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5 results for “Marker assisted selection”
Data from: Relatedness severely impacts accuracy of marker- assisted selection for disease resistance in hybrid wheat
The accuracy of genomic selection depends on the relatedness between the members of the set in which marker effects are estimated based on evaluation data and the types for which performance is predicted. Here, we investigate the impact of relatedness on the performance of marker-assisted selection for fungal disease resistance in hybrid wheat. A large and diverse mapping population of 1,739 elite European winter wheat inbred lines and hybrids was evaluated for powdery mildew, leaf rust, and stripe rust resistance in multi-location field trials and fingerprinted with 9k and 90k SNP arrays. Comparison of the accuracies of prediction achieved with data sets from the two marker arrays revealed a crucial role for a sufficiently high marker density in genome-wide association mapping. Cross- validation studies using test sets with varying degrees of relationship to the corresponding estimation sets unraveled that close relatedness leads to a substantial increase in the proportion of total genotypic variance explained by the identified QTL and, consequently, to an overoptimistic judgment of the prospected precision of marker-assisted selection.
Marker Assisted Selective ThErapy in Rare Cancers: Knowledge Database Establishing registrY Asia
ClinicalTrials.gov study NCT05217407. IPD Sharing: NO. Countries: 7. Publications: 1.
Data from: Relatedness severely impacts accuracy of marker- assisted selection for disease resistance in hybrid wheat
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Data from: Whole genome sequencing of elite rice cultivars as a comprehensive information resource for marker assisted selection
Current advances in sequencing technologies and bioinformatics revealed the genomic background of rice, a staple food for the poor people, and provided the basis to develop large genomic variation databases for thousands of cultivars. Proper analysis of this massive resource is expected to give novel insights into the structure, function, and evolution of the rice genome, and to aid the development of rice varieties through marker assisted selection or genomic selection. In this work we present sequencing and bioinformatics analyses of 104 rice varieties belonging to the major subspecies of Oryza sativa. We identified repetitive elements and recurrent copy number variation covering about 200 Mbp of the rice genome. Genotyping of over 18 million polymorphic locations within O. sativa allowed us to reconstruct the individual haplotype patterns shaping the genomic background of elite varieties used by farmers throughout the Americas. Based on a reconstruction of the alleles for the gene GBSSI, we could identify novel genetic markers for selection of varieties with high amylose content. We expect that both the analysis methods and the genomic information described here would be of great use for the rice research community and for other groups carrying on similar sequencing efforts in other crops.
Data from: Whole genome sequencing of elite rice cultivars as a comprehensive information resource for marker assisted selection
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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.