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214 results for “quantitative traits”
Data from: Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations
Though epistasis has long been postulated to play a critical role in genetic regulation of important pathways as well as provide a major source of variation in the process of speciation, the importance of epistasis for genomic selection in the context of plant breeding is still being debated. In this paper, we report the results on the prediction of genetic values with epistatic effects for 280 accessions in the Nebraska Wheat Breeding Program using adaptive mixed LASSO. The development of adaptive mixed LASSO, originally designed for association mapping, for the context of genomic selection is reported. The results show that adaptive mixed LASSO can be successfully applied to the prediction of genetic values while incorporating both marker main effects and epistatic effects. Especially, the prediction accuracy is substantially improved by the inclusion of two-locus epistatic effects (more than one fold in some cases as measured by cross validation correlation coefficient), which is observed for multiple traits and planting locations. This points to significant potential in using non-additive genetic effects for genomic selection in crop breeding practices.
Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping
In multiple quantitative trait locus (QTL) mapping, a high-dimensional sparse regression model is usually employed to account for possible multiple linked QTLs. The QTL model may include closely linked and thus highly correlated genetic markers, especially when high-density marker maps are used in QTL mapping because of the advancement in sequencing technology. Although existing algorithms, such as Lasso, empirical Bayesian Lasso (EBlasso) and elastic net (EN) are available to infer such QTL models, more powerful methods are highly desirable to detect more QTLs in the presence of correlated QTLs. We developed a novel empirical Bayesian EN (EBEN) algorithm for multiple QTL mapping that inherits the efficiency of our previously developed EBlasso algorithm. Simulation results demonstrated that EBEN provided higher power of detection and almost the same false discovery rate compared with EN and EBlasso. Particularly, EBEN can identify correlated QTLs that the other two algorithms may fail to identify. When analyzing a real dataset, EBEN detected more effects than EN and EBlasso. EBEN provides a useful tool for inferring high-dimensional sparse model in multiple QTL mapping and other applications. An R software package 'EBEN' implementing the EBEN algorithm is available on the Comprehensive R Archive Network (CRAN).
Data from: Genetic mapping of three quantitative trait loci for soybean aphid resistance in PI 567324
Host-plant resistance is an effective method for controlling soybean aphid (Aphis glycines Matsumura), the most damaging insect pest of soybean (Glycine max (L.) Merr.) in North America. Recently, resistant soybean lines have been discovered and at least four aphid resistance genes (Rag1, Rag2, Rag3 and rag4) have been mapped on different soybean chromosomes. However, the evolution of new soybean aphid biotypes capable of defeating host-plant resistance conferred by most single genes demonstrates the need for finding germplasm with multigenic resistance to the aphid. This study was conducted to map quantitative trait loci (QTL) for aphid resistance in PI 567324. We identified two major QTL (QTL_13_1 and QTL_13_2) for aphid resistance on soybean chromosome 13 using 184 recombinant inbred lines from a 'Wyandot' × PI 567324 cross. QTL_13_1 was located close to the previously reported Rag2 gene locus, and QTL_13_2 was close to the rag4 locus. A minor QTL (QTL_6_1) was also detected on chromosome 6, where no gene for soybean aphid resistance has been reported so far. These results indicate that PI 567324 possesses oligogenic resistance to the soybean aphid. The molecular markers closely linked to the QTL reported here will be useful for development of cultivars with oligogenic resistance that are expected to provide broader and more durable resistance against soybean aphids compared with cultivars with monogenic resistance.
Gene Mapping for Quantitative Traits
ClinicalTrials.gov study NCT00005535. IPD Sharing: Not stated. Countries: 0. Publications: 6.
Genetic identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue
GEO Series GSE26106. Homo sapiens. 748 samples. Type: Expression profiling by array; SNP genotyping by SNP array; Genome variation profiling by SNP array.
Data from: Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster
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Data from: Lineage-specific mapping of quantitative trait loci
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Data from: Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations
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Data from: Sex chromosome linked genetic variance and the evolution of sexual dimorphism of quantitative traits
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Data from: Genetic architecture of quantitative flower and leaf traits in a pair of sympatric sister species of Primulina
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Data from: Quantitative trait loci for light sensitivity, body weight, body size, and morphological eye parameters in the bumblebee, Bombus terrestris
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Data from: Genetic mapping of three quantitative trait loci for soybean aphid resistance in PI 567324
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Data from: Mapping quantitative trait loci using selected breeding populations: a segregation distortion approach
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Data from: Empirical Bayesian elastic net for multiple quantitative trait locus mapping
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Mapping of Hepatic Expression Quantitative Trait Loci (eQTLs) in a Han Chinese Population
GEO Series GSE53792. Homo sapiens. 128 samples. Type: Genome variation profiling by SNP array; Expression profiling by array.
Quantitative trait loci mapped for TCF21 binding, chromatin accessibility and chromosomal looping in coronary artery smooth muscle cells reveal molecular mechanisms of coronary disease loci (Hi-C)
GEO Series GSE141749. Homo sapiens. 1 samples. Type: Other.
Systems genomics study reveals expression quantitative trait loci, regulator genes and pathways associated with boar taint in pigs (SNP60v2 BeadChip data set)
GEO Series GSE113318. Sus scrofa. 48 samples. Type: Genome variation profiling by SNP array.
Genome-wide identification of expression quantitative trait loci (eQTLs) in human heart
GEO Series GSE55232. Homo sapiens. 258 samples. Type: Expression profiling by array; SNP genotyping by SNP array.
Aortic Cellular Diversity and Quantitative GWAS Trait Prioritization through Single Nuclear RNA Sequencing (snRNA-Seq) of the Aneurysmal Human Aorta
GEO Series GSE207784. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Quantitative Trait Locus and Brain Expression of HLA-DPA1 And Shared Immune Alterations in Psychiatric Disorders
GEO Series GSE78246. Homo sapiens. 40 samples. Type: Expression profiling by array.
ScienceDex guides
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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.