> Immune-checkpoint inhibition (ICI) has improved clinical outcomes for metastatic melanoma patients, however, 65-80% of patients treated with ICI experience immune-related adverse events (irAEs). Given the plausible link of irAEs with underlying host immunity, we explored if germline genetic variants controlling the expression of 42 immunomodulatory genes<br> were associated with the risk of irAEs in melanoma patients treated with the single-agent anti- CTLA-4 antibody ipilimumab (IPI).<br> <strong>Methods:</strong> We identified 42 immunomodulatory expression quantitative trait loci (ieQTLs) most significantly associated with the expression of 382 immune-related genes. These germline variants were genotyped in IPI-treated melanoma patients, collected as part of a multiinstitutional collaboration. We tested the association of ieQTLs with irAEs in a discovery cohort of 95 patients followed by validation in an additional 97 patients.<br> Results: We found that the alternate allele of rs7036417, a variant linked to increased expression of SYK, was strongly associated with an increased risk of grade 3-4 toxicity (OR= 7.46; 95% CI=2.65-21.03; p=1.43E-04). This variant was not associated with response (OR= 0.90; 95% CI=0.37-2.21; p=0.82).<br> Conclusion: We report that rs7036417 associates with increased risk of severe irAEs, independent of IPI efficacy. SYK plays an important role in B-cell/T-cell expansion and increased pSYK has been reported in patients with autoimmune disease. The association between rs7036417 and IPI irAEs in our data suggests a role of SYK over-expression in irAE development. These findings support the hypothesis that inherited variation in immune-related pathways modulate ICI toxicity and suggest SYK as a possible future target for therapies to reduce irAEs.</p> <p> </p> <p><strong>Keywords:</strong> irAEs; germline variants; immune checkpoint inhibition; melanoma</p> <p><strong>Funding:</strong> This research was funded by the Italian Ministry of Health (IT-MOH) through “Ricerca Corrente”, grants number M2/2 and L2-1.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
A Genome-Wide Scan For Quantitative Trait Loci of Serum Bilirubin - A Framingham Study
ClinicalTrials.gov study NCT00340509. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Genetic variation in HIF signaling underlies quantitative variation in physiological and life history traits within lowland butterfly populations
Open the record for dataset details and reuse information.
Data from: Admixture mapping of quantitative traits in Populus hybrid zones: power and limitations
Open the record for dataset details and reuse information.
Quantitative trait locus analysis of parasitoid counteradaptation to symbiont-conferred resistance
Open the record for dataset details and reuse information.
Genetic diversity among black cumin (<em>Nigella sativa</em> L.) accessions based on quantitative and qualitative traits
Open the record for dataset details and reuse information.
Data from: Widespread cumulative influence of small effect size mutations on yeast quantitative traits
Open the record for dataset details and reuse information.
Data from: Limits to behavioral evolution: the quantitative genetics of a complex trait under directional selection
Open the record for dataset details and reuse information.
Data from: Quantitative genetic architecture at latitudinal range boundaries: reduced variation but higher trait independence
Open the record for dataset details and reuse information.
Unlocking the grain quality enigma: A KASP-driven voyage through bread wheat's quantitative trait nucleotides under heat adversity
Open the record for dataset details and reuse information.
Data from: Evolutionary dynamics of quantitative variation in an adaptive trait at the regional scale: the case of zinc hyperaccumulation in Arabidopsis halleri
Open the record for dataset details and reuse information.
Data from: Replicated analysis of the genetic architecture of quantitative traits in two wild great tit populations
Open the record for dataset details and reuse information.
Data from: The quantitative genetics of incipient speciation: heritability and genetic correlations of skeletal traits in populations of diverging Favia fragum ecomorphs.
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.